Philippa Kaur

Pearson, P. N. & Palmer, M. R. Atmospheric carbon dioxide concentrations over the past 60 million years. Nature 406, 695–699 (2000).Article 
ADS 
CAS 

Google Scholar 
Wang, P. et al. The genome evolution and domestication of tropical fruit mango. Genome Biol. 21, 60 (2020).Article 

Google Scholar 
Yang, H. et al. Advances in the regulatory mechanisms of pollen response to heat stress in crops. Chin. Bull. Bot. 54(2), 157–167 (2019).CAS 

Google Scholar 
Liang, Q. Z. et al. Transcriptome and metabolome analyses reveal the involvement of multiple pathways in flowering intensity in mango. Front. Plant Sci. 13, 933923 (2022).Article 

Google Scholar 
Ranasinghe, C. S., Waidyarathna, K. P., Pradeep, A. P. C. & Meneripitiya, M. S. K. Approach to screen coconut varieties for high temperature tolerance by in-vitro pollen germination. COCOS. 19, 01–11 (2010).
Google Scholar 
Das, S., Krishnan, P., Nayak, M. & Ramakrishnan, B. High temperature stress effects on pollens of rice (Oryza sativa L.) genotypes. Environ. Exp. Bot. 101, 36–46 (2014).Article 

Google Scholar 
Balasubramanian, S., Sureshkumar, S., Lempe, J. & Weigel, D. Potent induction of Arabidopsis thaliana flowering by elevated growth temperature. PLoS Genet. 2(7), e106 (2006).Article 

Google Scholar 
Sakata, T., Takahashi, H., Nishiyama, I. & Higashitani, A. Effects of high temperature on the development of pollen mother cells and microspores in Barley Hordeum vulgare L.. J. Plant Res. 113(4), 395–402 (2000).Article 

Google Scholar 
Hedhly, A., Hormaza, J. I. & Herrero, M. The effect of temperature on pollen germination, pollen tube growth, and stigmatic receptivity in peach. Plant Biol. 7(5), 476–483 (2005).Article 
CAS 

Google Scholar 
Pirlak, L. The effects of temperature on pollen germination and pollen tube growth of apricot and sweet cherry. Gartenbauwissenschaft 67(2), 61–64 (2002).
Google Scholar 
Koti, S., Reddy, K. R., Reddy, V. R., Kakani, V. G. & Zhao, D. Interactive effects of carbon dioxide, temperature, and ultraviolet-B radiation on soybean (Glycine max L.) flower and pollen morphology, pollen production, germination, and tube lengths. J. Exp. Bot. 56(412), 725–736 (2004).Article 

Google Scholar 
Pham, V. T., Herrero, M. & Hormaza, J. I. Effect of temperature on pollen germination and pollen tube growth in longan (Dimocarpus longan Lour.). Sci. Hort. 197, 470–475 (2015).Article 

Google Scholar 
Meehl, T. G. A. & Tebaldi, C. More intense, more frequent, and longer lasting heat waves in the 21st century. Science 305, 994–997 (2004).Article 
ADS 
CAS 

Google Scholar 
Reddy, K. R., Hodges, H. F. & Reddy, V. R. Temperature effects on cotton fruit retention. Agron. J. 84, 26–30 (1992).Article 

Google Scholar 
Reddy, K. R., Reddy, V. R. & Hodges, H. F. Effects of temperature on early season cotton growth and development. Agron. J. 84, 229–237 (1992).Article 

Google Scholar 
Stainforth, D. et al. Uncertainty in predictions of the climate response to rising levels of greenhouse gases. Nature 433, 403–406 (2005).Article 
ADS 
CAS 

Google Scholar 
Liu, Z., Yuan, Y., Liu, S., Yu, X. & Rao, L. Screening for high temperature tolerant cotton cultivars by testing in vitro pollen germination, pollen tube growth and boll retention. J. Integr. Plant Biol. 48, 706–714 (2006).Article 

Google Scholar 
Kakani, V. G., Prasad, P. V. V., Craufurd, P. Q. & Wheeler, T. R. Response of in vitro pollen germination and pollen tube growth of groundnut (Arachis hypogaea L.) genotypes to temperature. Plant Cell Environ. 25, 1651–1661 (2002).Article 

Google Scholar 
Kakani, V. G. et al. Differences in in vitro pollen germination and pollen tube growth of cotton cultivars in response to high temperature. Ann. Bot. 96(1), 59–67 (2005).Article 
CAS 

Google Scholar 
Hebbar, K. B. et al. Differences in in vitro pollen germination and pollen tube growth of coconut (Cocos nucifera L.) genotypes in response to high temperature stress. Environ. Ex. Bot. 153, 35–44 (2018).Article 

Google Scholar 
Aloni, B., Peet, M., Pharr, M. & Karmi, L. The effect of high temperaturare and high atmospheric CO2 on carbohydrate changes in bell pepper (Capsicum annuum) pollen in relation to its germination. Physiol. Plant 112, 505–512 (2001).Article 
CAS 

Google Scholar 
Dai, Q., Shaobing, P., Chavez, A. Q. & Vergara, B. S. Intraspecific responses of 188 rice cultivars to enhanced UVB radiation. Environ. Exp. Bot. 34(4), 433–442 (1994).Article 

Google Scholar 
Hepler, P. K., Vidali, L. & Cheung, A. Y. Polarized cell growth in higher plants. Annu. Rev. Cell Dev. Biol. 17(1), 159–187 (2001).Article 
CAS 

Google Scholar 
Prado, A. M., Porterfield, D. M. & Feijo, J. A. Nitric oxide is involved in growth regulation and re-orientation of pollen tubes. Development 131(11), 2707–2714 (2004).Article 
CAS 

Google Scholar 
Potocky, M., Jones, M. A., Bezvoda, R., Smirnoff, N. & Zarsky, V. Reactive oxygen species produced by NADPH oxidase are involved in pollen tube growth. New Phytol. 174(4), 742–751 (2007).Article 
CAS 

Google Scholar 
Lassig, R., Gutermuth, T., Bey, T. D., Konrad, K. R. & Romeis, T. Pollen tube NAD (P)H oxidases act as a speed control to dampen growth rate oscillations during polarized cell growth. Plant J. 78(1), 94–106 (2014).Article 
CAS 

Google Scholar 
McInnis, S. M., Desikan, R., Hancock, J. T. & Hiscock, S. J. Production of reactive oxygen species and reactive nitrogen species by angiosperm stigmas and pollen: potential signalling crosstalk?. New Phytol. 172(2), 221–228 (2006).Article 
CAS 

Google Scholar 
Duan, Q. et al. Reactive oxygen species mediate pollen tube rupture to release sperm for fertilization in Arabidopsis. Nat. Commun. 5, 3129 (2014).Article 
ADS 

Google Scholar 
You, J. & Chan, Z. ROS regulation during abiotic stress responses in crop plants. Front Plant Sci. 6, 1092 (2015).Article 

Google Scholar 
Apel, K. & Hirt, H. Reactive oxygen species: Metabolism, oxidative stress, and signal transduction. Annu. Rev. Plant Biol. 55, 373–399 (2004).Article 
CAS 

Google Scholar 
Pandhair, V. & Sekhon, B. S. Reactive oxygen species and antioxidants in plants: An overview. J. Plant Biochem. Biot. 15(2), 71–78 (2006).Article 
CAS 

Google Scholar 
Sharma, P., Jha, A. B., Dubey, R. S. & Pessarakli, M. Reactive oxygen species, oxidative damage, and antioxidative defense mechanism in plants under stressful conditions. J. Bot. https://doi.org/10.1155/2012/217037 (2012).Article 

Google Scholar 
Luo, C. et al. Construction of a high-density genetic map based on large-scale marker development in mango using specific-locus amplified fragment sequencing (SLAF-seq). Front. Plant Sci. 7, 1310 (2016).Article 

Google Scholar 
IPCC. IPCC Fourth Assessment Report. http://www.ipcc.ch/. Accessed 15 Jan 2010 (2007)Reddy, K. R. & Kakani, V. G. Screening Capsicum species of different origins for high temperature tolerance by in vitro pollen germination and pollen tube length. Sci. Hort. 112, 130–135 (2007).Article 

Google Scholar 
Armendariz, B. H. C., Oropeza, C., Chan, J. L., Maust, B., Aguilar, C. C. C., & Saenz, L. Pollen Fertility and Female Flower Anatomy of Micropropagated Coconut Palms. 373–378 (Revista Fitotecnia Mexicana, Sociedad Mexicana de Fitogenetica, A C. Mexico, 2006)Binelli, G., Manincor, E. V. & Ottaviano, E. Temperature effects on pollen germination and pollen tube growth in maize. Genetica Agraria 39, 269–281 (1985).
Google Scholar 
Matlob, A. N. & Kelly, W. C. Effect of high temperature on pollen tube growth of snake melon and cucumber. J. Am. Soc. Hortic. Sci. 98, 296–300 (1973).Article 

Google Scholar 
Zhou, Q. F. An Empirical Study on the Evolution of Mango Production in China. 1–53 (Hainan University, 2017)He, L. et al. Grafting trial on mango varieties in hot-dry region Jinsha River. Subtropic. Agric. Res. 6(3), 21–24 (2010) (in Chinese with English abstract).
Google Scholar 
Gong, D. Y., Liu, Q. G., Zhang, Y. & Zhang, X. B. Studies on adaptability and application of mango varieties in south subtropical regions of Guizhou. Acta Agricult. Jiangxi 24(7), 28–31 (2012) (in Chinese).CAS 

Google Scholar 
Liu, Z. T. Performance and cultivation techniques of coconut mango in Panxi hot area. Trop. Agricult. Guangxi 3(110), 11–12 (2007).
Google Scholar 
Gajanayake, B., Trader, B. W., Reddy, K. R. & Harkess, R. L. Screening ornamental pepper cultivars for temperature tolerance using pollen and physiological parameters. Hortic. Sci. 46, 878–884 (2011).
Google Scholar 
Salem, M. A., Kakani, V. G., Koti, S. & Reddy, K. R. Pollen-based screening of soybean genotypes for high temperatures. Crop Sci. 47, 219–231 (2007).Article 

Google Scholar 
Young, L. W., Wilen, R. W. & Bonham-Smith, P. C. High temperature stress of Brassica napus during flowering reduces micro- and megagametophyte fertility, induces fruit abortion, and disrupts seed production. J. Exp. Bot. 55, 485–495 (2004).Article 
CAS 

Google Scholar 
Kafizadeh, N., Carapetian, J. & Kalantari, K. M. Effects of heat stress on pollen viability and pollen tube growth in pepper. Res. J. Biol. Sci. 3, 1159–1162 (2008).
Google Scholar 
Pressman, E., Peet, M. M. & Pharr, D. M. The effect of heat stress on tomato pollen characteristics is associated with changes in carbohydrate concentration in the developing anthers. Ann. Bot. 90, 613–636 (2002).Article 

Google Scholar 
Sukhvibul, N. et al. Effect of temperature on pollen germination and pollen tube growth of four cultivars of mango (Mangifera indica L.). J. Hortic. Sci. Biotechnol. 75(2), 214–222 (2000).Article 

