Ecology

De-extinction efforts that use genome editing aim to identify the genome sequence of extinct species and then edit the genome of a closely related, living species. Lin et al. explored the feasibility of this approach by sequencing ancient DNA samples of the extinct Christmas Island rat (Rattus macleari), which had been originally collected between 1900–1902. The authors then mapped the resulting sequence to reference genomes of different living Rattus species. Even when using the high-quality Norway brown rat (Rattus norvegicus) as a reference, the team found that nearly 5% of the genome sequence was unmappable owing to evolutionary divergence of the two species. Of note, the incompletely covered genomic regions were not random but disproportionately affected immune response and olfaction genes, which would have implications for the biology of any reconstructed animals. More

Okogwu, O. I., Nwonumara, G. N. & Okoh, F. A. Evaluating heavy metals pollution and exposure risk through the consumption of four commercially important fish species and water from cross river ecosystem, Nigeria. Bull. Environ. Contam. Toxicol. 102, 867–872. https://doi.org/10.1007/s00128-019-02610-4 (2019).CAS 
Article 
PubMed 

Google Scholar 
Fuentes-Gandara, F., Pinedo-Hernández, J., Marrugo-Negrete, J. & Díez, S. Human health impacts of exposure to metals through extreme consumption of fish from the Colombian Caribbean Sea. Environ. Geochem. Health 40, 229–242. https://doi.org/10.1007/s10653-016-9896-z (2018).CAS 
Article 
PubMed 

Google Scholar 
Liu, X. et al. Human health risk assessment of heavy metals in soil–vegetable system: A multi-medium analysis. Sci. Total Environ. 463, 530–540 (2013).PubMed 

Google Scholar 
Lelieveld, J., Evans, J. S., Fnais, M., Giannadaki, D. & Pozzer, A. The contribution of outdoor air pollution sources to premature mortality on a global scale. Nature 525, 367–371. https://doi.org/10.1038/nature15371 (2015).CAS 
Article 
PubMed 

Google Scholar 
Huang, R.-J. et al. High secondary aerosol contribution to particulate pollution during haze events in China. Nature 514, 218–222. https://doi.org/10.1038/nature13774 (2014).CAS 
Article 
PubMed 

Google Scholar 
Rajeshkumar, S. et al. Studies on seasonal pollution of heavy metals in water, sediment, fish and oyster from the Meiliang Bay of Taihu Lake in China. Chemosphere 191, 626–638. https://doi.org/10.1016/j.chemosphere.2017.10.078 (2018).CAS 
Article 
PubMed 

Google Scholar 
Gao, X. & Chen, C.-T.A. Heavy metal pollution status in surface sediments of the coastal Bohai Bay. Water Res. 46, 1901–1911. https://doi.org/10.1016/j.watres.2012.01.007 (2012).CAS 
Article 
PubMed 

Google Scholar 
Naser, H. A. Assessment and management of heavy metal pollution in the marine environment of the Arabian Gulf: A review. Mar. Pollut. Bull. 72, 6–13. https://doi.org/10.1016/j.marpolbul.2013.04.030 (2013).CAS 
Article 
PubMed 

Google Scholar 
Zhang, Y. et al. Heavy metals in aquatic organisms of different trophic levels and their potential human health risk in Bohai Bay, China. Environ. Sci. Pollut. Res. 23, 17801–17810 (2016).CAS 

Google Scholar 
Wei, M., Yanwen, Q., Zheng, B. & Zhang, L. Heavy metal pollution in Tianjin Bohai bay, China. J. Environ. Sci. 20, 814–819 (2008).
Google Scholar 
Zhao, B. et al. Spatiotemporal variation and potential risks of seven heavy metals in seawater, sediment, and seafood in Xiangshan Bay, China (2011–2016). Chemosphere 212, 1163–1171. https://doi.org/10.1016/j.chemosphere.2018.09.020 (2018).CAS 
Article 
PubMed 

Google Scholar 
Wang, Y. & Fang, X. Analysis of the impact of heavy metal on the Chinese aquaculture and the ecological hazard. GuangDong 836, 156.152 (2016).
Google Scholar 
Pini, J., Richir, J. & Watson, G. Metal bioavailability and bioaccumulation in the polychaete Nereis (Alitta) virens (Sars): The effects of site-specific sediment characteristics. Mar. Pollut. Bull. 95, 565–575 (2015).CAS 
PubMed 

Google Scholar 
Amoozadeh, E. et al. Marine organisms as heavy metal bioindicators in the Persian Gulf and the Gulf of Oman. Environ. Sci. Pollut. Res. 21, 2386–2395 (2014).CAS 

Google Scholar 
Gu, Y.-G., Huang, H.-H., Liu, Y., Gong, X.-Y. & Liao, X.-L. Non-metric multidimensional scaling and human risks of heavy metal concentrations in wild marine organisms from the Maowei Sea, the Beibu Gulf, South China Sea. Environ. Toxicol. Pharmacol. 59, 119–124. https://doi.org/10.1016/j.etap.2018.03.002 (2018).CAS 
Article 
PubMed 

Google Scholar 
Kennedy, A., Martinez, K., Chuang, C.-C., LaPoint, K. & McIntosh, M. Saturated fatty acid-mediated inflammation and insulin resistance in adipose tissue: Mechanisms of action and implications. J. Nutr. 139, 1–4. https://doi.org/10.3945/jn.108.098269 (2008).CAS 
Article 
PubMed 

Google Scholar 
Hao, Z. et al. Heavy metal distribution and bioaccumulation ability in marine organisms from coastal regions of Hainan and Zhoushan, China. Chemosphere 226, 340–350. https://doi.org/10.1016/j.chemosphere.2019.03.132 (2019).CAS 
Article 
PubMed 

Google Scholar 
Golden, C. D. et al. Nutrition: Fall in fish catch threatens human health. Nat. News 534, 317 (2016).
Google Scholar 
Bosch, A. C., O’Neill, B., Sigge, G. O., Kerwath, S. E. & Hoffman, L. C. Heavy metals in marine fish meat and consumer health: A review. J. Sci. Food Agric. 96, 32–48 (2016).CAS 
PubMed 

Google Scholar 
Burger, J., Gochfeld, M., Jeitner, C., Pittfield, T. & Donio, M. Heavy metals in fish from the Aleutians: Interspecific and locational differences. Environ. Res. 131, 119–130. https://doi.org/10.1016/j.envres.2014.02.016 (2014).CAS 
Article 
PubMed 

Google Scholar 
Anandkumar, A., Nagarajan, R., Prabakaran, K., Chua Han, B. & Rajaram, R. Human health risk assessment and bioaccumulation of trace metals in fish species collected from the Miri coast, Sarawak, Borneo. Mar. Pollut. Bull. 133, 655–663. https://doi.org/10.1016/j.marpolbul.2018.06.033 (2018).CAS 
Article 

Google Scholar 
Murtala, B. A., Abdul, W. O. & Akinyemi, A. A. Bioaccumulation of heavy metals in fish (Hydrocynus forskahlii, Hyperopisus bebe occidentalis and Clarias gariepinus) organs in downstream Ogun coastal water, Nigeria. J. Agric. Sci. 4, 51 (2012).
Google Scholar 
Ahmed, A. S. S., Rahman, M., Sultana, S., Babu, S. M. O. F. & Sarker, M. S. I. Bioaccumulation and heavy metal concentration in tissues of some commercial fishes from the Meghna River Estuary in Bangladesh and human health implications. Mar. Pollut. Bull. 145, 436–447. https://doi.org/10.1016/j.marpolbul.2019.06.035 (2019).CAS 
Article 
PubMed 

Google Scholar 
Sun, X. et al. Source identification, geochemical normalization and influence factors of heavy metals in Yangtze River Estuary sediment. Environ. Pollut. 241, 938–949. https://doi.org/10.1016/j.envpol.2018.05.050 (2018).CAS 
Article 
PubMed 

Google Scholar 
Dadar, M., Adel, M., NasrollahzadehSaravi, H. & Fakhri, Y. Trace element concentration and its risk assessment in common kilka (Clupeonella cultriventris caspia Bordin, 1904) from southern basin of Caspian Sea. Toxin Rev. 36, 222–227 (2017).CAS 

Google Scholar 
Chakraborty, P., Raghunadh Babu, P. V., Acharyya, T. & Bandyopadhyay, D. Stress and toxicity of biologically important transition metals (Co, Ni, Cu and Zn) on phytoplankton in a tropical freshwater system: An investigation with pigment analysis by HPLC. Chemosphere 80, 548–553. https://doi.org/10.1016/j.chemosphere.2010.04.039 (2010).CAS 
Article 
PubMed 

Google Scholar 
Handy, R. Seminar Series-Society for Experimental Biology 29–60 (Cambridge University Press, 1997).
Google Scholar 
Ahmed, M. K. et al. Human health risks from heavy metals in fish of Buriganga river, Bangladesh. Springerplus 5, 1–12 (2016).
Google Scholar 
WHO. Heavy metals-environmental aspects. Environment Health Criteria. No. 85. (1989).Xu, H. et al. Long-term study of heavy metal pollution in the northern Hangzhou Bay of China: Temporal and spatial distribution, contamination evaluation, and potential ecological risk. Environ. Sci. Pollut. Res. 28, 10718–10733 (2021).CAS 

Google Scholar 
El-Moselhy, K. M., Othman, A. I., AbdEl-Azem, H. & El-Metwally, M. E. A. Bioaccumulation of heavy metals in some tissues of fish in the Red Sea, Egypt. Egypti. J. Basic Appl. Sci. 1, 97–105. https://doi.org/10.1016/j.ejbas.2014.06.001 (2014).Article 

Google Scholar 
Jezierska, B. & Witeska, M. Soil and Water Pollution Monitoring, Protection and Remediation 107–114 (Springer, 2006).
Google Scholar 
Bawuro, A. A., Voegborlo, R. B. & Adimado, A. A. Bioaccumulation of heavy metals in some tissues of fish in Lake Geriyo, Adamawa State, Nigeria. J. Environ. Public Health 2018, 1854892. https://doi.org/10.1155/2018/1854892 (2018).CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
Zhuang, P., McBride, M. B., Xia, H., Li, N. & Li, Z. Health risk from heavy metals via consumption of food crops in the vicinity of Dabaoshan mine, South China. Sci. Total Environ. 407, 1551–1561. https://doi.org/10.1016/j.scitotenv.2008.10.061 (2009).CAS 
Article 
PubMed 

Google Scholar 
Hosseini, M., Nabavi, S. M. B., Nabavi, S. N. & Pour, N. A. Heavy metals (Cd Co, Cu, Ni, Pb, Fe, and Hg) content in four fish commonly consumed in Iran: Risk assessment for the consumers. Environ. Monit. Assess. 187, 237. https://doi.org/10.1007/s10661-015-4464-z (2015).CAS 
Article 
PubMed 

Google Scholar 
Prabhakaran, K., Nagarajan, R., MerlinFranco, F. & AnandKumar, A. Biomonitoring of Malaysian aquatic environments: A review of status and prospects. Ecohydrol. Hydrobiol. 17, 134–147. https://doi.org/10.1016/j.ecohyd.2017.03.001 (2017).Article 

Google Scholar 
Meche, A. et al. Determination of heavy metals by inductively coupled plasma-optical emission spectrometry in fish from the Piracicaba River in Southern Brazil. Microchem. J. 94, 171–174 (2010).CAS 

Google Scholar 
Zhang, Y. et al. Temporal and spatial changes of microbial community in an industrial effluent receiving area in Hangzhou Bay. J. Environ. Sci. 44, 57–68. https://doi.org/10.1016/j.jes.2015.11.023 (2016).CAS 
Article 

Google Scholar 
Huang, L. et al. Quantifying the spatiotemporal dynamics of industrial land uses through mining free access social datasets in the Mega Hangzhou Bay Region, China. Sustainability 10, 3463 (2018).
Google Scholar 
Pang, H.-J. et al. Contamination, distribution, and sources of heavy metals in the sediments of Andong tidal flat, Hangzhou bay, China. Continental Shelf Res. 110, 72–84. https://doi.org/10.1016/j.csr.2015.10.002 (2015).Article 