Google Scholar 
Koubouris, G. C., Metzidakis, I. T. & Vasilakakis, M. D. Impact of temperature on olive (Olea europaea L.) pollen performance in relation to relative humidity and genotype. Environ. Exp. Bot. 67(1), 209–214 (2009).Article 

Google Scholar 
Huang, J. H. et al. Effects of low temperatures on sexual reproduction of ‘Tainong 1’ mango (Mangifera indica). Sci. Horticult. 126(2), 109–119 (2010) (in Chinese with English abstract).Article 

Google Scholar 
Çetinbaş-Gença, A., Cai, G., Vardara, F. & Ünal, M. Differential effects of low and high temperature stress on pollen germination and tube length of hazelnut (Corylus avellana L.) genotypes. Sci. Horticult. 255, 61–69 (2019).Article 

Google Scholar 
Sorkheh, K. et al. Interactive effects of temperature and genotype on almond (Prunus dulcis L.) pollen germination and tube length. Sci. Hortic. 227, 162–168 (2018).Article 

Google Scholar 
Wang, L. et al. Analysis of common errors of custom enzyme activity units and suggestions for standardized use. Chin. J. Sci. Technol. 24(5), 1009–1011 (2013).
Google Scholar 
Wang, W. et al. Combined cytological and transcriptomic analysis reveals a nitric oxide signaling pathway involved in cold-inhibited Camellia sinensis pollen tube growth. Front. Plant Sci. 7, 456 (2016).
Google Scholar 
He, J. M., Bai, X. L., Wang, R. B., Cao, B. & She, X. P. The involvement of nitric oxide in ultraviolet-B-inhibited pollen germination and tube growth of Paulownia tomentosa in vitro. Physiol. Plant 131(2), 273–282 (2007).CAS 

Google Scholar 
Gao, Y. et al. Mitochondrial dysfunction mediated by cytoplasmic acidification results in pollen tube growth cessation in Pyrus pyrifolia. Physiol. Plant 153(4), 603–615 (2015).Article 
CAS 

Google Scholar 
Hall, A. E. Breading for heat tolerance. Plant Breed. Rev. (SAS Institute) 10, 129–168 (1999) (SAS/STAT user’s guide, version 9.2. SAS Institute, 1992).Mearns, L. O., Easterling, W., Hays, C. & Marx, D. Comparison of agricultural impacts of climate change calculated from high and low resolution climate change scenarios. Part I. The uncertainty due to spatial scale. Clim. Change. 51, 131–172 (2001).Article 

Google Scholar 
SAS Institute SAS/STAT User’s Guide, Version 9.1.3. (SAS Institute Inc., 2004).Li, H. S., Sun, Q., Zhao, S. J. & Zhang, W. H. Experiment Principle and Technology of Plant Physiology and Biochemistry (Higher Education Press, 2000).
Google Scholar 
Cai, Q. S. Plant Physiology Experiment. Vol. 4(1). 182–186 (China Agricultural University Press, 2013) (in Chinese).Jia, M. X. et al. ROS-induced oxidative stress is closely related to pollen deterioration following cryopreservation. In Vitro Cell Dev. Biol. Plant 53(4), 433–439 (2017).Article 
CAS 

Google Scholar  More

Abigail Meyer from the University of Minnesota, USA, and colleagues from the USA, investigated the physiological and morphological responses to experimental warming and CO2 additions in the widespread forest lichen Evernia mesomorpha. While impacts of CO2 were largely negligible, warming and associated drying was linked to decreases in biomass, carbon assimilation and respiration rates. As well as bleaching of the lichen, indicative of death of the photobiont, the authors found evidence of shifts in internal algal communities, including increased proportions of certain algal clades under warming. While the study reveals the sensitivity of lichen algae to warming, further work is needed to reveal whether photobiont turnover may assist in lichen acclimation and recovery. More

Auffret, M. D. et al. The role of microbial community composition in controlling soil respiration responses to temperature. PLoS ONE 11, e0165448 (2016).Article 
PubMed 
PubMed Central 

Google Scholar 
Yao, Y. et al. A data-driven global soil heterotrophic respiration dataset and the drivers of its inter‐annual variability. Glob. Biogeochem. Cycle 35, e2020GB006918 (2021).Article 
CAS 

Google Scholar 
Davidson, E. A., Janssens, I. A. & Luo, Y. On the variability of respiration in terrestrial ecosystems: moving beyond Q10. Glob. Change Biol. 12, 154–164 (2006).Article 

Google Scholar 
Wang, Q. et al. Soil microbial respiration rate and temperature sensitivity along a north–south forest transect in eastern China: patterns and influencing factors. J. Geophys. Res. Biogeosci. 121, 399–410 (2016).Article 

Google Scholar 
Sihi, D. et al. Merging a mechanistic enzymatic model of soil heterotrophic respiration into an ecosystem model in two AmeriFlux sites of northeastern USA. Agric. Meteorol. 252, 155–166 (2018).Article 

Google Scholar 
Shao, P., Zeng, X., Moore, D. J. P. & Zeng, X. Soil microbial respiration from observations and Earth system models. Environ. Res. Lett. 8, 034034 (2013).Article 
CAS 

Google Scholar 
Davidson, E. A., Samanta, S., Caramori, S. S. & Savage, K. The dual Arrhenius and Michaelis–Menten kinetics model for decomposition of soil organic matter at hourly to seasonal time scales. Glob. Change Biol. 18, 371–384 (2012).Article 

Google Scholar 
Oechel, W. C. et al. Acclimation of ecosystem CO2 exchange in the Alaskan Arctic in response to decadal climate warming. Nature 406, 978–981 (2000).Article 
CAS 
PubMed 

Google Scholar 
Alster, C. J., von Fischer, J. C., Allison, S. D. & Treseder, K. K. Embracing a new paradigm for temperature sensitivity of soil microbes. Glob. Change Biol. 26, 3221–3229 (2020).Article 

Google Scholar 
Nie, M. et al. Positive climate feedbacks of soil microbial communities in a semi-arid grassland. Ecol. Lett. 16, 234–241 (2013).Article 
PubMed 

Google Scholar 
Ji, F., Wu, Z., Huang, J. & Chassignet, E. P. Evolution of land surface air temperature trend. Nat. Clim. Change 4, 462–466 (2014).Article 

Google Scholar 
Huntingford, C., Jones, P. D., Livina, V. N., Lenton, T. M. & Cox, P. M. No increase in global temperature variability despite changing regional patterns. Nature 500, 327–330 (2013).Article 
CAS 
PubMed 

Google Scholar 
Hansen, J., Sato, M. & Ruedy, R. Perception of climate change. Proc. Natl Acad. Sci. USA 109, E2415–E2423 (2012).Article 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Byrne, M. P. Amplified warming of extreme temperatures over tropical land. Nat. Geosci. 14, 837–841 (2021).Article 
CAS 

Google Scholar 
IPCC Climate Change 2021: The Physical Science Basis (eds Masson-Delmotte, V. et al.) (Cambridge Univ. Press, 2021).Chan, W. P. et al. Seasonal and daily climate variation have opposite effects on species elevational range size. Science 351, 1437–1439 (2016).Article 
CAS 
PubMed 

Google Scholar 
Biederbeck, V. O. & Campbell, C. A. Soil microbial activity as influenced by temperature trends and fluctuations. Can. J. Soil Sci. 53, 363–375 (1973).Article 

Google Scholar 
Karhu, K. et al. Temperature sensitivity of soil respiration rates enhanced by microbial community response. Nature 513, 81–84 (2014).Article 
CAS 
PubMed 

Google Scholar 
Chen, H., Zhu, T., Li, B., Fang, C. & Nie, M. The thermal response of soil microbial methanogenesis decreases in magnitude with changing temperature. Nat. Commun. 11, 5733 (2020).Article 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Allison, S. D., Wallenstein, M. D. & Bradford, M. A. Soil-carbon response to warming dependent on microbial physiology. Nat. Geosci. 3, 336–340 (2010).Article 
CAS 

Google Scholar 
Nottingham, A. T. et al. Microbial responses to warming enhance soil carbon loss following translocation across a tropical forest elevation gradient. Ecol. Lett. 22, 1889–1899 (2019).Article 
PubMed 

Google Scholar 
Alster, C. J., Robinson, J. M., Arcus, V. L. & Schipper, L. A. Assessing thermal acclimation of soil microbial respiration using macromolecular rate theory. Biogeochemistry 158, 131–141 (2022).Article 
CAS 

Google Scholar 
Moinet, G. Y. K. et al. Soil microbial sensitivity to temperature remains unchanged despite community compositional shifts along geothermal gradients. Glob. Change Biol. 27, 6217–6231 (2021).Article 

Google Scholar 
Feng, J. et al. Soil microbial trait-based strategies drive metabolic efficiency along an altitude gradient. ISME Commun. 1, 71 (2021).Article 

Google Scholar 
Li, J. et al. Key microorganisms mediate soil carbon-climate feedbacks in forest ecosystems. Sci. Bull. 66, 2036–2044 (2021).Article 
CAS 

Google Scholar 
Trivedi, P. et al. Microbial regulation of the soil carbon cycle: evidence from gene–enzyme relationships. ISME J. 10, 2593–2604 (2016).Article 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Zhu, B. & Cheng, W. Constant and diurnally-varying temperature regimes lead to different temperature sensitivities of soil organic carbon decomposition. Soil Biol. Biochem. 43, 866–869 (2011).Article 
CAS 

Google Scholar 
Bradford, M. A. et al. Thermal adaptation of soil microbial respiration to elevated temperature. Ecol. Lett. 11, 1316–1327 (2008).Article 
PubMed 

Google Scholar 
Hartley, I. P., Hopkins, D. W., Garnett, M. H., Sommerkorn, M. & Wookey, P. A. Soil microbial respiration in Arctic soil does not acclimate to temperature. Ecol. Lett. 11, 1092–1100 (2008).Article 
PubMed 

Google Scholar 
Bradford, M. A. et al. Cross-biome patterns in soil microbial respiration predictable from evolutionary theory on thermal adaptation. Nat. Ecol. Evol. 3, 223–231 (2019).Article 
PubMed 

Google Scholar 
Tian, W. et al. Thermal adaptation occurs in the respiration and growth of widely distributed bacteria. Glob. Change Biol. 28, 2820–2829 (2022).Article 
CAS 

Google Scholar 
Bradford, M. A., Watts, B. W. & Davies, C. A. Thermal adaptation of heterotrophic soil respiration in laboratory microcosms. Glob. Change Biol. 16, 1576–1588 (2010).Article 

Google Scholar 
Walker, T. W. N. et al. Microbial temperature sensitivity and biomass change explain soil carbon loss with warming. Nat. Clim. Change 8, 885–889 (2018).Article 
CAS 

Google Scholar 
Chen, H. et al. Microbial respiratory thermal adaptation is regulated by r-/K-strategy dominance. Ecol. Lett. 25, 2489–2499 (2022).Article 
PubMed 

Google Scholar 
Wang, C. et al. The temperature sensitivity of soil: microbial biodiversity, growth, and carbon mineralization. ISME J. 15, 2738–2747 (2021).Article 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Ramadhin, C., Yi, C. & Hendrey, G. Temperature variance portends and indicates the extent of abrupt climate shifts. IOP SciNotes 2, 014002 (2021).Article 

Google Scholar 
Sun, Y. Q. & Ge, Y. Temporal changes in the function of bacterial assemblages associated with decomposing earthworms. Front. Microbiol. 12, 682224 (2021).Article 
PubMed 
PubMed Central 

Google Scholar 
Shi, Z., Xu, J., Li, X., Li, R. & Li, Q. Links of extracellular enzyme activities, microbial metabolism, and community composition in the river-impacted coastal waters. J. Geophys. Res. Biogeosci. 124, 3507–3520 (2019).Article 

Google Scholar 
Razanamalala, K. et al. Soil microbial diversity drives the priming effect along climate gradients: a case study in Madagascar. ISME J. 12, 451–462 (2017).Article 
PubMed 
PubMed Central 

Google Scholar 
Xu, M. et al. High microbial diversity stabilizes the responses of soil organic carbon decomposition to warming in the subsoil on the Tibetan Plateau. Glob. Change Biol. 27, 2061–2075 (2021).Article 
CAS 

Google Scholar 
Clemmensen, K. E. et al. Roots and associated fungi drive long-term carbon sequestration in boreal forest. Science 339, 1615–1618 (2013).Article 
CAS 
PubMed 

Google Scholar 
Qiao, N. et al. Labile carbon retention compensates for CO2 released by priming in forest soils. Glob. Change Biol. 20, 1943–1954 (2014).Article 

Google Scholar 
Ning, Q. et al. Carbon limitation overrides acidification in mediating soil microbial activity to nitrogen enrichment in a temperate grassland. Glob. Change Biol. 27, 5976–5988 (2021).Article 
CAS 

Google Scholar 
Wan, S. & Luo, Y. Substrate regulation of soil respiration in a tallgrass prairie: results of a clipping and shading experiment. Glob. Biogeochem. Cycle 17, 1054 (2003).Article 

Google Scholar 
Gillabel, J., Cebrian-Lopez, B., Six, J. & Merckx, R. Experimental evidence for the attenuating effect of SOM protection on temperature sensitivity of SOM decomposition. Glob. Change Biol. 16, 2789–2798 (2010).Article 

Google Scholar 
Xia, J. et al. Terrestrial carbon cycle affected by non-uniform climate warming. Nat. Geosci. 7, 173–180 (2014).Article 
CAS 

Google Scholar 
Balesdent, J. et al. Atmosphere–soil carbon transfer as a function of soil depth. Nature 559, 599–602 (2018).Article 
CAS 
PubMed 

Google Scholar 
Howard, D. M. & Howard, P. J. A. Relationships between CO2 evolution, moisture-content and temperature for a range of soil types. Soil Biol. Biochem. 25, 1537–1546 (1993).Article 

Google Scholar 
Hoyle, F. C., Murphy, D. V. & Brookes, P. C. Microbial response to the addition of glucose in low-fertility soils. Biol. Fertil. Soils 44, 571–579 (2008).Article 
CAS 

Google Scholar 
Mau, R. L. et al. Linking soil bacterial biodiversity and soil carbon stability. ISME J. 9, 1477–1480 (2015).Article 
CAS 
PubMed 

Google Scholar 
Tucker, C. L., Bell, J., Pendall, E. & Ogle, K. Does declining carbon-use efficiency explain thermal acclimation of soil respiration with warming? Glob. Change Biol. 19, 252–263 (2013).Article 

Google Scholar 
Billings, S. A. & Ballantyne, F. T. How interactions between microbial resource demands, soil organic matter stoichiometry, and substrate reactivity determine the direction and magnitude of soil respiratory responses to warming. Glob. Change Biol. 19, 90–102 (2013).Article 

Google Scholar 
Li, J. et al. Biogeographic variation in temperature sensitivity of decomposition in forest soils. Glob. Change Biol. 26, 1873–1885 (2020).Article 

Google Scholar 
Min, K. et al. Temperature sensitivity of biomass-specific microbial exo-enzyme activities and CO2 efflux is resistant to change across short- and long-term timescales. Glob. Change Biol. 5, 1793–1807 (2019).Article 

Google Scholar 
Dacal, M., Bradford, M. A., Plaza, C., Maestre, F. T. & Garcia-Palacios, P. Soil microbial respiration adapts to ambient temperature in global drylands. Nat. Ecol. Evol. 3, 232–238 (2019).Article 
PubMed 
PubMed Central 

Google Scholar 
Field-Fote, E. E. Mediators and moderators, confounders and covariates: exploring the variables that illuminate or obscure the “active ingredients” in neurorehabilitation. J. Neurol. Phys. Ther. 43, 83–84 (2019).Article 
PubMed 

Google Scholar 
Anderson, T. H. & Domsch, K. H. Soil microbial biomass: the eco-physiological approach. Soil Biol. Biochem. 12, 2039–2043 (2010).Article 

Google Scholar 
Vance, E. D., Brookes, P. C. & Jenkinson, D. S. Microbial biomass measurements in forest soils—the use of the chloroform fumigation incubation method in strongly acid soils. Soil Biol. Biochem. 19, 697–702 (1987).Article 
CAS 

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

Google Scholar 
Martin, M. Cutadapt removes adapter sequences from high-throughput sequencing reads. EMBnet J. 17, 10–12 (2011).Article 

Google Scholar 
Edgar, R. C., Haas, B. J., Clemente, J. C., Quince, C. & Knight, R. UCHIME improves sensitivity and speed of chimera detection. Bioinformatics 27, 2194–2200 (2011).Article 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Edgar, R. C. UPARSE: highly accurate OTU sequences from microbial amplicon reads. Nat. Methods 10, 996–998 (2013).Article 
CAS 
PubMed 

Google Scholar 
Bolyen, E. et al. Reproducible, interactive, scalable and extensible microbiome data science using QIIME 2. Nat. Biotechnol. 37, 852–857 (2019).Article 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Callahan, B. J. et al. DADA2: high-resolution sample inference from Illumina amplicon data. Nat. Methods 13, 581–583 (2016).Article 
CAS 
PubMed 
PubMed Central 

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 (2013).Article 
CAS 
PubMed 

Google Scholar 
Koljalg, U. et al. UNITE: a database providing web-based methods for the molecular identification of ectomycorrhizal fungi. N. Phytol. 166, 1063–1068 (2005).Article 
CAS 

Google Scholar 
German, D. P. et al. Optimization of hydrolytic and oxidative enzyme methods for ecosystem studies. Soil Biol. Biochem. 43, 1387–1397 (2011).Article 
CAS 

Google Scholar 
Mazerolle, M. Improving data analysis in herpetology: using Akaike’s information criterion (AIC) to assess the strength of biological hypotheses. Amphib. Reptil. 2, 169–180 (2006).Article 

Google Scholar 
Moinet, G. Y. K. et al. Temperature sensitivity of decomposition decreases with increasing soil organic matter stability. Sci. Total Environ. 704, 135460 (2020).Article 
CAS 
PubMed 

Google Scholar 
Moinet, G. Y. K. et al. The temperature sensitivity of soil organic matter decomposition is constrained by microbial access to substrates. Soil Biol. Biochem. 116, 333–339 (2018).Article 
CAS 

Google Scholar 
Dixon, P. VEGAN, a package of R functions for community ecology. J. Veg. Sci. 14, 927–930 (2003).Article 