Google Scholar 
National Bureau of Statstics. Zhejiang Statistical Yearbook-2017 (China Statistics Press, 2017).
Google Scholar 
Chen, W., Zheng, Y., Chen, Y. & Mathews, C. An assessment of fishery yields from the East China Sea ecosystem. Mar. Fish. Rev. 59, 1–7 (1997).
Google Scholar 
Zhejiang Provincial Development and Reform Commission. Zhejiang Zhoushan Islands New Area Development Plan (In Chinese). (2021).Che, Y., He, Q. & Lin, W.-Q. The distributions of particulate heavy metals and its indication to the transfer of sediments in the Changjiang Estuary and Hangzhou Bay, China. Mar. Pollut. Bull. 46, 123–131 (2003).CAS 
PubMed 

Google Scholar 
Li, R. et al. Environmental health and ecological risk assessment of soil heavy metal pollution in the coastal cities of Estuarine Bay—a case study of Hangzhou Bay, China. Toxics 8, 75 (2020).CAS 
PubMed Central 

Google Scholar 
Bergami, E., Manno, C., Cappello, S., Vannuccini, M. L. & Corsi, I. Nanoplastics affect moulting and faecal pellet sinking in Antarctic krill (Euphausia superba) juveniles. Environ. Int. 143, 105999. https://doi.org/10.1016/j.envint.2020.105999 (2020).CAS 
Article 
PubMed 

Google Scholar 
Fang, H., Huang, L., Wang, J., He, G. & Reible, D. Environmental assessment of heavy metal transport and transformation in the Hangzhou Bay, China. J. Hazard. Mater. 302, 447–457 (2016).CAS 
PubMed 

Google Scholar 
Zhu, G. et al. Evaluation of ecosystem health and potential human health hazards in the Hangzhou Bay and Qiantang Estuary region through multiple assessment approaches. Environ. Pollut. 264, 114791. https://doi.org/10.1016/j.envpol.2020.114791 (2020).CAS 
Article 
PubMed 

Google Scholar 
Li, F. et al. Distribution and risk assessment of trace metals in sediments from Yangtze River estuary and Hangzhou Bay, China. Environ. Sci. Pollut. Res. 25, 855–866. https://doi.org/10.1007/s11356-017-0425-0 (2018).CAS 
Article 

Google Scholar 
Liu, L., Huang, X., Cao, W. & Yang, Y. Pollution load characteristics of the Hangzhou Bay and its surrounding areas. Ocean Dev. Manage 5, 108–112 (2012).
Google Scholar 
He, Z., Li, F., Dominech, S., Wen, X. & Yang, S. Heavy metals of surface sediments in the Changjiang (Yangtze River) Estuary: Distribution, speciation and environmental risks. J. Geochem. Explor. 198, 18–28. https://doi.org/10.1016/j.gexplo.2018.12.015 (2019).CAS 
Article 

Google Scholar 
Jin, X., Zhao, X., Meng, T. & Cui, Y. The Fishery Resources and the Environment of the Bohai Sea and Yellow Sea (Science Press, 2005).
Google Scholar 
Huang, Z. The Species and Distribution of Marine Organisms of China (Ocean Press, Beijing, 1994) (In Chinese).
Google Scholar 
Schram, F. R. Checklist of Marine Biota of China Seas. J. Crustac. Biol. 30, 339–339. https://doi.org/10.1651/09-3228.1 (2010).Article 

Google Scholar 
AQSIQ, P. in GB 17378.6–2007 (General Administration of Quality Supervision, Inspection and Quarantine of People’s Republic of China, 2007).Zhang, L. et al. Distribution and bioaccumulation of heavy metals in marine organisms in east and west Guangdong coastal regions, South China. Mar. Pollut. Bull. 101, 930–937. https://doi.org/10.1016/j.marpolbul.2015.10.041 (2015).CAS 
Article 
PubMed 

Google Scholar 
Zhong, W. et al. Health risk assessment of heavy metals in freshwater fish in the central and eastern North China. Ecotoxicol. Environ. Saf. 157, 343–349. https://doi.org/10.1016/j.ecoenv.2018.03.048 (2018).CAS 
Article 
PubMed 

Google Scholar 
Wang, Q. et al. Bioaccumulation and biomagnification of emerging bisphenol analogues in aquatic organisms from Taihu Lake, China. Sci. Total Environ. 598, 814–820. https://doi.org/10.1016/j.scitotenv.2017.04.167 (2017).CAS 
Article 
PubMed 

Google Scholar 
Arnot, J. A. & Gobas, F. A. A review of bioconcentration factor (BCF) and bioaccumulation factor (BAF) assessments for organic chemicals in aquatic organisms. Environ. Rev. 14, 257–297 (2006).CAS 

Google Scholar 
Duan, X., Zhao, X., Wang, B., Chen, Y. & Cao, S. Exposure Factors Handbook of Chinese Population (Adults) (China Environmental Science Press, 2013).
Google Scholar 
Chauhan, G. & Chauhan, U. Human health risk assessment of heavy metals via dietary intake of vegetables grown in wastewater irrigated area of Rewa, India. Int. J. Sci. Res. Publ. 4, 1–9 (2014).
Google Scholar 
USEPA. (Philadelphia PA; Washington, DC, 2007).Wang, X., Sato, T., Xing, B. & Tao, S. Health risks of heavy metals to the general public in Tianjin, China via consumption of vegetables and fish. Sci. Total Environ. 350, 28–37. https://doi.org/10.1016/j.scitotenv.2004.09.044 (2005).CAS 
Article 
PubMed 

Google Scholar 
USEPA. (2015).FAO/WHO. Wastewater Use in Agriculture. 988 (World Health Organization).Ahmed, A. S. S. et al. Bioaccumulation of heavy metals in some commercially important fishes from a tropical river estuary suggests higher potential health risk in children than adults. PLoS One 14, e0219336. https://doi.org/10.1371/journal.pone.0219336 (2019).CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
Saha, N., Mollah, M. Z. I., Alam, M. F. & Safiur Rahman, M. Seasonal investigation of heavy metals in marine fishes captured from the Bay of Bengal and the implications for human health risk assessment. Food Control 70, 110–118. https://doi.org/10.1016/j.foodcont.2016.05.040 (2016).CAS 
Article 

Google Scholar 
Yin, S., Feng, C., Li, Y., Yin, L. & Shen, Z. Heavy metal pollution in the surface water of the Yangtze Estuary: A 5-year follow-up study. Chemosphere 138, 718–725. https://doi.org/10.1016/j.chemosphere.2015.07.060 (2015).CAS 
Article 
PubMed 

Google Scholar 
USEPA. Risk-based concentration table. United States Environmental Protection Agency, Washington DC, Philadelphia (2000).Hu, B. et al. Assessment of heavy metal pollution and health risks in the soil-plant-human system in the Yangtze River Delta, China. Int. J. Environ. Res. Public Health 14, 1042 (2017).PubMed Central 

Google Scholar 
USEPA. in United States Environmental Protection Agency, Washington DC, Philadelphia (2010).Kwok, C. K. et al. Bioaccumulation of heavy metals in fish and Ardeid at Pearl River Estuary, China. Ecotoxicol. Environ. Saf. 106, 62–67. https://doi.org/10.1016/j.ecoenv.2014.04.016 (2014).CAS 
Article 
PubMed 

Google Scholar 
Yu, T., Zhang, Y., Hu, X. & Meng, W. Distribution and bioaccumulation of heavy metals in aquatic organisms of different trophic levels and potential health risk assessment from Taihu lake, China. Ecotoxicol. Environ. Saf. 81, 55–64. https://doi.org/10.1016/j.ecoenv.2012.04.014 (2012).CAS 
Article 

Google Scholar 
Qiu, Y.-W., Lin, D., Liu, J.-Q. & Zeng, E. Y. Bioaccumulation of trace metals in farmed fish from South China and potential risk assessment. Ecotoxicol. Environ. Saf. 74, 284–293. https://doi.org/10.1016/j.ecoenv.2010.10.008 (2011).CAS 
Article 
PubMed 

Google Scholar 
Arulkumar, A., Paramasivam, S. & Rajaram, R. Toxic heavy metals in commercially important food fishes collected from Palk Bay, Southeastern India. Mar. Pollut. Bull. 119, 454–459. https://doi.org/10.1016/j.marpolbul.2017.03.045 (2017).CAS 
Article 
PubMed 

Google Scholar 
Jonathan, M. P. et al. Metal concentrations in demersal fish species from Santa Maria Bay, Baja California Sur, Mexico (Pacific coast). Mar. Pollut. Bull. 99, 356–361. https://doi.org/10.1016/j.marpolbul.2015.07.032 (2015).CAS 
Article 
PubMed 

Google Scholar 
Liu, H., Yang, J. & Gan, J. Trace element accumulation in bivalve mussels Anodonta woodiana from Taihu Lake, China. Arch. Environ. Contam. Toxicol. 59, 593–601. https://doi.org/10.1007/s00244-010-9521-6 (2010).CAS 
Article 
PubMed 

Google Scholar 
Wang, W. X. et al. Copper and zinc contamination in oysters: Subcellular distribution and detoxification. Environ. Toxicol. Chem. 30, 1767–1774 (2011).CAS 
PubMed 

Google Scholar 
de FreitasRebelo, M., do Amaral, M. C. R. & Pfeiffer, W. C. High Zn and Cd accumulation in the oyster Crassostrea rhizophorae, and its relevance as a sentinel species. Mar. Pollut. Bull. 46, 1354–1358 (2003).
Google Scholar 
AQSIQ, P. in GB 18421–2001 (General administration of quality supervision, inspection and quarantine of People’s Republic of China, 2001).FAO/WHO. in Fifth Session [displayed 10 February 2014]. ftp://ftp.fao.org/codex/meetings/CCCF/cccf5/cf05_INF.pdf.Nauen, C. E. Compilation of legal limits for hazardous substances in fish and fishery products. FAO Fisheries Circular (FAO). no. 764. (1983).Rajeshkumar, S. & Li, X. Bioaccumulation of heavy metals in fish species from the Meiliang Bay, Taihu Lake, China. Toxicol. Rep. 5, 288–295. https://doi.org/10.1016/j.toxrep.2018.01.007 (2018).CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
Baki, M. A. et al. Concentration of heavy metals in seafood (fishes, shrimp, lobster and crabs) and human health assessment in Saint Martin Island, Bangladesh. Ecotoxicol. Environ. Saf. 159, 153–163. https://doi.org/10.1016/j.ecoenv.2018.04.035 (2018).CAS 
Article 
PubMed 

Google Scholar 
Vu, C. T., Lin, C., Yeh, G. & Villanueva, M. C. Bioaccumulation and potential sources of heavy metal contamination in fish species in Taiwan: Assessment and possible human health implications. Environ. Sci. Pollut. Res. 24, 19422–19434. https://doi.org/10.1007/s11356-017-9590-4 (2017).CAS 
Article 

Google Scholar 
Sharma, B., Singh, S. & Siddiqi, N. J. Biomedical implications of heavy metals induced imbalances in redox systems. BioMed Res. Int. 20, 14 (2014).
Google Scholar 
Feng, W., Wang, Z., Xu, H., Chen, L. & Zheng, F. Trace metal concentrations in commercial fish, crabs, and bivalves from three lagoons in the South China Sea and implications for human health. Environ. Sci. Pollut. Res. https://doi.org/10.1007/s11356-019-06712-8 (2020).Article 

Google Scholar 
Ruiz-Fernández, A. C. et al. A comparative study on metal contamination in Estero de Urias lagoon, Gulf of California, using oysters, mussels and artificial mussels: Implications on pollution monitoring and public health risk. Environ. Pollut. 243, 197–205 (2018).PubMed 

Google Scholar 
Bergstad, O. A. In Encyclopedia of Ocean Sciences (Second Edition) (ed. Steele, J. H.) 458–466 (Academic Press, 2009).
Google Scholar 
Mauchline, J. & Gordon, J. Foraging strategies of deep-sea fish. Mar. Ecol. Prog. Ser. 27, 227–238 (1986).
Google Scholar 
Li, J., He, M., Han, W. & Gu, Y. Analysis and assessment on heavy metal sources in the coastal soils developed from alluvial deposits using multivariate statistical methods. J. Hazard. Mater. 164, 976–981. https://doi.org/10.1016/j.jhazmat.2008.08.112 (2009).CAS 
Article 
PubMed 

Google Scholar 
Yu, P. Applications of hierarchical cluster analysis (CLA) and principal component analysis (PCA) in feed structure and feed molecular chemistry research, using synchrotron-based Fourier transform infrared (FTIR) microspectroscopy. J. Agric. Food Chem. 53, 7115–7127 (2005).CAS 
PubMed 

Google Scholar 
Kara, D. Evaluation of trace metal concentrations in some herbs and herbal teas by principal component analysis. Food Chem. 114, 347–354 (2009).CAS 

Google Scholar 
Chai, X. et al. Distribution, sources and assessment of heavy metals in surface sediments of the Hangzhou Bay and its adjacent areas. Acta Sci. Circum. 35, 3906–3916 (2015).CAS 

Google Scholar 
Mackay, D. & Fraser, A. Bioaccumulation of persistent organic chemicals: Mechanisms and models. Environ. Pollut. 110, 375–391. https://doi.org/10.1016/S0269-7491(00)00162-7 (2000).CAS 
Article 
PubMed 

Google Scholar 
ATSDR, T. ATSDR (Agency for toxic substances and disease registry). Prepared by Clement International Corp., under contract 205, 88–0608 (2000).Traina, A. et al. Heavy metals concentrations in some commercially key species from Sicilian coasts (Mediterranean Sea): Potential human health risk estimation. Ecotoxicol. Environ. Saf. 168, 466–478. https://doi.org/10.1016/j.ecoenv.2018.10.056 (2019).CAS 
Article 
PubMed 

Google Scholar 
Ozmen, M., Ayas, Z., Güngördü, A., Ekmekci, G. F. & Yerli, S. Ecotoxicological assessment of water pollution in Sariyar Dam Lake, Turkey. Ecotoxicol. Environ. Saf. 70, 163–173. https://doi.org/10.1016/j.ecoenv.2007.05.011 (2008).CAS 
Article 
PubMed 

Google Scholar 
Jeffrey, B. & Alison, G. Guidance for assessing chemical contaminant data for use in fish advisories. v. 1. Fish sampling and analysis-v. 4. Risk communication. (1993).Regulations, U. S. E. P. A. O. o. W. Assessing Human Health Risks from Chemically Contaminated Fish and Shellfish: A Guidance Manual. (US Environmental Protection Agency, 1989).Liu, Q., Liao, Y. & Shou, L. Concentration and potential health risk of heavy metals in seafoods collected from Sanmen Bay and its adjacent areas, China. Mar. Pollut. Bull 131, 356–364. https://doi.org/10.1016/j.marpolbul.2018.04.041 (2018).CAS 
Article 
PubMed 

Google Scholar 
Abtahi, M. et al. Heavy metals (As, Cr, Pb, Cd and Ni) concentrations in rice (Oryza sativa) from Iran and associated risk assessment: A systematic review. Toxin Rev. 36, 331–341 (2017).CAS 

Google Scholar 
WHO. WHO Technical Report Series. Evaluation of Certain Food Additives and Contaminants. Fifty-Third Report of the Joint FAO/WHO Expert Committee on Food Additives (JECFA). http://www.Who.Int/foodsafety/publications/jecfa-reports/en/ (2000).USEPA. USEPA Regional Screening Level (RSL) summary table: November 2011. (2011).Farkas, A., Salánki, J. & Specziár, A. Age-and size-specific patterns of heavy metals in the organs of freshwater fish Abramis brama L. populating a low-contaminated site. Water Res. 37, 959–964 (2003).CAS 
PubMed 

Google Scholar 
Canpolat, Ö. & Çalta, M. Heavy metals in some tissues and organs of Capoeta capoeta umbla(Heckel, 1843) fish species in relation to body size, age, sex and seasons. Fresenius Environ. Bull. 12, 961–966 (2003).CAS 

Google Scholar 
Hosseini, M., Nabavi, S. M. B., Nabavi, S. N. & Pour, N. A. Heavy metals (Cd Co, Cu, Ni, Pb, Fe, and Hg) content in four fish commonly consumed in Iran: Risk assessment for the consumers. Environ. Monit. Assess. 187, 1–7 (2015).CAS 

Google Scholar 
Jiang, X. et al. Assessment of heavy metal accumulation in freshwater fish of Dongting Lake, China: Effects of feeding habits, habitat preferences and body size. J. Environ. Sci. 112, 355–365 (2022).
Google Scholar 
Yi, Y., Tang, C., Yi, T., Yang, Z. & Zhang, S. Health risk assessment of heavy metals in fish and accumulation patterns in food web in the upper Yangtze River, China. Ecotoxicol. Environ. Saf. 145, 295–302 (2017).CAS 
PubMed 

Google Scholar 
USEPA. Assessing Human Health Risks from Chemically Contaminated Fish and Shellfish: A Guidance Manual. (US Environmental Protection Agency, 1989).Means, B. Risk-assessment guidance for superfund. Volume 1. Human health evaluation manual. Part A. Interim report (Final). (Environmental Protection Agency, Washington, DC (USA). Office of Solid Waste …, 1989).Raknuzzaman, M. et al. Trace metal contamination in commercial fish and crustaceans collected from coastal area of Bangladesh and health risk assessment. Environ. Sci. Pollut. Res. 23, 17298–17310. https://doi.org/10.1007/s11356-016-6918-4 (2016).CAS 
Article 

Google Scholar 
Kalantzi, I. et al. Metals in tissues of seabass and seabream reared in sites with oxic and anoxic substrata and risk assessment for consumers. Food Chem. 194, 659–670. https://doi.org/10.1016/j.foodchem.2015.08.072 (2016).CAS 
Article 
PubMed 

Google Scholar 
Sarkar, S., Mukherjee, S., Chattopadhyay, A. & Bhattacharya, S. Differential modulation of cellular antioxidant status in zebrafish liver and kidney exposed to low dose arsenic trioxide. Ecotoxicol. Environ. Saf. 135, 173–182. https://doi.org/10.1016/j.ecoenv.2016.09.025 (2017).CAS 
Article 
PubMed 

Google Scholar 
Mandal, B. K. & Suzuki, K. T. Arsenic round the world: A review. Talanta 58, 201–235. https://doi.org/10.1016/S0039-9140(02)00268-0 (2002).CAS 
Article 
PubMed 

Google Scholar 
Kibria, G., Hossain, M. M., Mallick, D., Lau, T. C. & Wu, R. Trace/heavy metal pollution monitoring in estuary and coastal area of Bay of Bengal, Bangladesh and implicated impacts. Mar. Pollut. Bull. 105, 393–402. https://doi.org/10.1016/j.marpolbul.2016.02.021 (2016).CAS 
Article 
PubMed 

Google Scholar 
Fang, Y. et al. Concentrations and health risks of lead, cadmium, arsenic, and mercury in rice and edible mushrooms in China. Food Chem. 147, 147–151. https://doi.org/10.1016/j.foodchem.2013.09.116 (2014).CAS 
Article 
PubMed 

Google Scholar 
Vannoort, R. & Thomson, B. New Zealand Total Diet Study—Agricultural Compound Residues (Selected Contaminant and Nutrient Elements. Ministry for Primary Industries, 2009).
Google Scholar 
Praveena, S. M., Pradhan, B. & Ismail, S. N. S. Spatial assessment of heavy metals in surface soil from Klang District (Malaysia): An example from a tropical environment. Hum. Ecol. Risk Assess. Int. J. 21, 1980–2003 (2015).CAS 