Google Scholar  More

Strain assessmentWe did not detect any C-strain individual following analysis of 138 fully assembled mitochondrial DNA genomes (mitogenomes) from Australian samples. Our results, particularly that from Northern Territory, are not dissimilar to the finding of Piggott et al.56 who detected only two (i.e., 4.2%) C-strain mtCOI haplotype individuals from a much larger (i.e., n = 48) Northern Territory sample size. Proportions of C-strain to R-strain also varied significantly across the different SEA populations (Table S1) in contrast to the patterns observed in China, India, and African nations (e.g.,22,33,34,39,57). All Australian populations analysed for their corn or rice mitochondrial haplotypes via mitogenome assemblies of whole genome sequencing data therefore contrasted with the invasive populations from SEA where in some countries (e.g., Myanmar, Vietnam) FAW with the C-strain mtCOI haplotypes made up approximately 50% of the populations examined (see Table S1 for C- and R-strains mitogenome proportions, see also Fig. 1 ‘C-strain’ and ‘R-strain’ Maximum Likelihood cladograms).Figure 1Maximum Likelihood cladograms of unique Spodoptera frugiperda C-strain and R-strain partial mitochondrial genomes based on concatenation of the 13 PCGs (11,393 bp) using IQ-Tree with 1000 UFBoot replications. Individuals in clades I, II, III, and IV (C-strain) and in Clades I, II, V (R-strain) that are in the same colour scheme (i.e., green, orange, blue, or pinks) shared 100% nucleotide identity. Mitogenome haplotypes from native individuals for both C- and R-strains are in khaki green colour. Red and dark grey dots at branch nodes represent bootstrap values of 87–100% and 74–86%, respectively. Bootstrap values  Hetexp; see60) could likewise indicate recent mixing of distinct populations from SEA that suggest multiple introductions (e.g.,33,39 cf.46,47,61,62; i.e., due to a recent bottleneck from a recent western Africa founder event).Table 1 Population genetic differentiation via pairwise FST estimates between Spodoptera frugiperda populations from the invasive ranges of Africa (Uganda, Malawi, Benin), South Asia (India), East Asia (China (Cangyuan (CY), Xinping (XP), YuanJiang (YJ)), South Korea), Southeast Asia (Malaysia (Johor, Kedah, Penang States), Laos, Vietnam, Myanmar), and Pacific/Australia (Papua New Guinea (PNG), Australia—Kununurra (Western Australia, WA), Northern Territory (NT), Strathmore, Walkamin, Burdekin, Mackay (Queensland, Qld), Wee Waa (New South Wales, NSW).Full size tableThe observed heterozygosity excess detected in all invasive range populations could be further explained as due to population sub-structure and isolation breaking through periodic migration. Significant numbers of loci (ca. 30%) were also shown to not be in Hardy–Weinberg equilibrium (HWE) especially for the Malaysian (i.e., Kedah), but also Australian (i.e., Wee Waa, NT, Kununurra), Chinese (e.g., XP), South Korean, and Malawian populations. Taken as a whole, genetic diversity results from this study therefore suggested that the invasive Asian (i.e., SA, SEA, EA) FAW populations exhibited signatures of recent mixing of previously separated populations. Simulated patterns of moth migration of various invasive FAW populations such as between Myanmar and China (e.g.,41,42,55) and to Australia54 are incompatible with the population genomic data, which suggests these were likely discrete and non-panmictic FAW populations with the most probable explanation being due to multiple origins of founding populations.Genetic differentiation analysisEstimates of pairwise genetic differentiation (FST) between populations varied significantly (Table 1) and extended to between populations within a country (e.g., Mackay vs. rest of Australia; Kedah vs. rest of Malaysia). Of interest are the pairwise estimates between different Australian FAW populations from Kununurra (Western Australia), Northern Territory, Queensland (Strathmore, Walkamin, Burdekin, Mackay) and New South Wales (Wee Waa) that represented the most recently reported invasive populations in this study, and predominantly showed significant differentiation amongst themselves (with the exception of the two Queensland populations of Mackay and partially for Walkamin) and with other SEA/SA/EA countries. The majority of non-significant population genetic differentiation estimates were in SEA where the presence of FAW was reported earlier, i.e., since 2018 (e.g.,63,64 or as early as 200865,66; see also33), while across Asia (e.g., China) since 2016 but also potentially pre-2014 (16,67; see also33).Interestingly, significant genetic differentiation was observed between populations from Yunnan province in China and populations from Myanmar, Laos, and Vietnam. Penang and Johor (Malaysia) populations were not significantly differentiated from other SE Asian populations, nor with Ugandan and Malawian populations from east Africa. Individuals from Benin and Mackay (Queensland, Australia) showed non-significant genetic differentiation with all populations except with Kedah, and for Mackay also surprisingly with the Wee Waa population from New South Wales. The South Korean population exhibited significant genetic differentiation with SE Asian population except with Mackay, India and the Yuanjiang (YJ) population in Yunnan Province. Finally, the Kedah population, being one of the earliest collected samples from Malaysia and having been maintained as a laboratory population, showed strong differentiation with all populations (and lowest nucleotide diversity, π = 0.237; Table 2) further supporting unique, non-African, introduction events in SEA. Strong genetic differentiation suggested there was limited gene flow to breakdown sub-structure between populations, and the FST estimates from these invasive populations therefore failed to support a west-to-east spread pathway for the FAW. This observation instead suggested the widespread presence of genetically distinct FAW populations, likely due to independent introductions and therefore also highlighting likely biosecurity weaknesses especially in East Asia (e.g., China, South Korea) and SEA (e.g., Malaysia).Table 2 Population statistics for Spodoptera frugiperda populations from Southeast Asia (i.e., Malaysia (MYS; Johor, Kedah, Penang), Laos, Vietnam, Myanmar), East Asia (i.e., South Korea), and Pacific/Australia (i.e., Papua New Guinea (PNG), Australia).Full size tableThe genetic diversity of Australian populations identified surprisingly complex sub-structure patterns given the short time frame of population detections across different northern Australian regions. Significant genetic differentiation between, e.g., Kununurra (WA), Northern Territory (NT), Queensland (e.g., Strathmore, Burdekin), and Wee Waa (NSW) populations suggests these populations likely derived from separate establishment events. The WA Kununurra population was not significantly differentiated from the Johor State (Malaysia), India and the Cangyuan (CY) China populations, suggesting a potential south-eastern route from SA/SEA into north-western Australia. Contrasting this, Walkamin and Mackay populations showed non-significant genetic differentiation with the Madang (PNG) population, suggesting a potential second pathway for SEA individuals to arrive at the north-eastern region of Australia. Significant genetic differentiation between WA, NT, and Qld populations suggested that at least during the early stage of pest establishment in northern Australia, there was limited gene flow to homogenise the unique genetic background carried by these distinct individuals, some of which exhibited also distinct insecticide resistance profiles48,49.PCAWe selected specific populations to compare using Principal Component Analysis (PCA) as examples to support evidence of independent introductions, as seen from Fig. 3a between China (CY, YJ, XP) populations vs. Myanmar, in Fig. 3b (within Malaysian populations between those collected from Penang and Johor States vs. Kedah State), in Fig. 3c for between China and East Africa (e.g., Uganda, Malawi), and where Benin and India individuals that grouped with either China or east Africa; and in Fig. 3d between China, Malaysia (Kedah State), and Australia (NT, NSW)). Genetic variability between Australian populations (e.g., Strathmore (QLD) vs. NT and NSW) was also evident (Fig. 3d).Figure 3Principal component analysis (PCA) showing variability between selected FAW populations from their invasive ranges. (a) China and Myanmar; (b) Kedah and Johor/Penang populations from Malaysia, (c) China and east African (Uganda/Malawi) populations, (d) Australia (Strathmore, Qld/Northern Territory + New South Wales), China, and Malaysia (Kedah) populations, (e) Australia (Strathmore, Qld) and PNG (Madang Province) populations, (f) Lao PDR/Vietnam and South Korea populations, (g) China and SE Asian (Lao PDR/Vietnam/Myanmar/Philippines/Malaysia) and Pacific/Australia (PNG) populations, and (h) Australia, China and Malaysia (Kedah) populations. Note the overall population genomic variability between countries (e.g., a, c–g) and within countries (e.g., Malaysia (b), Australia (d)). Populations with similar genomic variability are also evident, e.g., for Strathmore (e) and South Korea (f); and for Madang (e) and Lao PDR/Vietnam (f), further supporting potential different population origins of various FAW populations across the current invasive regions. The Southeast Asian and Chinese populations are overall different (g), Australia’s FAW populations showed similarity with both Southeast Asia and China (g, h).Full size imagePCA also showed that differences existed between FAW populations from the Madang Province in PNG and with the Strathmore population from Qld (Fig. 3e). The SEA FAW populations from Lao PDR/Vietnam also exhibited diversity from the South Korean population (Fig. 3f), with the South Korean and Strathmore populations largely exhibiting similar diversity patterns, while the Madang population shared similarity with Laos and Vietnam populations. Plotting all SEA populations against China clearly showed that populations from SEA were distinct from the Chinese FAW populations (Fig. 3g), while in Australia, individuals from various populations shared similarity with both Chinese and SEA FAW. Despite the connectedness of the landscape between SEA and China, SEA largely appeared to have their own FAW populations, with FAW in SEA and in China differing in their genome compositions overall as shown via PCA.PCA further enabled visualisation of genetic diversity amongst Australia FAW populations, suggesting that arrival and establishment of FAW likely involved separate introduction events that followed closely after each other and over a short timeframe. While it had been anticipated that the southward spread of FAW from SEA would necessarily lead to Australia FAW and PNG FAW to share similar genetic backgrounds, the Madang Province FAW population appeared to be different from the Strathmore (Qld) population, with the Madang population being more similar to Lao PDR/Vietnam populations, and the Strathmore population more similar to FAW from South Korea.DivMigrate analysisDirectionality of gene flow between African, South Asia (Indian), East Asia (China) and SE Asian populations were predominantly from China to east African and SE Asian populations (e.g., Figs. 4a, b, S-1; see also Table 3), while movements of FAW in Laos and Vietnam (i.e., the Indochina region) were predominantly with other SEA countries (e.g., with Myanmar and East Africa; Figs. 4c, d, S-2; see also Table 3) but with no directional movements to the three Yunnan populations (CY, XP, YJ). Migration directionality with other SE Asian populations (e.g., Johor (JB; Fig. S-3) and Penang (PN, Fig. S-4)) showed that these two populations (but especially the Johor population) were predominantly source populations for Uganda, Malawi, Philippines, Vietnam, and PNG (Fig. S-3). Bidirectional migration between Myanmar and Laos PDR populations were also detected with the Johor population from Malaysia (Fig. S-3). When India was selected as the source population, bidirectional migration events were detected with Myanmar and with the Cangyuan (CY) populations (Fig. S-5) while unidirectional migration events from India to Uganda and Malawi and to Laos were detected, and the China Yuanjian (YJ) population showed unidirectional migration to India. Unidirectional migration events from CY and YJ populations to the PNG Madang population were detected, while bidirectional migration events between PNG and Myanmar, Laos PDR, Philippines, Vietnam, and with Uganda and Malawi were also detected (Fig. S-6). No migration events were detected between the West African Benin population and with the South Korean population.Figure 4Source populations are CY (a) and XP (b). (c, d) DivMigrate analyses with edge weight setting at 0.453 showing unidirectional (yellow arrow lines) and bidirectional (blue arrow lines) migration between countries in Africa and South Asia/East Asia/SE Asia. Migration rates between populations are as provided in Table 3. (c) Vietnam (VNM) as the source population identified an incidence of unidirectional migration from Malaysia (MYS) Johor state (JB) to Vietnam, while bidirectional migration events were detected from Vietnam to other SE Asian (e.g., Philippines (PHL), Lao PDR (Lao), Myanmar (MMR)), to Pacific/Australia (i.e., Papua New Guinea (PNG)), as well as to east Africa (Uganda (UGA), Malawi (MWI)). (d) Lao PDR (LAO) as source population identified bidirectional migration events between various SEA populations and east African populations, while unidirectional migration events were identified from India (IND) and China (CHN) Yunnan populations (CY, YJ) to Laos PDR. No migration events were evident from SE Asian populations to China.(a, b) DivMigrate analyses with edge weight setting at 0.453 showing unidirectional (yellow arrow lines) and bidirectional (blue arrow lines) gene flow between countries in Africa and South Asia/East Asia/SE Asia. Significant migration rates (at alpha = 0.5) are in red and as provided in Table 3. Incidences of unidirectional migration were predominantly detected from China (CHN) Yunnan populations (CY, XP) to SE Asian populations (e.g., Myanmar (MMR), Laos PDR (Lao), Philippines (PHL)) and to east African populations (e.g., Uganda (UGA), Malawi (MWI)) (a, b).Full size imageTable 3 DivMigrate matrix showing effective migration rates calculated using GST from source to target invasive populations.Full size tableAdmixture analysisAdmixture analyses involving all Australian, Southeast Asian and South Korean populations from this study; and native populations from the Americas and Caribbean Islands, and invasive populations from Africa (Benin, Uganda, Malawi), India, and China33, provided an overall complex picture of population structure that reflected the species’ likely introduction histories across its invasive ranges.Admixture analysis that excluded New World, African and Indian populations identified four genetic clusters (i.e., K = 4) to best describe these invasive populations from SEA, and EA (i.e., China, South Korea), and Pacific/Australia (Fig. 5a). At K = 4, Australian populations from NT and NSW, YJ population from China, South Korean, and Malaysia’s Kedah population, each showed unique admixture patterns (i.e., some individuals from NT and NSW populations lacked cluster 3; most of YJ (but also some CY and XP) individuals lacked clusters 1 and 2; South Korean (e.g., MF individuals) lacked cluster 2; Malaysia’s Kedah population lacked evidence of admixture (i.e., reflecting its laboratory culture history) and was made up predominantly by individuals that belonged to cluster 4. Populations from China also differed from most populations from SEA due to the overall absence of genetic cluster 4. Taken as a whole, establishment of the FAW populations in China, Malaysia, vs. other SE Asian populations, and between Australian populations (e.g., NT/NSW cf. WA/Qld), likely involved individuals from diverse genetic background (i.e., multiple introductions). At K = 4, the majority of Australian populations appeared to contain genetic clusters similar to China (i.e., cluster 3) and to SEA (i.e., cluster 2).Figure 5Admixture and corresponding CV plots for FAW populations from: (a) Australia, China, South Korea, Lao PDR, Myanmar, Malaysia, Philippines, PNG, and Vietnam, and (b) Benin, China, India, South Korea, Lao PDR, Myanmar, Malaysia, Philippines, PNG, Tanzania, and Vietnam. Optimal ancestral genetic clusters are K = 4 for both admixture plots. Boxed individuals have unique admixture patterns at K = 4 when compared with other populations. China FAW lacked Cluster 2 (navy blue colour; present in almost all SEA and Australian FAW), while in NSW and NT some individuals lacked cluster 3. South Korea ‘MF’ population generally lacked cluster 2, while Kedah (Malaysia) showed distinct (cluster 4) pattern for all individuals. The overall same observations are evident in the admixture plot in (b), with African FAW generally exhibiting admixture patterns similar to SEA populations than to Chinese FAW. With the exception of Kedah (Malaysia) and some Chinese FAW individuals, all FAW in the invasive range showed evidence of genomic admixture (i.e., hybrid signature). The figures were generated using the POPHELPER program  and further manipulated in Microsoft PowerPoint for Mac v16.54.Full size imageOverall admixture patterns at best K = 4 in China and SEA remained unchanged when analysed together with African and Indian individuals (Fig. 5b; excluded Australia). Benin individuals were either similar to China or to SEA, while eastern African populations (e.g., Uganda, Malawi) were similar to Southeast Asian populations from e.g., Vietnam, Laos, and is in agreement with the phylogenetic inference (Fig. 3) that identified these African individuals as having loci that were derived from Southeast Asian populations.Genome-wide SNP loci demonstrated that invasive FAW populations from SEA and Australia exhibited admixed genomic signatures similar to that observed in other invasive populations33,34. While the current invasive populations in Africa and Asia likely arrived already as hybrids as suggested by Yainna et al.68, the Malaysia Kedah State population was potentially established by offspring of a non-admixed female. Distinct admixture patterns in Malaysian FAW populations between Kedah and Johor/Penang states therefore suggested that establishment of these populations was likely as separate introduction events. As reported also in Tay et al.33, the Chinese YJ population appeared to have admixed signature that differed from XP and CY populations, and suggested that the YJ population could have a different introduction history than the XP and CY populations. Similar multiple genetic signatures based on lesser nuclear markers by Jiang et al.39 also supported likely multiple introductions of China Yunnan populations. More