Google Scholar  More

Study sitesWe sampled foliar fungal endophytes and root fungi (root endophytes and AM fungi) in the Colorado Rockies at the Rocky Mountain Biological Laboratory, Gunnison Co., Colorado, USA (38°57’N, 106°59’W). This region has predictable decreases in air temperature (c. 0.8 °C per 100 m; [40]) and declines in soil nutrients with altitude [41], but increases in precipitation, mainly as snow [42]. The entire region is warming at rates of 0.5–1.0 °C per decade [43].To capture environmental, spatial, and grass-host specific variation in fungal guilds, we sampled 66 sites encompassing 9–13 elevations from each of six altitudinal gradients in July 2014 (Supplementary Table S1, Supplementary Fig. S1). Elevational gradients represented separate mountains in the Gunnison Basin and were located within 20 km of each other. We created a regional climate model to interpolate average number of growing degree days (GDD, base 0 °C), mean annual temperature (MAT), maximum temperature (Tmax), minimum temperature (Tmin), mean annual precipitation (MAP), and mean snow depth (MSD) for each site based on data from 29 local meteorological stations [44]. At each site, soil edaphic parameters were measured on dried soil at the UC Davis soils lab (see [24] for more details) and soil nutrients at Western Ag (Saskatoon, Canada). Soil pH was measured in a 1:1 solution with diH2O, and soil moisture was measured gravimetrically. Physical characteristics of each site (e.g., aspect, soil depth, elevation) were measured as described in Lynn et al. [44]. Environmental variation across sites was large. For example, MAT varied from 7.1 to 13.3 °C, MAP from 563 to 1171 mm, and Total N from 2 to 316 ug/g dry soil (Table S1).Host plant speciesWe focused on grasses because grasslands cover ~20% of Earth’s land surface [45] and dominate subalpine meadows of the Rocky Mountains. In addition, individual grass species spanned the entire elevational range of our study system [46], whereas tree, shrub, and forb species did not. At each location, we sampled nine adult individuals from up to 13 grass species representing five genera (Poaceae, subfamily Pooideae; Supplementary Table S1). Many sites had fewer than 13 grass species present, but all sites, except for two, had at least two grass species. Samples were composited by tissue type (leaves v. roots) and grass species within each site.Fungal compositionCollected root and leaf samples were surface sterilized (1 min in 95% ethanol, 2 min in 1% sodium hypochlorite solution, and 2 min in 70% ethanol) over ice to focus on the endophytic fungal community [34]. Following surface sterilization, samples were rinsed in purified water (Milli-Q Integral Water Purification System, EMD Millipore Corporation, Billerica, MA), stored in RNAlater, and refrigerated. All samples were then frozen in liquid nitrogen and ground using a mortar and pestle. Total DNA was extracted from ~50 mg of ground sample using QIAGEN DNeasy plant extraction kits (QIAGEN Inc., Valencia, CA).Fungal composition was characterized using barcoded primers targeting the ITS2 region for leaf and root endophytes [47], and FLR3-FLR4 primers targeting ~300 bp in the 28S region for AMF [48]. Each PCR contained 5 μL of ~1–10 ng/μL DNA template, 21.5 μL of Platinum PCR SuperMix (Thermo Fisher Scientific Inc., Waltham, MA), 1.25 μL of each primer (10 μM), 1.25 μL of 20 mg/mL BSA, and 0.44 μL of 25 mM MgCl2. For the ITS2 primers, the reactions included an initial denaturing step at 96 °C for 2 min, followed by 24 cycles of 94 °C for 30 sec, 51 °C for 40 s, and 72 °C for 2 min, with a final extension at 72 °C for 10 min. For the 28S primers, reactions started with an initial denaturing step at 93 °C for 5 min, followed by 33 cycles of 93 °C for 1 min, 55 °C for 1 min, and 72 °C for 1 min, with a final extension at 72 °C for 10 min.Three PCR replicates from each sample were pooled and then cleaned and concentrated using a ZR-96 DNA Clean & Concentrator-5 (Zymo Research Corporation, Irvine, CA). PCR was then carried out on all samples to add dual indexes and Illumina sequencing adaptors; each reaction began with an initial denaturing step at 98 °C for 30 s, followed by 7 cycles of 98 °C for 30 s, 62 °C for 30 s, and 72 °C for 30 s, with a final extension at 72 °C for 5 min. Sequencing was performed by the Genomic Sequencing and Analysis Facility at The University of Texas at Austin using paired-end 250 base Illumina MiSeq v.3 chemistry (Illumina, Inc., San Diego, CA). We aimed to obtain a minimum of 30,000 reads/sample for the ITS2 region and 20,000 reads/sample for the 28S region. All sequences are deposited in the NCBI SRA database under accession number (PRJNA639093).BioinformaticsWe processed reads to generate OTUs using commands from USEARCH (v9.2.64). Reads from previous studies [24] and this study were clustered together to improve OTU delineations for a total of 36,754,931 reads. We merged paired-end reads using the fastq_mergepairs from USEARCH with “fastq_maxdiffs” set to 20 and “fastq_maxdiffpct” set to 10 to ensure proper merging at a low error rate. The merged reads and the forward unmerged reads were trimmed at the primer sites using cutadapt with “e” set to 0.2, “m” set to 200, and untrimmed reads were discarded. Merged reads were filtered using fastq_filter from USEARCH with “fastq_maxee” set to 1.0. The forward reads were first trimmed to 230 using fastx_truncate from USEARCH with “trunclen” set to 230 and then filtered by fastq_filter from USEARCH with “fastq_maxee” set to 1.0. We then concatenated the merged and forward reads into one file and de-replicated using fastx_uniques from USEARCH with “minuniquesize” set to 2. After these steps, 11,357,274 sequences remained. We clustered these sequences to form OTUs at 97% similarity [49] using cluster_otus command from UPARSE. The reads (all reads before filtering step) of each sample were mapped to OTUs with usearch_global from USEARCH with “id” set to 0.97. We determined taxonomy for the representative OTUs using sintax from USEARCH with the database set to UNITE all eukaryotes (v. 8.2) “strand” set to both and “sintax_cutoff” set to 0.8 [50]. Representative OTUs were also blasted against Genbank with “perc_identity” set to 80 and “max_target_seqs” set to 50. All OTUs identified as “fungi” were retained, and OTUs labeled as “unknown” or “unidentified” were manually inspected based on blast results to determine retention. Our filtering criteria left between 5 and 418 OTUs per sample (Supplementary Table S2).Due to low fungal abundance in leaves [34], many leaf samples were dominated by plant sequences (average ~78% plant reads). Therefore, fungal sequence numbers in leaf samples were low, despite adequate sequencing depth to capture trends in fungal endophyte communities across sites based on prior analyses [24, 34, 35]. We included only samples that contained at least 50 fungal sequences after data processing (Leaves N = 192, Roots N = 191, AMF N = 251), and most samples had much greater sequencing depth, especially for roots (Supplementary Table S2). Nevertheless, there were no correlations between sequence read depth and richness, alpha diversity, or evenness of our samples (P  > 0.05 in all cases), and plant species did not differ in the average sequencing depth for samples (P  > 0.05). Data for each fungal OTU were transformed to the proportion of total sequence abundance to minimize any differences in sampling effort [51].Diversity and compositionWe calculated the alpha diversity metrics of richness, Shannon’s Diversity, Inverse Simpson’s Diversity, and Pielou’s Evenness. For each fungal guild, differences among plant species and elevation in alpha diversity were first determined using a general linear mixed effects model with plant species (categorical) and elevation (continuous) as fixed effects and site nested within elevation gradient (e.g., mountain identity, Supplementary Table S1, Supplementary Fig. S1) as random effects to account for the lack of statistical independence among plant species sampled at the same site and among sites located within the same mountain elevation gradient (Supplementary Fig. S1). Models were constructed using the lmer function in R package lme4 [52, 53]. To address, do fungal community patterns along environmental gradients differ among guilds: leaf endophytes, root endophytes, or arbuscular mycorrhizal fungi?, we then compared alpha diversity metrics among fungal guilds using a general linear mixed effects model with fungal guild, plant species, and elevation as fixed effects and site nested within elevation gradient as random effects. In all models, we evaluated parameter fit with analysis of deviance using Wald chi-square tests and corrected for multiple comparisons using a false discovery alpha of 0.05. Differences among grass species were determined using Tukey post-hoc tests.Because elevation is a good proxy for variation in both climate and soil parameters (Supplementary Table S1), in all community analyses, we first ran models with grass species and elevation to parse biotic versus abiotic influences on fungal OTUs, then secondly ran full variance partitioning models with all environmental covariates (Supplementary Table S1, climate, physical, soil) in addition to grass species identity and space (gradient location, Supplementary Fig. S1). Because leaf and root endophytes were sequenced using different primers than AM fungi, we could not compare composition among the three guilds directly. Instead, we compared the relative influence of biotic and abiotic drivers on fungal composition within each guild to compare patterns among guilds. To do so, we first used distance-based redundancy analysis (dbRDA) to analyze the effects of plant host species and elevation on fungal composition for general fungal communities in leaves and roots and separately for AM fungal communities in roots. All models were run on quantitative Jaccard indices of fungal composition for each guild and included site nested within elevation gradient (e.g., mountain side, Supplementary Fig. S1) as random effects. Second, to evaluate which environmental variables most strongly influenced fungal composition, we further partitioned variance in fungal composition due to grass species, climate variables (MAP, MAT, MSD, Tmax, Tmin, and GDD), soil variables (total nitrogen, total phosphorus, nitrate, ammonium, calcium, magnesium, potassium, iron, manganese, sulfur, aluminum, soil pH, soil gravimetric moisture content), physical variables (aspect degree, aspect category (e.g., cardinal direction), slope, soil depth, and elevation) and spatial variables (latitude and longitude) using the varpart function in Vegan v. 2–5.3 [54]. Plots of fungal composition by plant host were also generated using dbRDA separately for each fungal guild. Spatial variables were de-trended and tested for spatial autocorrelation using the ade4 package v. 1.7–16 [55]. When we detected significant spatial autocorrelation eigenvectors, we included these in the spatial variable matrix. To characterize how many fungal taxa occurred in multiple plant taxa and elevations, we used the VennDiagram package v. 1.6.20 [56].Turnover and rewiringTo evaluate whether fungal composition was driven by grasses associating with different fungal taxa or differing relative abundances of the same fungal taxa, we first performed a beta partitioning analysis using betapart v. 1.5.3 [57]. Each fungal guild was analyzed separately. Next, to examine turnover in the abundances of fungal functional groups (pathogens, saprotrophs, mutualists), we defined groups using the FungalTrait database, which merges previous databases into one cohesive framework of 17 functional trait types (referred to here as functional groups; [58]). We recognize that fungal functions are highly environmentally dependent and therefore these functional groups may represent potential function more than actual function. Functional group identity was ascribed to 60% of leaf endophyte and 62% of root endophyte fungal taxa. Then, cumulative abundance of proportionally transformed sequence reads in each functional group was analyzed using a general linear mixed effects model with grass species and elevation as fixed effects and site nested within elevation gradient as random effects, as above. Finally, we defined indicator species within the OTUs that comprised at least 1% of the total abundance of each fungal guild by grass host, gradient, and elevation classes (rounded to the nearest 100 m) using the indicspecies package v. 1.7.9 [59]. Functional group assignments using the FungalTrait database from above were assigned to each indicator taxon [58]. A large percentage of significant indicator taxa out of the total number of OTUs would confirm that turnover in the species identity of fungal associations is stronger than turnover in the relative abundances of the same fungal taxa.Network propertiesTo address does grass-fungal network structure track elevation?, we analyzed four properties that encompass different facets of ecological networks at the site level. First, we calculated network nestedness, or the propensity for specialists to interact with the same plant species as generalists, using the weighted NODF (Nestedness metric based on Overlap and Decreasing Fill; [60]). Second, we calculated complexity as linkage density or the average number of interactions per plant species [61]. Third, to characterize specialization, we used the H2’ Index [62]. Finally, network evenness was calculated as Alatalo’s interaction evenness [63]. In all cases, these network metrics were weighted indices to increase accuracy [64], and calculations were performed in the Bipartite package v. 2.15 [65]. To address, how much do fungal guilds differ in altitudinal variation in network structure?, we compared network-level statistics among fungal guilds using a general linear mixed effects model with fungal guild as a fixed effect, number of grass hosts as a fixed effect, and gradient as a random effect (function lmer in lme4 [52],). We compared relationships with elevation separately for each fungal guild, using general linear mixed effects models with elevation as a fixed, continuous effect, number of grass hosts within the network as a fixed, continuous effect, and gradient identity as a random effect (Supplementary Table S1, Supplementary Fig. S2). We evaluated parameter fit with analysis of deviance using Wald chi-square tests using the car package 3.0–10 in R [66].All data met model assumptions of normality of residuals and homogeneity of variance. All analyses were performed in R v. 3.5.0 [53]. More

Badola, H. K. & Aitken, S. Potential biological resources for poverty alleviation in Indian Himalaya. Biodiver. 11(3–4), 8–18 (2010).
Google Scholar 
Pandey, A., Badola, H. K., Rai, S. & Singh, S. P. Timberline structure and woody taxa regeneration towards treeline along latitudinal gradients in Khangchendzonga National Park Eastern Himalaya. PLoS ONE 13(11), e0207762 (2018).PubMed 
PubMed Central 

Google Scholar 
Isbell, F. et al. Linking the influence and dependence of people on biodiversity across scales. Nature https://doi.org/10.1038/nature22899 (2017).Article 
PubMed 
PubMed Central 

Google Scholar 
Hansen, A. J. et al. Global change in forests: Responses of species, communities, and biomes. Bio-Sciences 51, 765–779 (2001).
Google Scholar 
Gooden, B., French, K. O. & Turner, P. Invasion and management of a woody plant, Lantana camara L., alters vegetation diversity within wet sclerophyll forest in southeastern Australia. For. Ecol. Manag. 257(3), 960–967 (2009).
Google Scholar 
Xu, Q. et al. Rapid bamboo invasion (expansion) and its effects on biodiversity and soil processes. Global Ecol. Cons. https://doi.org/10.1016/j.gecco.2019.e00787 (2020).Article 

Google Scholar 
Dhar, U., Rawal, R. S. & Samant, S. S. Structural diversity and representativeness of forest vegetation in a protected area of Kumaun Himalaya, India: Implications for conservation. Biodiver. Cons. 6, 1045–1062 (1997).
Google Scholar 
Mack, R. N. et al. Biotic invasions: Causes, epidemiology, global consequences, and control. Ecol. Appl. 10(3), 689–710 (2000).
Google Scholar 
Tomimatsu, H. et al. Consequences of forest fragmentation in an understory plant community: Extensive range expansion of native dwarf bamboo. Plant Species Biol. 26, 3–12 (2011).
Google Scholar 
Sala, O. E. et al. Global biodiversity scenarios for the year 2100. Science 287, 1770–1774 (2000).CAS 
PubMed 

Google Scholar 
Royo, A. A. & Carson, W. P. On the formation of dense understory layers in forests worldwide: Consequences and implications for forest dynamics, biodiversity, and succession. Can. J. For. Res. 36, 1345–1362 (2006).
Google Scholar 
Royo, A. A., Stout, S. L. & Pierson, T. G. Restoring forest herb communities through landscape-level deer herd reductions: Is recovery limited by legacy effects?. Biol. Cons. 143, 2425–2434 (2010).
Google Scholar 
Taylor, A. H., Jinyan, H. & ShiQiang, Z. Canopy tree development and undergrowth bamboo dynamics in old-growth Abies-Betula forests in southwestern China: A 12-year study. For. Ecol. Manag. 200(1), 347–360 (2004).
Google Scholar 
Zhou, X., Chen, L. & Lin, Q. Effects of chemical foaming agents on the physico-mechanical properties and rheological behavior of bamboo powder-polypropylene foamed composites. Bio Resour. 7(2), 2183–2198 (2012).CAS 

Google Scholar 
Lima, R. A., Rother, D. C., Muler, A. E., Lepsch, I. F. & Rodrigues, R. R. Bamboo overabundance alters forest structure and dynamics in the Atlantic Forest hotspot. Biol. Conserv. 147, 32–39 (2012).
Google Scholar 
Tariyal, K. Bamboo as a successful carbon sequestration substrate in Uttarakhand: A brief analysis. Int. J. Curr. Adv. Res. 5(4), 736–738 (2016).
Google Scholar 
Badoni, A.K., Badola, H. K. & Sharma, S.P. Inter-disciplinary approach towards environmental management: A case study with wild bamboos in Garhwal Himalayas, In: Prakash R (Ed), Editor. Advances in Forestry Research in India, Vol. III, Intl. Book Distrib., Dehradun. pp 261–280 (1989).Bahadur, K. N. Bamboos in the service of man. Biol. Contemp. J. 1(2), 69–72 (1974).
Google Scholar 
Tomar, J. M. S., Hore, D. K. & Annadurai, A. Bamboos and their conservation in North-East India. Indian For. 135(6), 817–824 (2009).
Google Scholar 
Kumar, P. S., Kumari, K. U., Devi, M. P., Choudhary, V. K. & Sangeetha, A. Bamboo shoot as a source of nutraceuticals and bioactive compounds: a review. Indian J. Nat. Proc. Res. 8(1), 32–46 (2016).
Google Scholar 
Pradhan, S., Saha, G. K. & Khan, J. A. Ecology of the red panda Ailurus fulgens in the Singhalila National Park, Darjeeling, India. Biol. Cons. 98(1), 11–18 (2001).
Google Scholar 
Dorji, S., Vernes, K. & Rajaratnam, A. Habitat correlates of the Red Panda in the temperate forests of Bhutan. PLoS ONE 610, 1–11 (2011).
Google Scholar 
Mohan Ram, H. Y. & Tandon, R. Bamboos and rattans—from riches to rags. Proc. Natl. Sci. Acad. India 63(3), 245–267 (1997).
Google Scholar 
Sharma, R., Wahono, J. & Baral, H. Bamboo as an alternative bioenergy crop and powerful ally for land restoration in Indonesia. Sustainability 10, 4367 (2018).
Google Scholar 
Seethalakshmi, K.K. & Kumar, M.S.M. Bamboos of India: A Compendium. Bamboo Information Center, India, Kerala Forest Research Institute, Peechi and International Network for Bamboo and Ratten, Beijing (1998).Sarmah, A., Thomas, S., Goswami, M., Haridashan, K. & Borthakur, S. K. Rattan and bamboo flora of North-East India in a conservation perspective. In Sustainable Management of Forests (eds Arunachalan, A. & Khan, M. L.) 37–45 (International Book Distributors, 2000).
Google Scholar 
Das, M., Bhattacharya, S., Singh, P., Filgueiras, T. S. & Pal, A. Bamboo taxonomy and diversity in the era of molecular markers. Adv. Bot. Res. 47, 225–268 (2008).CAS 

Google Scholar 
Biswas, S. et al. Evidence of stress induced flowering in bamboo and comments on probable biochemical and molecular factors. J. Plant Biochem. Biotechnol. 30(4), 1020–1026 (2021).CAS 

Google Scholar 
Ray, P. K. Gregarious flowering of a common hill bamboo Arundinaria maling. Indian For. 78(2), 89–90 (1952).
Google Scholar 
Taylor, A. H. & Zisheng, Q. Culm dynamics and dry matter production of bamboos in the Wolong and Tangjiahe giant panda reserves, Sichuan, China. J. Appl. Ecol. 24, 419–433 (1987).
Google Scholar 
Okutomi, K., Shinoda, S. & Fukuda, H. Causal analysis of the invasion of broadleaved forest by bamboo in Japan. J. Veg. Sci. 7, 723–728 (1996).
Google Scholar 
Nath, A. J., Das, M. C. & Das, A. K. Gregarious flowering in woody bamboos: Does it mean end of life?. Curr. Sci. 106(1), 12–13 (2014).
Google Scholar 
Silveira, M. Ecological aspects of bamboo-dominated Forest in southwestern Amazonia: An ethnoscience perspective. Ecotropica 5, 213–216 (1999).
Google Scholar 
Song, Q. N. et al. Accessing the impacts of bamboo expansion on NPP and N cycling in evergreen broadleaved forest in subtropical China. Sci. Rep. 7(1), 1–10 (2017).ADS 

Google Scholar 
Rother, D. C., Rodrigues, R. R. & Pizo, M. A. Effects of bamboo stands on seed rain and seed limitation in a rainforest. For. Ecol. Manag. 257, 885–892 (2009).
Google Scholar 
Srivastava, V., Griess, V.C. & Padalia, H. Mapping invasion potential using ensemble modelling. A case study on Yushania maling in the Darjeeling Himalayas. Ecol Model 385:35–44 (2018).Roy, A., Bhattacharya, S., Ramprakash, M. & Kumar, A. S. Modelling critical patches of connectivity for invasive Maling bamboo (Yushania maling) in Darjeeling Himalayas using graph theoretic approach. Ecol. Model. 329, 77–85 (2016).
Google Scholar 
Stapleton, C. M. A. The morphology of woody bamboos. LinneanSocietySymposium Series 19 251–268 (Academic Press Limited, 1997).
Google Scholar 
Larpkern, P., Mor, S. R. & Totland, Q. Bamboo dominance reduces tree regeneration in a disturbed tropical forest. Oecologia 165(1), 161–168 (2011).ADS 
PubMed 

Google Scholar 
Tao, J. P., Shi, X. P. & Wang, Y. J. Effects of different bamboo densities on understory species diversity and trees regeneration in an Abies faxoniana forest, Southwest China. Sci. Res. Essays 7, 660–668 (2012).
Google Scholar 
Wang, W., Franklin, S. B., Ren, Y. & Ouellette, J. R. Growth of bamboo Fargesiaqinlingensis and regeneration of trees in a mixed hardwood-conifer forest in the Qinling Mountains, China. For. Ecol. Manag. 234(1–3), 107–115 (2006).
Google Scholar 
Gratzer, G., Rai, P. B. & Glatzel, G. The influence of the bamboo Yushaniamicrophylla on regeneration of Abies densa in central Bhutan. Can. J. For. Res. 29, 1518–1527 (1999).
Google Scholar 
Takahashi, K., Uemura, S., Suzuki, J. I. & Hara, T. Effect of understory dwarf bamboo on soil water and the growth of overstory trees in a dense secondary Betula ermanii forest, northern Japan. Ecol. Res. 18(6), 767–774 (2003).
Google Scholar 
Ito, H. & Hino, T. Effects of deer, mice and dwarf bamboo on the emergence, survival and growth of Abieshomolepis (Piceaceae) seedlings. Ecol. Res. 19(2), 217–223 (2004).
Google Scholar 
Tenzin, K. & Rinzin, A. Impact of Livestock Grazing on the Regeneration of Some Major Species of Plants in Conifer Forest (RNR-RC, 2003).
Google Scholar 
Darabant, A., Rai, P. B., Tenzin, K., Roder, W. & Gratzer, G. Cattle grazing facilitates tree regeneration in a conifer forest with palatable bamboo understory. For. Ecol. Manag. 252(1–3), 73–83 (2007).
Google Scholar 
Sinha, S. et al. Effect of altitude and climate in shaping the forest compositions of Singalila National Park in Khangchendzonga Landscape, Eastern Himalaya, India. J. Asia-Pac. Biodiver. 11(2), 267–275 (2018).
Google Scholar 
Zhang, W., Huang, D., Wang, R., Liu, J. & Du, N. Altitudinal patterns of species diversity and phylogenetic diversity across temperate mountain forests of Northern China. PLoS ONE 11(7), e0159995. https://doi.org/10.1371/journal.pone.0159995 (2016).CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
Sharma, C. M., Mishra, A. K., Tiwari, O. P., Krishna, R. & Rana, Y. S. Effect of altitudinal gradients on forest structure and composition on ridge tops in Garhwal Himalaya. Energy Ecol. Environ. 2(6), 404–417. https://doi.org/10.1007/s40974-017-0067-6(2016) (2017).Article 

Google Scholar 
Silveira, M. Ecological aspects of bamboo-dominated forests in southwestern Amazonia: An ethnoscience perspective. Ecotropica 5, 213–216 (1999).
Google Scholar 
Franklin, D. C. Vegetation phenology and growth of a facultatively deciduous bamboo in a monsoonal climate. Biotropica 37, 343–350 (2005).
Google Scholar 
Nath, A. N., Lal, R. & Das, A. K. Managing woody bamboos for carbon farming and carbon trading. Glob. Ecol. Cons. 3, 654–663 (2015).
Google Scholar 
Venkatesh, M. S., Bhatt, B. P., Kumar, K., Majumdar, B. & Singh, K. Soil properties as influenced by some important edible bamboo species in the North Eastern Himalayan region. Indian J. Bamboo Rattan 4(3), 221–230 (2005).
Google Scholar 
ICIMOD, WCD, GBPNIHESD, RECAST Kangchenjunga landscape feasibility assessment report. ICIMOD Working Paper 2017/9. Kathmandu: ICIMOD (2017).Mueller-Dombois, A. & Ellenburg, A. Aims and Methods of Vegetation Ecology 48–50 (John Wiley Sons, 1974).
Google Scholar 
Polunin, O. & Stainton, A. Flowers of the Himalaya 580 (Oxford University Press, 2001).
Google Scholar 
Ghosh, D.K. & Mallick, J.K. Flora of darjeeling himalayas and foothills: Angiosperms. Research Circle, Forest Directorate, Government of West Bengal & Bishen Singh Mahendra Pal Singh (2014).Pradhan, U. C. & Lachungpa, M. L. Sikkim Himalayan Rhododendrons 130 (Primulaceae Books, 1990).
Google Scholar 
de Bello, F., Leps, J. & Sebastia, M. T. Variations in species and functional plant diversity along climatic and grazing gradients. Ecograph 29, 801–810 (2006).
Google Scholar 
Hastie, T. J. & Tibshirani, R. J. Generalized Additive Models (Chapman and Hall, 1990).MATH 

Google Scholar 
Guisan, A., Edwards, T. C. Jr. & Hastie, T. Generalized linear and generalized additive models in studies of species distributions: Setting the scene. Ecol. Model. 157, 89–100 (2002).
Google Scholar 
McCullagh, P. & Nelder, J. A. Generalized Linear Models 2nd edn. (Chapman and Hall, 1989).MATH 

Google Scholar 
Gaira, K. S., Dhar, U. & Belwal, O. K. Potential of herbarium records to sequence phenological pattern: A case study of Aconitum heterophyllum in the Himalaya. Biodiver. Cons. 20, 2201–2210 (2011).
Google Scholar  More

Costa, F. et al. Global morbidity and mortality of leptospirosis: A systematic review. PLoS Negl. Trop. Dis. 9, e0003898. https://doi.org/10.1371/journal.pntd.0003898 (2015).Article 
PubMed 
PubMed Central 

Google Scholar 
Cosson, J.-F. et al. Epidemiology of Leptospira transmitted by rodents in southeast Asia. PLoS Negl. Trop. Dis. 8, e2902. https://doi.org/10.1371/journal.pntd.0002902 (2014).Article 
PubMed 
PubMed Central 

Google Scholar 
Jonsson, C. B., Figueiredo, L. T. M. & Vapalahti, O. A global perspective on Hantavirus ecology, epidemiology, and disease. Clin. Microbiol. Rev. 23, 412–441. https://doi.org/10.1128/CMR.00062-09 (2010).CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
Vaheri, A. et al. Uncovering the mysteries of Hantavirus infections. Nat. Rev. Microbiol. 11, 539–550. https://doi.org/10.1038/nrmicro3066 (2013).CAS 
Article 
PubMed 

Google Scholar 
Peniche Lara, G., Dzul-Rosado, K. R., Zavala Velázquez, J. E. & Zavala-Castro, J. Murine typhus: Clinical and epidemiological aspects. Colomb. Med. (Cali) 43, 175–180 (2012).Article 

Google Scholar 
Pimentel, D., Lach, L., Zuniga, R. & Morrison, D. Environmental and economic costs of nonindigenous species in the United States. Bioscience 50, 53–65. https://doi.org/10.1641/0006-3568(2000)050[0053:EAECON]2.3.CO;2 (2000).Article 

Google Scholar 
Smith, R. & Meyer, A. Rodent control methods: Non-chemical and non-lethal chemical, with special reference to food stores. in Rodent Pests and Their Control (Buckle, A.P., Smith, R. eds.). 2nd edn. 101–122. (CAB International, 2015).Himsworth, C. G., Jardine, C. M., Parsons, K. L., Feng, A. Y. T. & Patrick, D. M. The characteristics of wild rat (Rattus spp.) populations from an inner-city neighborhood with a focus on factors critical to the understanding of rat-associated zoonoses. PLoS ONE 9, e91654. https://doi.org/10.1371/journal.pone.0091654 (2014).ADS 
CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
Mari Saez, A. et al. Rodent control to fight Lassa fever: Evaluation and lessons learned from a 4-year study in Upper Guinea. PLoS Negl. Trop. Dis. 12, e0006829–e0006829. https://doi.org/10.1371/journal.pntd.0006829 (2018).Article 
PubMed 
PubMed Central 

Google Scholar 
Baldwin, R., Quinn, N., Davis, D. & Engeman, R. Effectiveness of rodenticides for managing invasive roof rats and native deer mice in orchards. Environ. Sci. Pollut. Res. 21, 5795–5802. https://doi.org/10.1007/s11356-014-2525-4 (2014).CAS 
Article 