Peaucelle, M. et al. Spatial variance of spring phenology in temperate deciduous forests is constrained by background climatic conditions. Nat. Commun. 10, 5388 (2019).Article 

Google Scholar 
Hopkins, A. D. The bioclimatic law. Mon. Weather Rev. 48, 355–355 (1920).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 
Ge, Q., Wang, H., Rutishauser, T. & Dai, J. Phenological response to climate change in China: a meta-analysis. Glob. Change Biol. 21, 265–274 (2015).Article 

Google Scholar 
Templ, B. et al. Pan European Phenological database (PEP725): a single point of access for European data. Int. J. Biometeorol. 62, 1109–1113 (2018).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 
Morisette, J. T. et al. Tracking the rhythm of the seasons in the face of global change: phenological research in the 21st century. Front. Ecol. Environ. 7, 253–260 (2009).Article 

Google Scholar 
Flynn, D. F. B. & Wolkovich, E. M. Temperature and photoperiod drive spring phenology across all species in a temperate forest community. New Phytol. 219, 1353–1362 (2018).Article 
CAS 

Google Scholar 
Peñuelas, J., Rutishauser, T. & Filella, I. Phenology feedbacks on climate change. Science 324, 887–888 (2009).Article 

Google Scholar 
Körner, C. & Basler, D. Plant science. Phenol. Glob. Warm. Sci. 327, 1461–1462 (2010).
Google Scholar 
Delpierre, N. et al. Temperate and boreal forest tree phenology: from organ-scale processes to terrestrial ecosystem models. Ann. For. Sci. 73, 5–25 (2016).Article 

Google Scholar 
Klosterman, S. T. et al. Evaluating remote sensing of deciduous forest phenology at multiple spatial scales using PhenoCam imagery. Biogeosciences 11, 4305–4320 (2014).Article 

Google Scholar 
Hufkens, K. et al. Linking near-surface and satellite remote sensing measurements of deciduous broadleaf forest phenology. Remote Sens. Environ. 117, 307–321 (2012).Article 

Google Scholar 
Garrity, S. R. et al. A comparison of multiple phenology data sources for estimating seasonal transitions in deciduous forest carbon exchange. Agric. For. Meteorol. 151, 1741–1752 (2011).Article 

Google Scholar 
Fracheboud, Y. et al. The control of autumn senescence in European aspen. Plant Physiol. 149, 1982–1991 (2009).Article 
CAS 

Google Scholar 
Mariën, B. et al. Does drought advance the onset of autumn leaf senescence in temperate deciduous forest trees? Biogeosciences 18, 3309–3330 (2021).Article 

Google Scholar 
Fu, Y. H. et al. Larger temperature response of autumn leaf senescence than spring leaf-out phenology. Glob. Change Biol. 24, 2159–2168 (2018).Article 

Google Scholar 
Menzel, A., Sparks, T. H., Estrella, N. & Roy, D. B. Altered geographic and temporal variability in phenology in response to climate change. Glob. Ecol. Biogeogr. 15, 498–504 (2006).Article 

Google Scholar 
Gordo, O. & Sanz, J. J. Long-term temporal changes of plant phenology in the Western Mediterranean. Glob. Change Biol. 15, 1930–1948 (2009).Article 

Google Scholar 
Meier, M., Vitasse, Y., Bugmann, H. & Bigler, C. Phenological shifts induced by climate change amplify drought for broad-leaved trees at low elevations in Switzerland. Agric. For. Meteorol. 307, 108485 (2021).Basler, D. Evaluating phenological models for the prediction of leaf-out dates in six temperate tree species across central Europe. Agric. For. Meteorol. 217, 10–21 (2016).Article 

Google Scholar 
Keenan, T. F. et al. Terrestrial biosphere model performance for inter-annual variability of land–atmosphere CO2 exchange. Glob. Change Biol. 18, 1971–1987 (2012).Article 

Google Scholar 
Liu, G., Chen, X., Fu, Y. & Delpierre, N. Modelling leaf coloration dates over temperate China by considering effects of leafy season climate. Ecol. Modell. 394, 34–43 (2019).Article 

Google Scholar 
Keenan, T. F. & Richardson, A. D. The timing of autumn senescence is affected by the timing of spring phenology: implications for predictive models. Glob. Change Biol. 21, 2634–2641 (2015).Article 

Google Scholar 
Wu, C., Hou, X., Peng, D., Gonsamo, A. & Xu, S. Land surface phenology of China’s temperate ecosystems over 1999–2013: spatial–temporal patterns, interaction effects, covariation with climate and implications for productivity. Agric. For. Meteorol. 216, 177–187 (2016).Article 

Google Scholar 
Fu, Y. S. H. et al. Variation in leaf flushing date influences autumnal senescence and next year’s flushing date in two temperate tree species. Proc. Natl Acad. Sci. USA 111, 7355–7360 (2014).Article 
CAS 

Google Scholar 
Zani, D., Crowther, T. W., Mo, L., Renner, S. S. & Zohner, C. M. Increased growing-season productivity drives earlier autumn leaf senescence in temperate trees. Science 370, 1066–1071 (2020).Article 
CAS 

Google Scholar 
Paul, M. J. & Foyer, C. H. Sink regulation of photosynthesis. J. Exp. Bot. 52, 1383–1400 (2001).Article 
CAS 

Google Scholar 
Herold, A. Regulation of photosynthesis by sink activity—the missing link. New Phytol. 86, 131–144 (1980).Article 
CAS 

Google Scholar 
Keenan, T. F. et al. Recent pause in the growth rate of atmospheric CO2 due to enhanced terrestrial carbon uptake. Nat. Commun. 7, 13428 (2016).Campbell, J. E. et al. Large historical growth in global terrestrial gross primary production. Nature 544, 84–87 (2017).Article 
CAS 

Google Scholar 
Schimel, D., Stephens, B. B. & Fisher, J. B. Effect of increasing CO2 on the terrestrial carbon cycle. Proc. Natl Acad. Sci.USA 112, 436–441 (2015).Article 
CAS 

Google Scholar 
Walker, A. P. et al. Integrating the evidence for a terrestrial carbon sink caused by increasing atmospheric CO. New Phytol. 229, 2413–2445 (2021).Article 
CAS 

Google Scholar 
Liu, Q. et al. Modeling leaf senescence of deciduous tree species in Europe. Glob. Change Biol. 26, 4104–4118 (2020).Article 

Google Scholar 
Friedl, M., Gray, J. & Sulla-Menashe, D. MCD12Q2 MODIS/Terra+Aqua Land Cover Dynamics Yearly L3 Global 500m SIN Grid V006 (NASA, 2019).Zhang, X. et al. Monitoring vegetation phenology using MODIS. Remote Sens. Environ. 84, 471–475 (2003).Article 

Google Scholar 
Stocker, B. D. et al. P-model v1.0: an optimality-based light use efficiency model for simulating ecosystem gross primary production. Geosci. Model Dev. 13, 1545–1581 (2020).Article 

Google Scholar 
Pastorello, G. et al. The FLUXNET2015 dataset and the ONEFlux processing pipeline for eddy covariance data. Sci. Data 7, 225 (2020).Article 

Google Scholar 
Sitch, S. et al. Evaluation of ecosystem dynamics, plant geography and terrestrial carbon cycling in the LPJ dynamic global vegetation model. Glob. Change Biol. 9, 161–185 (2003).Article 

Google Scholar 
Hänninen, H. & Tanino, K. Tree seasonality in a warming climate. Trends Plant Sci. 16, 412–416 (2011).Article 

Google Scholar 
Kikuzawa, K. & Lechowicz, M. J. Ecology of Leaf Longevity (Springer, 2011).Fu, Y. H. et al. Nutrient availability alters the correlation between spring leaf-out and autumn leaf senescence dates. Tree Physiol. 39, 1277–1284 (2019).Article 
CAS 

Google Scholar 
Lim, P. O., Kim, H. J. & Nam, H. G. Leaf senescence. Annu. Rev. Plant Biol. 58, 115–136 (2007).Article 
CAS 

Google Scholar 
Piao, S., Friedlingstein, P., Ciais, P., Viovy, N. & Demarty, J. Growing season extension and its impact on terrestrial carbon cycle in the Northern Hemisphere over the past 2 decades. Glob. Biogeochem. Cycles 21, GB3018 (2007).Jeong, S.-J., Ho, C.-H., Gim, H.-J. & Brown, M. E. Phenology shifts at start vs. end of growing season in temperate vegetation over the Northern Hemisphere for the period 1982–2008. Glob. Change Biol. 17, 2385–2399 (2011).Article 