Google Scholar 
Hadler, M. R. & Buckle, A. P. Forty five years of anticoagulant rodenticides—Past, present and future trends. Proc. Vertebr. Pest Conf. 15, 149–155 (1992).
Google Scholar 
Rost, S. et al. Novel mutations in the VKORC1 gene of wild rats and mice – A response to 50 years of selection pressure by warfarin?. BMC Genet. 10, 4. https://doi.org/10.1186/1471-2156-10-4 (2009).CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
Buckle, A., Prescott, C. & Ward, K. J. Resistance to the first and second generation anticoagulant rodenticides – A new perspective. Proc. Verebr. Pest Conf. 16, 138–144 (1994).
Google Scholar 
Goulois, J., Lambert, V., Legros, L., Benoit, E. & Lattard, V. Adaptative evolution of the Vkorc1 gene in Mus musculus domesticus is influenced by the selective pressure of anticoagulant rodenticides. Ecol. Evol. 7, 2767–2776. https://doi.org/10.1002/ece3.2829 (2017).Article 
PubMed 
PubMed Central 

Google Scholar 
Meerburg, B. G., van Gent-Pelzer, M. P. E., Schoelitsz, B. & van der Lee, T. A. J. Distribution of anticoagulant rodenticide resistance in Rattus norvegicus in the Netherlands according to Vkorc1 mutations. Pest Manag. Sci. 70, 1761–1766. https://doi.org/10.1002/ps.3809 (2014).CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
Lund, M. Rodent resistance to the anticoagulant rodenticides, with particular reference to Denmark. Bull. World Health Organ. 47, 611–618 (1972).CAS 
PubMed 
PubMed Central 

Google Scholar 
Lee, M. J. et al. Effects of culling on Leptospira interrogans carriage by rats. Emerg. Infect. Dis. 24, 356–360. https://doi.org/10.3201/eid2402.171371 (2018).Article 
PubMed 
PubMed Central 

Google Scholar 
Greaves, J. H. Resistance to anticoagulant rodenticides. in Rodent Pests and Their Control (Buckle, A.P., Smith, R. eds.). 2nd edn. 187–208. (CAB International, 2015).Lefebvre, S. B., Benoit, E. & Lattard, V. Comparative biology of the resistance to vitamin K antagonists: An overview of the resistance mechanisms in Anticoagulation Therapy (Basaran, O., Biteker, M. eds.). 20–45. (Intech Open, 2016).Grandemange, A. et al. Consequences of the Y139F Vkorc1 mutation on resistance to AVKs: In-vivo investigation in a 7th generation of congenic Y139F strain of rats. Pharmacogenet. Genomics. 19, 742–750. https://doi.org/10.1097/FPC.0b013e32832ee55b (2009).CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
Sadowski, J. A., Esmon, C. T. & Suttie, J. W. Vitamin K-dependent carboxylase. Requirements of the rat liver microsomal enzyme system. J. Biol. Chem. 251, 2770–2776 (1976).CAS 
Article 

Google Scholar 
Mooney, J. et al. VKORC1 sequence variants associated with resistance to anticoagulant rodenticides in Irish populations of Rattus norvegicus and Mus musculus domesticus. Sci. Rep. 8, 4535. https://doi.org/10.1038/s41598-018-22815-7 (2018).ADS 
CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
Thijssen, H. H. W. Warfarin-based rodenticides: Mode of action and mechanism of resistance. Pestic. Sci. 43, 73–78. https://doi.org/10.1002/ps.2780430112 (1995).CAS 
Article 

Google Scholar 
Bell, R. G. & Caldwell, P. T. Mechanism of warfarin resistance. Warfarin and the metabolism of vitamin K1. Biochemistry 12, 1759–1762. https://doi.org/10.1021/bi00733a015 (1973).CAS 
Article 
PubMed 

Google Scholar 
Pelz, H.-J. et al. The genetic basis of resistance to anticoagulants in rodents. Genetics 170, 1839–1847. https://doi.org/10.1534/genetics.104.040360 (2005).CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
Baert, K., Stuyck, J., Breyne, P., Maes, D. & Casaer, J. Distribution of anticoagulant resistance in the brown rat in Belgium. Belg. J. Zool. 142, 39–48 (2012).
Google Scholar 
Prescott, C. V., Buckle, A. P., Gibbings, J. G., Allan, E. N. W. & Stuart, A. M. Anticoagulant resistance in Norway rats (Rattus norvegicus Berk.) in Kent – A VKORC1 single nucleotide polymorphism, tyrosine139phenylalanine, new to the UK. Int. J. Pest Manag. 57, 61–65. https://doi.org/10.1080/09670874.2010.523124 (2010).CAS 
Article 

Google Scholar 
Grandemange, A., Lasseur, R., Longin-Sauvageon, C., Benoit, E. & Berny, P. Distribution of VKORC1 single nucleotide polymorphism in wild Rattus norvegicus in France. Pest Manag. Sci. 66, 270–276. https://doi.org/10.1002/ps.1869 (2009).CAS 
Article 

Google Scholar 
Goulois, J. et al. Evidence of a target resistance to antivitamin K rodenticides in the roof rat Rattus rattus: Identification and characterisation of a novel Y25F mutation in the Vkorc1 gene. Pest Manag. Sci. 72, 544–550. https://doi.org/10.1002/ps.4020 (2015).CAS 
Article 
PubMed 

Google Scholar 
Endepols, S., Klemann, N., Jacob, J. & Buckle, A. P. Resistance tests and field trials with bromadiolone for the control of Norway rats (Rattus norvegicus) on farms in Westphalia, Germany. Pest Manag. Sci. 68, 348–354. https://doi.org/10.1002/ps.2268 (2011).CAS 
Article 
PubMed 

Google Scholar 
Andru, J., Cosson, J.-F., Caliman, J.-P. & Benoit, E. Coumatetralyl resistance of Rattus tanezumi infesting oil palm plantations in Indonesia. Ecotoxicology 22, 377–386. https://doi.org/10.1007/s10646-012-1032-y (2012).CAS 
Article 
PubMed 

Google Scholar 
Department of Statistics Singapore. Population and Population Structure. https://www.singstat.gov.sg/find-data/search-by-theme/population/population-and-population-structure/latest-data (2020).Department of Statistics Singapore. Environment. https://www.singstat.gov.sg/find-data/search-by-theme/society/environment/latest-data (2020).Department of Statistics Singapore. M890531—Licensed Food Establishments (End of Period), Annual. https://www.tablebuilder.singstat.gov.sg/publicfacing/createDataTable.action?refId=14624 (2021).QGIS Development Team. QGIS Geographic Information System. QGIS Association. https://www.qgis.org/en/site/ (2021).Ivanova, N. V., Clare, E. L. & Borisenko, A. V. in DNA Barcodes: Methods and Protocols (John Kress, W. & Erickson, D.L. eds.) 153–182 (Humana Press, 2012).Pagès, M. et al. Revisiting the taxonomy of the Rattini tribe: A phylogeny-based delimitation of species boundaries. BMC Evol. Biol. 10, 184. https://doi.org/10.1186/1471-2148-10-184 (2010).CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
Pagès, M. et al. Cytonuclear discordance among Southeast Asian black rats (Rattus rattus complex). Mol. Ecol. 22, 1019–1034. https://doi.org/10.1111/mec.12149 (2013).CAS 
Article 
PubMed 

Google Scholar 
Rungrojn, A. et al. Prevalence and molecular characterization of Rickettsia spp. from wild small mammals in public parks and urban areas of Bangkok metropolitan, Thailand. Trop. Med. Infect. Dis. https://doi.org/10.3390/tropicalmed6040199 (2021).Article 
PubMed 
PubMed Central 

Google Scholar 
Wulandhari, S. A. et al. High prevalence and low diversity of chigger infestation in small mammals found in Bangkok metropolitan parks. Med. Vet. Entomol. 35, 534–546. https://doi.org/10.1111/mve.12531 (2021).CAS 
Article 
PubMed 

Google Scholar 
Cowan, P. E. et al. Vkorc1 sequencing suggests anticoagulant resistance in rats in New Zealand. Pest Manag. Sci. 73, 262–266. https://doi.org/10.1002/ps.4304 (2016).CAS 
Article 
PubMed 

Google Scholar 
Rost, S. et al. Mutations in VKORC1 cause warfarin resistance and multiple coagulation factor deficiency type 2. Nature 427, 537–541. https://doi.org/10.1038/nature02214 (2004).ADS 
CAS 
Article 
PubMed 

Google Scholar 
Wong, T. W. et al. Hantavirus infections in humans and commensal rodents in Singapore. Trans. R. Soc. Trop. Med. Hyg. 83, 248–251. https://doi.org/10.1016/0035-9203(89)90666-4 (1989).CAS 
Article 
PubMed 

Google Scholar 
Dubock, A. Pulsed baiting – A new technique for high potency, slow acting rodenticides. Proc. Vertebr. Pest Conf. 10, 123–136 (1982).
Google Scholar 
Garg, N., Singla, N., Jindal, V. & Babbar, B. Studies on bromadiolone resistance in Rattus rattus populations from Punjab, India. Pestic. Biochem. Physiol. 139, 24–31 (2017).CAS 
Article 

Google Scholar 
Song, Y., Lan, Z. & Kohn, M. H. Mitochondrial DNA phylogeography of the Norway rat. PLoS ONE 9, e88425. https://doi.org/10.1371/journal.pone.0088425 (2014).ADS 
CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
Aplin, K. P. et al. Multiple geographic origins of commensalism and complex dispersal history of black rats. PLoS ONE 6, e26357. https://doi.org/10.1371/journal.pone.0026357 (2011).ADS 
CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
Boyle, C. M. Case of apparent resistance of Rattus norvegicus Berkenhout to anticoagulant poisons. Nature 188, 517. https://doi.org/10.1038/188517a0 (1960).ADS 
Article 

Google Scholar 
Jackson, W. B. & Kaukeinen, D. Resistance of wild Norway rats in North Carolina to warfarin rodenticide. Science 176, 1343 (1972).ADS 
CAS 
Article 

Google Scholar 
Ma, X. et al. Low warfarin resistance frequency in Norway rats in two cities in China after 30 years of usage of anticoagulant rodenticides. Pest Manag. Sci. 74, 2555–2560. https://doi.org/10.1002/ps.5040 (2018).CAS 
Article 
PubMed 

Google Scholar 
Markussen, M. D. K., Heiberg, A.-C., Fredholm, M. & Kristensen, M. Differential expression of cytochrome P450 genes between bromadiolone-resistant and anticoagulant-susceptible Norway rats: A possible role for pharmacokinetics in bromadiolone resistance. Pest Manag. Sci. 64, 239–248. https://doi.org/10.1002/ps.1506 (2008).CAS 
Article 
PubMed 

Google Scholar  More

Giglio, L., Schroeder, W. & Justice, C. O. The collection 6 MODIS active fire detection algorithm and fire products. Remote Sens. Environ. 178, 31–41 (2016).ADS 
PubMed 
PubMed Central 

Google Scholar 
Grace, J., José, J. S., Meir, P., Miranda, H. S. & Montes, R. A. Productivity and carbon fluxes of tropical savannas. J. Biogeogr. 33, 387–400 (2006).
Google Scholar 
Van Der Werf, G. R. et al. Global fire emissions estimates during 1997–2016. Earth Syst. Sci. Data 9, 697–720 (2017).ADS 

Google Scholar 
Bastin, J.-F. et al. The global tree restoration potential. Science 365, 76–79 (2019).ADS 
CAS 

Google Scholar 
Russell-Smith, J. et al. Opportunities and challenges for savanna burning emissions abatement in southern Africa. J. Environ. Manage. 288, 112414 (2021).CAS 
PubMed 

Google Scholar 
Andela, N. et al. A human-driven decline in global burned area. Science 356, 1356–1362 (2017).ADS 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Wu, C. et al. Historical and future global burned area with changing climate and human demography. One Earth 4, 517–530 (2021).
Google Scholar 
Pellegrini, A. F. A. et al. Fire frequency drives decadal changes in soil carbon and nitrogen and ecosystem productivity. Nature 553, 194–198 (2018).ADS 
CAS 
PubMed 

Google Scholar 
Higgins, S. I. et al. Effects of four decades of fire manipulation on woody vegetation structure in savanna. Ecology 88, 1119–1125 (2007).
Google Scholar 
Staver, A. C., Archibald, S. & Levin, S. A. The global extent and determinants of savanna and forest as alternative biome states. Science 334, 230–232 (2011).ADS 
CAS 
PubMed 
MATH 

Google Scholar 
Shi, Z. et al. The age distribution of global soil carbon inferred from radiocarbon measurements. Nat. Geosci. 13, 555–559 (2020).ADS 
CAS 

Google Scholar 
Pellegrini, A. F. A., Hedin, L. O., Staver, A. C. & Govender, N. Fire alters ecosystem carbon and nutrients but not plant nutrient stoichiometry or composition in tropical savanna. Ecology 96, 1275–1285 (2015).PubMed 

Google Scholar 
Tilman, D. et al. Fire suppression and ecosystem carbon storage. Ecology 81, 2680–2685 (2000).
Google Scholar 
Mokany, K., Raison, R. J. & Prokushkin, A. S. Critical analysis of root:shoot ratios in terrestrial biomes. Glob. Change Biol. 12, 84–96 (2006).ADS 

Google Scholar 
de Miranda, S. D. C. et al. Regional variations in biomass distribution in Brazilian savanna woodland. Biotropica 46, 125–138 (2014).
Google Scholar 
Wigley, B. J., Cramer, M. D. & Bond, W. J. Sapling survival in a frequently burnt savanna: mobilisation of carbon reserves in Acacia karroo. Plant Ecol. 203, 1 (2009).
Google Scholar 
Sankaran, M. et al. Determinants of woody cover in African savannas. Nature 438, 846–849 (2005).ADS 
CAS 
PubMed 

Google Scholar 
Staver, A. C., Botha, J. & Hedin, L. Soils and fire jointly determine vegetation structure in an African savanna. New Phytol. 216, 1151–1160 (2017).CAS 
PubMed 

Google Scholar 
Zhou, Y., Wigley, B. J., Case, M. F., Coetsee, C. & Staver, A. C. Rooting depth as a key woody functional trait in savannas. New Phytol. 227, 1350–1361 (2020).PubMed 

Google Scholar 
Govender, N., Trollope, W. S. W., Van, & Wilgen, B. W. The effect of fire season, fire frequency, rainfall and management on fire intensity in savanna vegetation in South Africa. J. Appl. Ecol. 43, 748–758 (2006).
Google Scholar 
Colgan, M. S., Asner, G. P. & Swemmer, T. Harvesting tree biomass at the stand level to assess the accuracy of field and airborne biomass estimation in savannas. Ecol. Appl. 23, 1170–1184 (2013).PubMed 

Google Scholar 
Davies, A. B. & Asner, G. P. Elephants limit aboveground carbon gains in African savannas. Glob. Change Biol. 25, 1368–1382 (2019).ADS 

Google Scholar 
Butnor, J. R. et al. Utility of ground-penetrating radar as a root biomass survey tool in forest systems. Soil Sci. Soc. Am. J. 67, 1607–1615 (2003).ADS 
CAS 

Google Scholar 
Staver, A. C., Wigley-Coetsee, C. & Botha, J. Grazer movements exacerbate grass declines during drought in an African savanna. J. Ecol. 107, 1482–1491 (2019).
Google Scholar 
Ryan, C. M., Williams, M. & Grace, J. Above- and belowground carbon stocks in a miombo woodland landscape of Mozambique. Biotropica 43, 423–432 (2011).
Google Scholar 
Swezy, D. M. & Agee, J. K. Prescribed-fire effects on fine-root and tree mortality in old-growth ponderosa pine. Can. J. For. Res. 21, 626–634 (1991).
Google Scholar 
Canadell, J. et al. Maximum rooting depth of vegetation types at the global scale. Oecologia 108, 583–595 (1996).ADS 
CAS 
PubMed 

Google Scholar 
Coetsee, C., Bond, W. J. & February, E. C. Frequent fire affects soil nitrogen and carbon in an African savanna by changing woody cover. Oecologia 162, 1027–1034 (2010).ADS 
PubMed 

Google Scholar 
Holdo, R. M., Mack, M. C. & Arnold, S. G. Tree canopies explain fire effects on soil nitrogen, phosphorus and carbon in a savanna ecosystem. J. Veg. Sci. 23, 352–360 (2012).
Google Scholar 
Lloyd, J. et al. Contributions of woody and herbaceous vegetation to tropical savanna ecosystem productivity: a quasi-global estimate. Tree Physiol. 28, 451–468 (2008).PubMed 

Google Scholar 
Wigley, B. J., Augustine, D. J., Coetsee, C., Ratnam, J. & Sankaran, M. Grasses continue to trump trees at soil carbon sequestration following herbivore exclusion in a semiarid African savanna. Ecology 101, e03008 (2020).PubMed 

Google Scholar 
Khomo, L., Trumbore, S., Bern, C. R. & Chadwick, O. A. Timescales of carbon turnover in soils with mixed crystalline mineralogies. Soil 3, 17–30 (2017).ADS 
CAS 

Google Scholar 
Six, J., Conant, R. T., Paul, E. A. & Paustian, K. Stabilization mechanisms of soil organic matter: implications for C-saturation of soils. Plant Soil 241, 155–176 (2002).CAS 

Google Scholar 
Abreu, R. C. R. et al. The biodiversity cost of carbon sequestration in tropical savanna. Sci. Adv. 3, e1701284 (2017).ADS 
PubMed 
PubMed Central 

Google Scholar 
Bond, W. J., Stevens, N., Midgley, G. F. & Lehmann, C. E. The trouble with trees: afforestation plans for Africa. Trends Ecol. Evol. 34, 963–965 (2019).PubMed 

Google Scholar 
West, T. A., Börner, J. & Fearnside, P. M. Climatic benefits from the 2006–2017 avoided deforestation in Amazonian Brazil. Front. For. Glob. Change 2, 52 (2019).
Google Scholar 
Aleman, J. C., Blarquez, O. & Staver, C. A. Land-use change outweighs projected effects of changing rainfall on tree cover in sub-Saharan Africa. Glob. Change Biol. 22, 3013–3025 (2016).ADS 

Google Scholar 
Huang, J., Yu, H., Guan, X., Wang, G. & Guo, R. Accelerated dryland expansion under climate change. Nat. Clim. Change 6, 166–171 (2016).ADS 

Google Scholar 
Ratajczak, Z., Nippert, J. B. & Collins, S. L. Woody encroachment decreases diversity across North American grasslands and savannas. Ecology 93, 697–703 (2012).PubMed 

Google Scholar 
Smit, I. P. & Prins, H. H. Predicting the effects of woody encroachment on mammal communities, grazing biomass and fire frequency in African savannas. PLoS One 10, e0137857 (2015).PubMed 
PubMed Central 

Google Scholar 
Huxman, T. E. et al. Ecohydrological implications of woody plant encroachment. Ecology 86, 308–319 (2005).
Google Scholar 
Hermoso, V., Regos, A., Morán-Ordóñez, A., Duane, A. & Brotons, L. Tree planting: a double-edged sword to fight climate change in an era of megafires. Glob. Change Biol. 27, 3001–3003 (2021).
Google Scholar 
Venter F. A. Classification of Land for Management Planning in the Kruger National Park. PhD thesis, Univ. South Africa (1990).Biggs, R., Biggs, H. C., Dunne, T. T., Govender, N. & Potgieter, A. L. F. Experimental burn plot trial in the Kruger National Park: history, experimental design and suggestions for data analysis. Koedoe 46, 15 (2003).
Google Scholar 
Codron, J. et al. Taxonomic, anatomical, and spatio-temporal variations in the stable carbon and nitrogen isotopic compositions of plants from an African savanna. J. Archaeol. Sci. 32, 1757–1772 (2005).
Google Scholar 
Zhou, Y., Boutton, T. W. & Ben Wu, X. Soil carbon response to woody plant encroachment: importance of spatial heterogeneity and deep soil storage. J. Ecol. 105, 1738–1749 (2017).CAS 

Google Scholar 
Sheldrick B. & Wang C. In Soil Sampling and Methods of Analysis (ed. Carter, M. R.) 499–511 (CRC Press, 1993).Butnor, J. R. et al. Surface-based GPR underestimates below-stump root biomass. Plant Soil 402, 47–62 (2016).CAS 

Google Scholar 
Pau, G., Fuchs, F., Sklyar, O., Boutros, M. & Huber, W. EBImage—an R package for image processing with applications to cellular phenotypes. Bioinformatics 26, 979–981 (2010).CAS 
PubMed 
PubMed Central 

Google Scholar 
Hirano, Y. et al. Limiting factors in the detection of tree roots using ground-penetrating radar. Plant Soil 319, 15–24 (2009).CAS 

Google Scholar 
Popescu, S. C. & Wynne, R. H. Seeing the trees in the forest. Photogramm. Eng. Remote Sensing 70, 589–604 (2004).
Google Scholar 
Case, M. F., Wigley-Coetsee, C., Nzima, N., Scogings, P. F. & Staver, A. C. Severe drought limits trees in a semi-arid savanna. Ecology 100, e02842 (2019).PubMed 

Google Scholar 
Beucher S. & Meyer F. In Mathematical Morphology in Image Processing (ed. Dougherty, E. R.) 433–481 (CRC Press, 1993).Nickless, A., Scholes, R. J. & Archibald, S. A method for calculating the variance and confidence intervals for tree biomass estimates obtained from allometric equations. S. Afr. J. Sci. 107, 1–10 (2011).
Google Scholar 
Plowright A. & Roussel J.-R. ForestTools: analyzing remotely sensed forest data. R package version 0.2.1. https://CRAN.R-project.org/package=ForestTools (2020).Hijmans R. J. raster: geographic data analysis and modeling. R package version 3.3-7. https://CRAN.R-project.org/package=raster (2020).Penman J. et al. (eds) Good Practice Guidance for Land Use, Land-Use Change and Forestry (Intergovernmental Panel on Climate Change, 2003).Kuznetsova, A., Brockhoff, P. & Christensen, R. lmerTest package: tests in linear mixed effects models. J. Stat. Softw. 82, 1–26 (2017).
Google Scholar  More