Google Scholar 
Cong, N. et al. Changes in satellite-derived spring vegetation green-up date and its linkage to climate in China from 1982 to 2010: a multimethod analysis. Glob. Change Biol. 19, 881–891 (2013).Article 

Google Scholar 
Keenan, T. F. et al. Net carbon uptake has increased through warming-induced changes in temperate forest phenology. Nat. Clim. Change 4, 598–604 (2014).Article 
CAS 

Google Scholar 
Garonna, I., de Jong, R. & Schaepman, M. E. Variability and evolution of global land surface phenology over the past three decades (1982–2012). Glob. Change Biol. 22, 1456–1468 (2016).Article 

Google Scholar 
Smith, N. G. & Dukes, J. S. Plant respiration and photosynthesis in global-scale models: incorporating acclimation to temperature and CO2. Glob. Change Biol. 19, 45–63 (2013).Article 

Google Scholar 
Estiarte, M. & Peñuelas, J. Alteration of the phenology of leaf senescence and fall in winter deciduous species by climate change: effects on nutrient proficiency. Glob. Change Biol. 21, 1005–1017 (2015).Article 

Google Scholar 
Delpierre, N. et al. Modelling interannual and spatial variability of leaf senescence for three deciduous tree species in France. Agric. For. Meteorol. 149, 938–948 (2009).Article 

Google Scholar 
Chung, H. et al. Experimental warming studies on tree species and forest ecosystems: a literature review. J. Plant Res. 126, 447–460 (2013).Article 

Google Scholar 
Schaaf, C. B. et al. First operational BRDF, albedo nadir reflectance products from MODIS. Remote Sens. Environ. 83, 135–148 (2002).Article 

Google Scholar 
Tuck, S. L. et al. MODISTools—downloading and processing MODIS remotely sensed data in R. Ecol. Evol. 4, 4658–4668 (2014).Article 

Google Scholar 
Farquhar, G. D., von Caemmerer, S. & Berry, J. A. A biochemical model of photosynthetic CO2 assimilation in leaves of C3 species. Planta 149, 78–90 (1980).Article 
CAS 

Google Scholar 
Medlyn, B. E. et al. Reconciling the optimal and empirical approaches to modelling stomatal conductance. Glob. Change Biol. 17, 2134–2144 (2011).Article 

Google Scholar 
Stocker, B. rsofun: A modelling framework that implements the P-model for leaf-level acclimation of photosynthesis. R package version 4.3 https://github.com/computationales/rsofun (2020).Weedon, G. P. et al. The WFDEI meteorological forcing data set: WATCH Forcing Data methodology applied to ERA-Interim reanalysis data. Water Resour. Res. 50, 7505–7514 (2014).Article 

Google Scholar 
Meek, D. W., Hatfield, J. L., Howell, T. A., Idso, S. B. & Reginato, R. J. A generalized relationship between photosynthetically active radiation and solar radiation 1. Agron. J. 76, 939–945 (1984).Article 

Google Scholar 
Fick, S. E. & Hijmans, R. J. WorldClim 2: new 1‐km spatial resolution climate surfaces for global land areas. Int. J. Climatol. 37, 4302–4315 (2017).Article 

Google Scholar 
Stocker, B. ingestr: A tool to extract environmental point data from large global files or remote data servers. R package version 1.4 https://github.com/computationales/ingestr (2020).Wang, H. et al. Towards a universal model for carbon dioxide uptake by plants. Nat. Plants 3, 734–741 (2017).Article 
CAS 

Google Scholar 
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 
R Core Team. R: A Language and Environment for Statistical Computing (R Foundation for Statistical Computing, 2021).Myneni, R., Knyazikhin, Y. & Park, T. MCD15A3H MODIS/Terra+Aqua Leaf Area Index/FPAR 4-day L4 Global 500m SIN Grid V006 (NASA EOSDIS Land Processes DAAC, 2015). More

Mace, G. M. et al. Aiming higher to bend the curve of biodiversity loss. Nat. Sustain. 1, 448–451 (2018).Article 

Google Scholar 
Díaz, S., et al. Pervasive human-driven decline of life on Earth points to the need for transformative change. Science 366, eaax3100 (2019).Díaz, S. et al. Set ambitious goals for biodiversity and sustainability. Science 370, 411 (2020).Article 

Google Scholar 
Locke, H., et al. A Nature-Positive World: The Global Goal for Nature (Wildlife Conservation Society, 2020); https://library.wcs.org/doi/ctl/view/mid/33065/pubid/DMX3974900000.aspxOpen-ended Working Group on the Post-2020 Global Biodiversity Framework. First Draft of the Post-2020 Global Biodiversity Framework CBD/WG2020/3/3 (Convention on Biological Diversity, 2021).Open-Ended Working Group on the Post-2020 Global Biodiversity Framework. Draft Recommendation Submitted by the Co-Chairs CBD/WG2020/4/L.2-ANNEX (Convention on Biological Diversity, 2022).Environment Act 2021 (UK) (HM Government, 2021); https://www.legislation.gov.uk/ukpga/2021/30/contents/enactedBull, J. W. & Strange, N. The global extent of biodiversity offset implementation under no net loss policies. Nat. Sustain. 1, 790–798 (2018).Article 

Google Scholar 
Prendeville, S., Cherim, E. & Bocken, N. Circular cities: mapping six cities in transition. Environ. Innov. Soc. Transit. 26, 171–194 (2018).de Silva, G. C., Regan, E. C., Pollard, E. H. B. & Addison, P. F. E. The evolution of corporate no net loss and net positive impact biodiversity commitments: understanding appetite and addressing challenges. Bus. Strategy Environ. 28, 1481–1495 (2019).Article 

Google Scholar 
zu Ermgassen, S. O. S. E. et al. Exploring the ecological outcomes of mandatory biodiversity net gain using evidence from early‐adopter jurisdictions in England. Conserv. Lett. 14, e12820 (2021).Article 

Google Scholar 
McGlyn, J., et al. Science-Based Targets for Nature: Initial Guidance for Business (Science Based Targets Network, 2020); https://sciencebasedtargetsnetwork.org/resource-repository/zu Ermgassen, S. O. S. E. et al. Are corporate biodiversity commitments consistent with delivering ‘nature-positive’ outcomes? A review of ‘nature-positive’ definitions, company progress and challenges. J. Clean. Prod. 379, 134798 (2022).Article 

Google Scholar 
Addison, P. F. E., Bull, J. W. & Milner‐Gulland, E. J. Using conservation science to advance corporate biodiversity accountability. Conserv. Biol. 33, 307–318 (2019).Article 

Google Scholar 
Smith, T. et al. Biodiversity means business: reframing global biodiversity goals for the private sector. Conserv. Lett. 13, e12690 (2020).Article 

Google Scholar 
Maron, M. et al. Setting robust biodiversity goals. Conserv. Lett. https://doi.org/10.1111/conl.12816 (2021).Newing, H. & Perram, A. What do you know about conservation and human rights? Oryx 53, 595–596 (2019).Article 

Google Scholar 
Standard on Biodiversity Offsets (The Business and Biodiversity Offsets Programme, 2012).Arlidge, W. N. S., et al. A mitigation hierarchy approach for managing sea turtle captures in small-scale fisheries. Front. Mar. Sci. 7, 49 (2020).Squires, D. & Garcia, S. The least-cost biodiversity impact mitigation hierarchy with a focus on marine fisheries and bycatch issues. Conserv. Biol. 32, 989–997 (2018).Article 

Google Scholar 
Booth, H., Squires, D. & Milner-Gulland, E. J. The mitigation hierarchy for sharks: a risk-based framework for reconciling trade-offs between shark conservation and fisheries objectives. Fish Fish. 21, 269–289 (2020).Article 

Google Scholar 
Gupta, T. et al. Mitigation of elasmobranch bycatch in trawlers: a case study in Indian fisheries. Front. Mari. Sci. 7, 571 (2020).Budiharta, S. et al. Restoration to offset the impacts of developments at a landscape scale reveals opportunities, challenges and tough choices. Global Environ. Change 52, 152–161 (2018).Article 

Google Scholar 
Bull, J. W. et al. Net positive outcomes for nature. Nat. Ecol. Evol. 4, 4–7 (2020).Article 

Google Scholar 
Arlidge, W. N. S. et al. A global mitigation hierarchy for nature conservation. BioScience 68, 336–347 (2018).Article 

Google Scholar 
Milner-Gulland, E. J. et al. Four steps for the Earth: mainstreaming the post-2020 global biodiversity framework. One Earth 4, 75–87 (2021).Article 
ADS 

Google Scholar 
Wolff, A., Gondran, N. & Brodhag, C. Detecting unsustainable pressures exerted on biodiversity by a company. Application to the food portfolio of a retailer. J. Clean. Prod. 166, 784–797 (2017).Article 

Google Scholar 
FAOSTAT Analytical Brief 15 Land Use and Land Cover Statistics: Global, Regional and Country Trends, 1990–2018 (FAO, 2020).Williams, D. R. et al. Proactive conservation to prevent habitat losses to agricultural expansion. Nat. Sustain. 4, 314–322 (2021).Article 

Google Scholar 
Leclère, D. et al. Bending the curve of terrestrial biodiversity needs an integrated strategy. Nature 585, 551–556 (2020).Article 
ADS 

Google Scholar 
Springmann, M. et al. Health and nutritional aspects of sustainable diet strategies and their association with environmental impacts: a global modelling analysis with country-level detail. Lancet Planet. Health 2, e451–e461 (2018).Article 

Google Scholar 
Clark, M. A., Springmann, M., Hill, J. & Tilman, D. Multiple health and environmental impacts of foods. Proc. Natl Acad. Sci. USA 116, 23357 (2019).Article 
ADS 
CAS 

Google Scholar 
Willett, W. et al. Food in the Anthropocene: the EAT–Lancet Commission on healthy diets from sustainable food systems. Lancet 393, 447–492 (2019).Article 

Google Scholar 
Poore, J. & Nemecek, T. Reducing food’s environmental impacts through producers and consumers. Science 360, 987 (2018).Article 
ADS 
CAS 

Google Scholar 
Wiedmann, T., Lenzen, M., Keyßer, L. T. & Steinberger, J. K. Scientists’ warning on affluence. Nat. Commun. 11, 3107 (2020).Article 
ADS 
CAS 

Google Scholar 
Benton, T. G. et al. A ‘net zero’ equivalent target is needed to transform food systems. Nat. Food 2, 905–906 (2021). 2021.Article 

Google Scholar 
Crenna, E., Sinkko, T. & Sala, S. Biodiversity impacts due to food consumption in Europe. J. Clean. Prod. 227, 378–391 (2019).Article 
CAS 

Google Scholar 
Bull, J. W., et al. Analysis: the biodiversity footprint of the University of Oxford. Nature 604, 420–424 (2022).Harrington, R. A., Adhikari, V., Rayner, M. & Scarborough, P. Nutrient composition databases in the age of big data: foodDB, a comprehensive, real-time database infrastructure. BMJ Open 9, e026652 (2019).Article 

Google Scholar 
Chaudhary, A., Verones, F., De Baan, L. & Hellweg, S. Quantifying land use impacts on biodiversity: combining species–area models and vulnerability indicators. Environ. Sci. Technol. 49, 9987–9995 (2015).Article 
ADS 
CAS 

Google Scholar 
Winter, L., Lehmann, A., Finogenova, N. & Finkbeiner, M. Including biodiversity in life cycle assessment—state of the art, gaps and research needs. Environ. Impact Assess. Rev. 67, 88–100 (2017).Article 

Google Scholar 
Chaudhary, A. & Kastner, T. Land use biodiversity impacts embodied in international food trade. Global Environ. Change 38, 195–204 (2016).Article 

Google Scholar 
Lenzen, M. et al. International trade drives biodiversity threats in developing nations. Nature 486, 109–112 (2012).Article 
ADS 
CAS 

Google Scholar 
Bates, B., et al. National Diet and Nutrition Survey Years 1 to 9 of the Rolling Programme (2008/2009–2016/2017): Time Trend and Income Analyses (Public Health England & Food Standards Agency, 2019).Stewart, C., Piernas, C., Cook, B. & Jebb, S. A. Trends in UK meat consumption: analysis of data from years 1–11 (2008–09 to 2018–19) of the National Diet and Nutrition Survey rolling programme. Lancet Planet. Health 5, e699–e708 (2021).Article 

Google Scholar 
Nielsen, K. S. et al. Improving climate change mitigation analysis: a framework for examining feasibility. One Earth 3, 325–336 (2020).Article 
ADS 

Google Scholar 
Selinske, M. J. et al. We have a steak in it: eliciting interventions to reduce beef consumption and its impact on biodiversity. Conserv. Lett. 13, e12721 (2020).Article 

Google Scholar 
Hollands, G. J. et al. The TIPPME intervention typology for changing environments to change behaviour. Nat. Hum. Behav. 1, 1–9 (2017).Article 

Google Scholar 
Marteau, T. M., Hollands, G. J. & Fletcher, P. C. Changing human behavior to prevent disease: the importance of targeting automatic processes. Science 337, 1492–1495 (2012).Article 
ADS 
CAS 

Google Scholar 
Michie, S., van Stralen, M. M. & West, R. The behaviour change wheel: a new method for characterising and designing behaviour change interventions. Implement. Sci. 6, 42 (2011).Article 

Google Scholar 
Moran, D., Giljum, S., Kanemoto, K. & Godar, J. From satellite to supply chain: new approaches connect earth observation to economic decisions. One Earth 3, 5–8 (2020).Article 
ADS 

Google Scholar 
Godar, J., Suavet, C., Gardner, T. A., Dawkins, E. & Meyfroidt, P. Balancing detail and scale in assessing transparency to improve the governance of agricultural commodity supply chains. Environ. Res. Lett. 11, 035015 (2016).Article 
ADS 

Google Scholar 
DeFries, R. S., Fanzo, J., Mondal, P., Remans, R. & Wood, S. A. Is voluntary certification of tropical agricultural commodities achieving sustainability goals for small-scale producers? A review of the evidence. Environ. Res. Lett. 12, 033001 (2017).Article 
ADS 

Google Scholar 
Bull, J. W., Suttle, K. B., Gordon, A., Singh, N. J. & Milner-Gulland, E. J. Biodiversity offsets in theory and practice. Oryx 47, 369–380 (2013).Article 

Google Scholar 
zu Ermgassen, S. O. S. E. et al. The ecological outcomes of biodiversity offsets under “no net loss” policies: a global review. Conserv. Lett. 12, e12664 (2019).Article 

Google Scholar 
Waddock, S. Achieving sustainability requires systemic business transformation. Glob. Sustain. 3, e12 (2020).Travers, H., Walsh, J., Vogt, S., Clements, T. & Milner-Gulland, E. J. Delivering behavioural change at scale: what conservation can learn from other fields. Biol. Conserv. 257, 109092 (2021).Article 

Google Scholar 
Gaupp, F. et al. Food system development pathways for healthy, nature-positive and inclusive food systems. Nat. Food 2, 928–934 (2021).Article 

Google Scholar 
Astill, J. et al. Transparency in food supply chains: a review of enabling technology solutions. Trends Food Sci. Technol. 91, 240–247 (2019).Article 
CAS 

Google Scholar 
Poore, J & Nemecek, T. Full Excel model: life-cycle environmental impacts of food drink products. Oxford University Research Archive https://ora.ox.ac.uk/objects/uuid:a63fb28c-98f8-4313-add6-e9eca99320a5 (2018).Clark, M., et al. Estimating the environmental impacts of 57,000 food products. Proc. Natl Acad. Sci. USA 119, e2120584119 (2022).Clark, M., et al. Supplemental Data for ‘Estimating the environmental impacts of 57,000 food products’. Oxford University Research Archive https://ora.ox.ac.uk/objects/uuid:4ad0b594-3e81-4e61-aefc-5d869c799a87 (2022).Bianchi, F., Dorsel, C., Garnett, E., Aveyard, P. & Jebb, S. A. Interventions targeting conscious determinants of human behaviour to reduce the demand for meat: a systematic review with qualitative comparative analysis. IJBNPA 15, 102 (2018).
Google Scholar 
Bianchi, F., Garnett, E., Dorsel, C., Aveyard, P. & Jebb, S. A. Restructuring physical micro-environments to reduce the demand for meat: a systematic review and qualitative comparative analysis. Lancet Planet. Health 2, e384–e397 (2018).Article 

Google Scholar 
Hillier-Brown, F. C. et al. The impact of interventions to promote healthier ready-to-eat meals (to eat in, to take away or to be delivered) sold by specific food outlets open to the general public: a systematic review. Obes. Rev. 18, 227–246 (2017).Article 
CAS 

Google Scholar 
von Philipsborn, P. et al. Environmental interventions to reduce the consumption of sugar-sweetened beverages and their effects on health. Cochrane Database Syst. Rev. 6, Cd012292 (2019).
Google Scholar 
Attwood, S., Voorheis, P., Mercer, C., Davies, K. & Vennard, D. Playbook for Guiding Diners toward Plant-Rich Dishes in Food Service (World Resources Institute, 2020); https://www.wri.org/research/playbook-guiding-diners-toward-plant-rich-dishes-food-serviceGarnett, E. E., Balmford, A., Sandbrook, C., Pilling, M. A. & Marteau, T. M. Impact of increasing vegetarian availability on meal selection and sales in cafeterias. Proc. Natl Acad. Sci. USA 116, 20923 (2019).Article 
ADS 
CAS 

Google Scholar 
Reinders, M. J., Huitink, M., Dijkstra, S. C., Maaskant, A. J. & Heijnen, J. Menu-engineering in restaurants—adapting portion sizes on plates to enhance vegetable consumption: a real-life experiment. IJBNPA 14, 41 (2017).
Google Scholar 
Brunner, F., Kurz, V., Bryngelsson, D. & Hedenus, F. Carbon label at a university restaurant—label implementation and evaluation. Ecol. Econ. 146, 658–667 (2018).Article 

Google Scholar 
McClain, A. D., Hekler, E. B. & Gardner, C. D. Incorporating prototyping and iteration into intervention development: a case study of a dining hall-based intervention. J. Am. Coll. Health 61, 122–131 (2013).Article 

Google Scholar 
de Vaan, J. Eating Less Meat: How to Stimulate the Choice for a Vegetarian Option without Inducing Reactance. MSc thesis, Radboud Univ. (2018). More

Tilman, D. et al. Future threats to biodiversity and pathways to their prevention. Nature 546, 73. https://doi.org/10.1038/nature22900 (2017).Article 
ADS 
CAS 

Google Scholar 
Laurance, W. F., Sayer, J. & Cassman, K. G. Agricultural expansion and its impacts on tropical nature. Trends Ecol. Evol. 29, 107–116. https://doi.org/10.1016/j.tree.2013.12.001 (2014).Article 

Google Scholar 
Ceballos, G. et al. Accelerated modern human–induced species losses: Entering the sixth mass extinction. J. Sci. Adv. 1, e1400253. https://doi.org/10.1126/sciadv.1400253 (2015).Article 
ADS 

Google Scholar 
Cardillo, M. et al. Human population density and extinction risk in the world’s carnivores. PLoS Biol. 2, e197. https://doi.org/10.1371/journal.pbio.0020197 (2004).Article 

Google Scholar 
Estes, J. A. et al. Trophic downgrading of planet earth. Science 333, 301–306 (2011).Article 
ADS 
CAS 

Google Scholar 
Ripple, W. J. et al. Status and ecological effects of the world’s largest carnivores. Science 343, 124–148 (2014).Article 

Google Scholar 
Bauer, H. et al. Lion (Panthera leo) populations are declining rapidly across Africa, except in intensively managed areas. Proc. Natl. Acad. Sci. 112, 14895–14899 (2015).Article 
ADS 

Google Scholar 
Bauer, H., Page-Nicholson, S., Hinks, A. & Dickman, A. Guidelines for the Conservation of lion in Africa 17–24 (IUCN SSC Cat Specialist Group, 2018).
Google Scholar 
Lindsey, P. A., Roulet, P. A. & Romanach, S. S. Economic and conservation significance of the trophy hunting industry in sub-Saharan Africa. Biol. Conserv. 134, 455–469. https://doi.org/10.1016/j.biocon.2006.09.005 (2007).Article 

Google Scholar 
Vucetich, J. A. et al. The value of argument analysis for understanding ethical considerations pertaining to trophy hunting and lion conservation. Biol. Conserv. 235, 260–272. https://doi.org/10.1016/j.biocon.2019.04.012 (2019).Article 

Google Scholar 
Dube, N. Voices from the village on trophy hunting in Hwange district, Zimbabwe. Ecol. Econ. 159, 335–343. https://doi.org/10.1016/j.ecolecon.2019.02.006 (2019).Article 

Google Scholar 
Murombedzi, J. African wildlife and livelihoods. In The Promise and Performance of Community Conservation (eds Hulme, D. & Murphree, M.) 244–255 (James Currey, 2001).
Google Scholar 
Leader-Williams, N., Baldus, R. D. & Smith, R. J. Recreational hunting. In Conservation and Rural Livelihoods (eds Dickson, B. et al.) 296–316 (Blackwell Publishing Ltd., 2009).Chapter 

Google Scholar 
DiMinin, E., Leader-Williams, N. & Bradshaw, C. J. A. Banning trophy hunting will exacerbate biodiversity loss. Trends Ecol. Evol. 31, 99–102 (2016).Article 

Google Scholar 
Whitman, K., Starfield, A. M., Quadling, H. S. & Packer, C. Sustainable trophy hunting of African lions. Nature 428, 175–178 (2004).Article 
ADS 
CAS 

Google Scholar 
Packer, C. et al. Sport hunting, predator control and conservation of large carnivores. PLoS ONE 4, e5941. https://doi.org/10.1371/journal.pone.0005941 (2009).Article 
ADS 
CAS 

Google Scholar 
Mweetwa, T. et al. Quantifying lion (Panthera leo) demographic response following a three-year moratorium on trophy hunting. PLoS ONE 13, e0197030. https://doi.org/10.1371/journal.pone.0197030 (2018).Article 
CAS 

Google Scholar 
Loveridge, A. J. et al. Conservation of large predator populations: Demographic and spatial responses of African lions to the intensity of trophy hunting. Biol. Conserv. 204, 247–254. https://doi.org/10.1016/j.biocon.2016.10.024 (2016).Article 

Google Scholar 
Starfield, A. M., Shiell, J. D. & Smuts, G. L. Simulation of lion control strategies in a large game reserve. Ecol. Model. 13, 17–28 (1981).Article 

Google Scholar 
Venter, J. & Hopkins, M. E. Use of a simulation model in the management of a lion population. S. Afr. J. Wildl. Res. 18, 126–130 (1988).
Google Scholar 
Starfield, A. M. & Bleloch, A. L. Modelling the effect of contraception on part of the lion population in Etosha National Park. Applied Mathematic Dept. Report R3/82, Witwaterstrand University, South Africa. 7 (1982).Dickman, A., Becker, M., Begg, C., Loveridge, A. J. & Macdonald, D. W. Guidelines for the Conservation of Lions in Africa, Ch. 6 69–75 (IUCN SSC Cat Specialist Group, 2018).
Google Scholar 
Creel, S. et al. Assessing the sustainability of lion trophy hunting with recomendations for policy. Ecol. Appl. 26, 2347–2357. https://doi.org/10.1002/eap.1377 (2016).Article 

Google Scholar 
Barthold, J., Loveridge, A. J., Macdonald, D. W., Packer, C. & Colchero, F. Bayesian estimates of male and female African lion mortality for future use in population management. J. Appl. Ecol. 53, 295–304 (2016).Article 

Google Scholar 
Loveridge, A. J., Valeix, M., Elliot, N. B. & Macdonald, D. W. The landscape of anthropogenic mortality: How African lions respond to spatial variation in risk. J. Appl. Ecol. 54, 815–825. https://doi.org/10.1111/1365-2664.12794 (2017).Article 

Google Scholar 
Loveridge, A. J. et al. Evaluating the spatial intensity and demographic impacts of wire-snare bush-meat poaching on large carnivores. Biol. Conserv. 244, 108504 (2020).Article 

Google Scholar 
Becker, M. S. et al. Estimating past and future male loss in three Zambian lion populations. J. Wildl. Manag. 77, 128–142 (2013).Article 

Google Scholar 
Kiffner, C., Meyer, B., Muhlenberg, M. & Waltert, M. Plenty of prey, few predators: What limits lions Panthera leo in Katavi National park, western Tanzania?. Oryx 43, 52–59 (2009).Article 

Google Scholar 
Loveridge, A. J., Searle, A. W., Murindagomo, F. & Macdonald, D. W. The impact of sport hunting on the population dynamics of an African lion population in a protected area. Biol. Conserv. 134, 548–558 (2007).Article 

Google Scholar 
Miller, J. R. B. et al. Aging traits and sustainable trophy hunting of African lions. Biol. Conserv. 201, 160–168 (2016).Article 

Google Scholar 
Woodroffe, R. & Ginsberg, J. R. Edge effects and the extinction of populations inside protected areas. Science 280, 2126–2128 (1998).Article 
ADS 
CAS 

Google Scholar 
Gervasi, V., Linnell, J. D. C., Brøseth, H. & Gimenez, O. Failure to coordinate management in transboundary populations hinders the achievement of national management goals: The case of wolverines in Scandinavia. J. Appl. Ecol. 56, 1905–1915. https://doi.org/10.1111/1365-2664.13379 (2019).Article 

Google Scholar 
Breitenmoser, U. & Nobbe, C. Guidelines for the Conservation of Lions in Africa (ed IUCN CSG/SSC) 29–30 (IUCN, 2018).du Preez, B. & Lopez-Bao, J. V. Guidelines for the Conservation of the Lion in Africa (ed IUCN CSG/SSC) 76–78 (IUCN, 2018).Loveridge, A. J., Hemson, G., Davidson, Z. & Macdonald, D. W. African lions on the edge: reserve boundaries as ‘attractive sinks’ In Biology and Conservation of Wild Felids, Ch. 11 (eds Macdonald, D. W. & Loveridge, A. J.) 283–304 (Oxford University Press, London, 2010).

Google Scholar 
Borrego, N., Ozgul, A., Slotow, R. & Packer, C. Lion population dynamics: Do nomadic males matter?. Behav. Ecol. 29, 660–666. https://doi.org/10.1093/beheco/ary018%JBehavioralEcology (2018).Article 

Google Scholar 
Packer, C. et al. The case for fencing remains intact. Ecol. Lett. https://doi.org/10.1111/ele.12171 (2013).Balme, G. et al. Big cats at large: Density, structure, and spatio-temporal patterns of a leopard population free of anthropogenic mortality. Popul. Ecol. 61, 256–267. https://doi.org/10.1002/1438-390x.1023 (2019).Article 

Google Scholar 
Grünewald, C., Schleuning, M. & Böhning-Gaese, K. Biodiversity, scenery and infrastructure: Factors driving wildlife tourism in an African savannah national park. Biol. Conserv. 201, 60–68. https://doi.org/10.1016/j.biocon.2016.05.036 (2016).Article 

Google Scholar 
Pulliam, H. R. Sources, sinks, and population. Regulation 132, 652–661. https://doi.org/10.1086/284880 (1988).Article 

Google Scholar 
Lamb, C. T. et al. The ecology of human–carnivore coexistence. Proc. Natl. Acad. Sci. 117, 17876–17883. https://doi.org/10.1073/pnas.1922097117 (2020).Article 
ADS 
CAS 

Google Scholar 
Robinson, H. S., Weilgus, R. B., Cooley, H. & Cooley, S. Source—sink populations in carnivore management: cougar demography and immigration in a hunted population. Ecol. Appl. 18, 1028–1037 (2008).Article 

Google Scholar 
Creel, S. et al. Questionable policy for large carnivore hunting. Science 350, 1473–1475 (2015).Article 
ADS 
CAS 

Google Scholar 
Cushman, S. A. et al. Prioritizing core areas, corridors and conflict hotspots for lion conservation in southern Africa. PLoS ONE 13, e0196213. https://doi.org/10.1371/journal.pone.0196213 (2018).Article 
CAS 

Google Scholar 
Kelly, M. J. & Durant, S. M. Viability of the Serengeti cheetah population. Conserv. Biol. 14, 786–797 (2000).Article 

Google Scholar 
Skalski, J. R., Ryding, K. & Millspaug, J. J. Wildlife Demography: Analysis of Sex, Age, and Count Data (Elsevier Academic Press, 2005).
Google Scholar 
Hamlin, K. L., Pac, D. F., Sime, C. A., DeSimone, R. M. & Dusek, G. L. Evaluating the accuracy of ages obtained by two methods for montana ungulates. J. Wildl. Manag. 64, 441–449. https://doi.org/10.2307/3803242 (2000).Article 

Google Scholar 
Storm, D. J. et al. Estimating ages of white-tailed deer: Age and sex patterns of error using tooth wear-and-replacement and consistency of cementum annuli. Wildl Soc Bull 38, 849–856. https://doi.org/10.1002/wsb.457 (2014).Article 
ADS 

Google Scholar 
Balme, G. A., Hunter, L. & Braczkowski, A. R. Applicability of age-based hunting regulations for African Leopards. PLoS ONE 7, e35209. https://doi.org/10.1371/journal.pone.0035209 (2012).Article 
ADS 
CAS 

Google Scholar 
Gipson, P. S., Ballard, W. B., Nowak, R. M. & Mech, L. D. Accuracy and precision of estimating age of gray wolves by tooth wear. J. Wildl. Manag. 64, 752–758. https://doi.org/10.2307/3802745 (2000).Article 

Google Scholar 
Hiller, T. L. Comparison of two age-estimation techniques for cougars. J. Northwest. Nat. 77–82, 76 (2014).
Google Scholar 
Begg, C. M., Miller, J. R. B. & Begg, K. S. Effective implementation of age restrictions increases selectivity of sport hunting of the African lion. J. Appl. Ecol. 55, 139–146. https://doi.org/10.1111/1365-2664.12951 (2018).Article 

Google Scholar 
Mandisodza-Chikerema, R., Jooste, D. & Funston, P. J. Lion aging and adaptive quota management report: Ages of lions hunted and recommended quotas for 2019 in Zimbabwe. 12 (Unpublished report, Zimbabwe Parks and Wildlife Management and Panthera, Harare, Zimbabwe, 2019).Smuts, G. L., Anderson, J. L. & Austin, J. C. Age determination of the African lion (Panthera leo). J. Zool. Lond. 185, 115–146 (1978).Article 

Google Scholar 
Lindsey, P. A. et al. The trophy hunting of African lions: Scale, current management practices and factors undermining sustainability. PLoS ONE 8, 1–11 (2013).Article 

Google Scholar 
R Core Team. R: A Language and Environment for Statistical Computing. R Foundation for Statistical Computing, Vienna, Austria. https://www.R-project.org/ (2022).Packer, C. et al. Effects of trophy hunting on lion and leopard populations in Tanzania. Conserv. Biol. 25, 142–153 (2011).Article 
CAS 

Google Scholar 
Mace, G. M. & Reynolds, J. Exploitation as a conservation issue. In Conservation of Exploited Species, Ch. 1 (eds Reynolds, J. et al.) 3–15 (Cambridge University Press, Cambridge, 2001).
Google Scholar 
Struhsaker, T. T. A biologists perspective on the role of sustainable harvest in conservation. Conserv. Biol. 12, 930–932 (1998).Article 

Google Scholar  More

Galperin, M. Y. Environ. Microbiol. 6, 552–567 (2004).Article 
CAS 

Google Scholar 
Higgins, D. & Dworkin, J. FEMS Microbiol. Rev. 36, 131–148 (2012).Article 
CAS 

Google Scholar 
Maamar, H., Raj, A. & Dubnau, D. Science 317, 526–529 (2007).Article 
CAS 

Google Scholar 
Ackermann, M. Nat. Rev. Microbiol. 13, 497–508 (2015).Article 
CAS 

Google Scholar 
Robinson, R. W. Appl. Environ. Microbiol. 52, 17–27 (1986).Article 
CAS 

Google Scholar 
McIlroy, S. J. et al. Nat. Microbiol. https://doi.org/10.1038/s41564-022-01292-9 (2023).Article 

Google Scholar 
Leu, A. O. et al. ISME J. 14, 1030–1041 (2020).Article 
CAS 

Google Scholar 
Cui, M., Ma, A., Qi, H., Zhuang, X. & Zhuang, G. Microbiologyopen 4, 1–11 (2015).Article 

Google Scholar 
Haroon, M. F. et al. Nature 500, 567–570 (2013).Article 
CAS 

Google Scholar 
Fritts, R. K., McCully, A. L. & McKinlay, J. B. Microbiol. Molec. Biol. Rev. 85, e00135-20 (2021).Article 

Google Scholar  More