Characteristics of environmental samples before and after bioremediationTable 1 lists the parameters of the samples collected from the upper aquifer (12 m) at three-time points. In sample 1, before bioremediation, the content of nitrate ions reached 2517 mg/L. Against this background, in an oxidizing environment, a high content of uranium up to 1.1 mg/L and plutonium up to 0.7 Bq/L was observed. The content of organic matter did not exceed 5.9 mg/L. The suspension contained a significant amount of clay particles. Uranium in sample 1 was predominantly in dissolved form or nanoaggregates less than 5 nm in size (Fig. 1).Figure 1Percentage distribution of uranium in the filtrate during sequential filtration of samples 1 and 3. Concentrations of U in the filtrates were determined by the ICP-MS method.Full size imageIn sample 2, a year after the injection of organic matter, the content of nitrate ions reached 320 mg/L, while the values of the redox potential continued to remain in the reduction region (− 175 mV) as they were 3 months after bioremediation. The content of organic matter reached 57.5 mg/L. The uranium content dropped to 80 μg/L, and the plutonium content was below the detection limit of the device.2 years after injection (sample 3), the content of nitrate ions increased to 970 mg/L, the redox potential entered the oxidizing region and reached + 70 mV, while no significant release of uranium into solution occurred. According to the distribution scheme of uranium (Fig. 1), most of it was associated with large particles of more than 400 microns in size of clay and ferruginous nature. The plutonium content was below the sensitivity of the method. Thus, despite the fact that after a single injection of organic matter, after two years the content of nitrate ions increased markedly and the value of the redox potential returned to the oxidizing region. Nevertheless, it should be mentioned no significant remobilization of uranium and plutonium occurred. It is important to note that according to the data in Table 1, a decrease in the content of suspended matter was observed in the course of bioremediation. A discussion of the content of organic matter in the suspended matter will be carried out in the next section.Figure 2 shows electronic maps of micrographs of a filter with a maximum pore size after filtration of sample 3. It has been established that U is mainly associated with large particles (suspensions) of aluminosilicate and ferrous nature. The distribution of Al, Si, Fe and U on the surface of the filter cake was fairly uniform.Figure 2Electron micrographs of the filters with a pore size of 2400 nm surface after sample 3 filtration with elements maps (A) Al, (B) Si, (C) Fe, (D) U (SEM EDX analysis).Full size imageAlthough at low plutonium concentration it was not possible to see it by the SEM EDX method on clays, it is well known that clay minerals montmorillonite and kaolinite could have been carrier phases for Pu39. In work on the analysis of colloidal transport of radionuclides in groundwater at Yucca mountain40 uranium was found to be dominantly associated with an unidentified phase rich in Si and Fe while Pu was shown to be preferentially adsorbed onto Mn-oxides in the presence of Fe-oxides.Laboratory simulation of biogenic associative colloids formation in environmental water samples, stimulated by H2
In a laboratory experiment with environmental samples, molecular hydrogen was used to stimulate microbial processes in order to avoid changing the content of the organic matter.Filtration studies (step-by-step filtration, Fig. 3) revealed that only 8% of organic matter in sample 1 was represented by suspended particles over 1200 nm in size. These were bacterial cells and other large particles (fulvic and humate acids, etc.). More than 50% of organic matter was in soluble form or in the form of colloidal particles up to 100 nm. In general, the distribution of organic matter in sample 3 was similar to sample 1—about 60% of organic matter was in dissolved or colloidal form and about 10% in the form of large particles.Figure 3Organic matter distribution by particle size (nm) in samples 1 and 3 before and after (B) microbial activation. Organic matter in the filtrate after each filtration step was measured using an Elementar Vario EL III CHN analyzer.Full size imageAn organic carbon content of 100 and 200 mg/L was observed in samples 1 and 2, respectively, after microbial activation by molecular hydrogen.After day 30 of incubation in sample 1 and after microbial processes, there was a noticeable increase in the content of large organic particles; their contribution reached 50%. In this case, the content of dissolved organic matter and organic particles of colloidal size decreased noticeably (their total contribution did not exceed 10% probably due to their consumption or aggregation into larger fractions). The content of organic particles with a size range of 220–450 nm had noticeably increased.In sample 3, a noticeable decrease in dissolved and colloidal organic matter was also noted; the content of organic particles of 220–100 nm and particles of 1200–400 nm increased markedly. We believe that the increase in organic particles in both samples in the range of 100–1200 nm is associated with an increase in the content of bacterial cells. Changes in the intensity of light scattering provided the most relevant information (Table 2).Table 2 The intensity of light scattering (kHz) by suspended particles of different fractions before and after day 30 of the ongoing microbial process in the stratal water (Light scattering intensity was determined by Zetasizer Nano ZS, Malvern Panalytical).Full size tableIn sample 1, before stimulation, the intensity of light scattering was at its maximum in the filtrate at 450–220 nm. In the filtrate less than 10 nm, light scattering was not detected. In filtrates larger than 450 nm and 220–50 nm, the values of the light scattering intensity were close. After microbial activation with hydrogen, a tenfold change in the intensity of light scattering was observed in the filtrate with particles larger than 2400 nm. Also, there was an almost twofold increase in filtrates with a particle size of 450–2400 nm, which is probably associated with the appearance of cells in the solution.In sample 2, before microbial activation, the maximum intensity of light scattering was observed in the filtrate with particle sizes in the range of 450–1200 nm. After microbial activation, the intensity of light scattering significantly increased in all filtrates. It is important to note that the light scattering of particles with a size characteristic of colloids (50–100 nm) increased by more than 10 times. The different behavior after hydrogen activation of two samples can probably be explained by the fact that in sample 3 the microbial community was initially more active after the injection of organic matter into the formation. In both samples, a noticeable increase in the content of coarse suspensions may indicate the agglomeration of clay suspensions by microbial polysaccharides. According to Ivanov et al.41, a similar process is observed for soil and clay particles.Laboratory simulation of the formation of biogenic associative colloids in model and environmental water samples with actinidesThe second series of experiments was carried out to evaluate the behavior of U, Np, and Pu upon activation of microbial processes. At the first stage of the laboratory simulation, a significant enlargement of large particles possibly caused by the agglomeration of natural clay and ferruginous particles due to microbial polysaccharides in natural samples was found. An important task of the second stage of the work was to assess the contribution of ferruginous and clay particles to the distribution of actinides over particles with different sizes in model solutions.When activating the microbial community in groundwater, a mixture of whey and acetate was used. However, in a laboratory simulation of this process, we decided not to use such a complex multicomponent substrate like whey. The whey contained a lot of organic suspensions and its use in this experiment would have led to even more uncertainties. A mixture of highly soluble sodium acetate and glucose substrates was added to the samples.Table 3 shows the data on the content of polysaccharides and proteins in solutions during microbial processes in samples.Table 3 Polysaccharide (A) (mg/L) and protein (B) (mg/ml) concentrations in the model solutions during incubation. Polysaccharide determination was carried out by the phenol–sulfuric acid method according to Dubois 34. Protein content was measured with the Folin phenol reagent according to Lowry 35.Full size tableNo significant increase of cells or polysaccharide content was recorded in samples with no organic matter additions. A low protein content was found in the sample NWO, which indicates that some content of cells remained in it after bioremediation. An increase in the concentration of the biomass, with peak values on day 10 and polysaccharides on day 15, was observed in all samples with additions of organic matter (O) (Table 3). The maximum accumulation of polysaccharides and protein was observed for the natural sample.On the 30th day of the experiment, there was no visible sediment in the MW sample, in the rest of the samples, there was a large amount of sediment at the bottom of the test tubes. At the same time, the solution looked almost transparent in both the MW model water sample and the MWIO sample with added iron. The average hydrodynamic radii of colloidal particles were obtained on days 3, 7, 14, 21, and 28 of the experiment (Table 4). In model water samples without added organic compounds, colloidal particles were not formed. However, by the end of the experiment, particle formation was observed. This was probably due to the transformation of colloidal matter originating from the natural water aliquot or as a result of low microbial activity.Table 4 Hydrodynamic radii of colloidal particles during the experiment, nm (The measurement accuracy was at least 2%.).Full size tableIn the presence of glucose and acetate, the emergence of the colloidal phase and a gradual increase in particle size were observed from the fifth day of incubation. The average stable hydrodynamic radii of the particles amounted to ~ 100 nm. In the presence of clay, stable colloids with the average hydrodynamic radii of 80–90 nm were formed. Stimulation of microbial processes with glucose and acetate resulted in increased particle size and partial sedimentation (samples MWO through day 20, MWIO through day 15, and NWO through day 30). After that, the sedimentation of large particles took place, and particles of smaller sizes remained in the solution.The addition of iron to the model system resulted in the formation of the particles with hydrodynamic radii of ~ 100 nm. The stimulation of the biological processes resulted in increased particle size, the formation of new particles (by day 21), and complete particle sedimentation by day 30.An important parameter used to evaluate the stability of colloidal particles in the system is the value of particles’ zeta potential. When no organic matter was added, the charge of preliminarily filtered 100–50 nm particles equaled − 29, − 26.2 mV in model water, and − 16, − 12 mV in natural water, which indicates low stability of such particles (see Table 2 Supplementary). A shift in charge of particles towards zero and positive values was observed when microbial processes were running, and this hints at the stabilization of particles in the solution.The diagrams of actinide distribution by size of colloidal particles in solutions of different nature before and after microbial stimulation on day 30 are shown in Fig. 4.Figure 4Actinide distribution by size of colloidal particles in solutions of different nature depending on the incubation time, normalized % in the filtrate. (I-before, II-after microbial stimulation on day 30). Actinides (233U, 237Np, and 239Pu) were added in the concentrations of 10–8 M/l per sample. Concentrations of 233U 239Pu were determined by liquid scintillation (Tri-Carb-3180 TR/SL liquid scintillation spectrometer) (“Perkin-Elmer,” USA).Full size imageIn the model water Pu(IV) forms true colloidal associates (up to 50%) due to deep hydrolytic polymerization. Np(V) was also partially sorbed due to slight disproportionation (by 10%). U(VI) was a stable component of soluble carbonate complexes. In the model water, increased pH and decreased Eh result in the occurrence of 99% Pu, 30% Np, and 10% U within large colloidal particles. Ultrafiltration, however, is not suitable for the assessment of the possible actinide reduction and biosorption contribution to the process of colloid formation.The microbiota and clay promote the stabilization of Pu, U, and Np in large colloidal particles. The addition of iron had no effect on actinide colloid formation, although iron caused a significant increase in neptunium colloid formation in the presence of the microbiota. This is probably due to the formation of iron-polysaccharide complexes42, which also have a high ability to chelate actinides.In Bentley More

NEWS AND VIEWS
16 March 2022

Savannahs store carbon despite frequent fires

An analysis of carbon stored in the plants and soil of an African savannah suggests that atmospheric carbon dioxide concentrations — and thus global warming — might be less affected by frequent fires than we thought.

Niall P. Hanan

0
&

Anthony M. Swemmer

1

Niall P. Hanan

Niall P. Hanan is in the Jornada Basin Long-Term Ecological Research programme, Department of Plant and Environmental Sciences, New Mexico State University, Las Cruces, New Mexico 88003, USA.

View author publications

You can also search for this author in PubMed
 Google Scholar

Anthony M. Swemmer

Anthony M. Swemmer is in the South African Environment Observation Network, Phalaborwa 1390, South Africa.

View author publications

You can also search for this author in PubMed
 Google Scholar

Twitter

Facebook

Email

Savannahs burn more frequently than any other biome, and tropical savannahs alone account for 62% of the carbon dioxide emitted from fires globally1. Strategies involving fire suppression2 or the planting of trees3 in savannahs have therefore been proposed as a means of reducing CO2 emissions and increasing carbon sequestration, thus potentially contributing to the mitigation of global climate change. But it remains unclear whether these measures would make a substantial difference to the accumulation of CO2 in the atmosphere. Writing Nature, Zhou et al.4 analyse a long-term fire experiment in Kruger National Park, South Africa, and reveal that the total amount of carbon stored in the ecosystem increases more slowly than expected in the absence of fire — challenging our assumptions about how fire affects carbon storage in savannahs.

Access options

Access through your institution

Change institution

Buy or subscribe

/* style specs start */
style{display:none!important}.LiveAreaSection-193358632 *{align-content:stretch;align-items:stretch;align-self:auto;animation-delay:0s;animation-direction:normal;animation-duration:0s;animation-fill-mode:none;animation-iteration-count:1;animation-name:none;animation-play-state:running;animation-timing-function:ease;azimuth:center;backface-visibility:visible;background-attachment:scroll;background-blend-mode:normal;background-clip:borderBox;background-color:transparent;background-image:none;background-origin:paddingBox;background-position:0 0;background-repeat:repeat;background-size:auto auto;block-size:auto;border-block-end-color:currentcolor;border-block-end-style:none;border-block-end-width:medium;border-block-start-color:currentcolor;border-block-start-style:none;border-block-start-width:medium;border-bottom-color:currentcolor;border-bottom-left-radius:0;border-bottom-right-radius:0;border-bottom-style:none;border-bottom-width:medium;border-collapse:separate;border-image-outset:0s;border-image-repeat:stretch;border-image-slice:100%;border-image-source:none;border-image-width:1;border-inline-end-color:currentcolor;border-inline-end-style:none;border-inline-end-width:medium;border-inline-start-color:currentcolor;border-inline-start-style:none;border-inline-start-width:medium;border-left-color:currentcolor;border-left-style:none;border-left-width:medium;border-right-color:currentcolor;border-right-style:none;border-right-width:medium;border-spacing:0;border-top-color:currentcolor;border-top-left-radius:0;border-top-right-radius:0;border-top-style:none;border-top-width:medium;bottom:auto;box-decoration-break:slice;box-shadow:none;box-sizing:border-box;break-after:auto;break-before:auto;break-inside:auto;caption-side:top;caret-color:auto;clear:none;clip:auto;clip-path:none;color:initial;column-count:auto;column-fill:balance;column-gap:normal;column-rule-color:currentcolor;column-rule-style:none;column-rule-width:medium;column-span:none;column-width:auto;content:normal;counter-increment:none;counter-reset:none;cursor:auto;display:inline;empty-cells:show;filter:none;flex-basis:auto;flex-direction:row;flex-grow:0;flex-shrink:1;flex-wrap:nowrap;float:none;font-family:initial;font-feature-settings:normal;font-kerning:auto;font-language-override:normal;font-size:medium;font-size-adjust:none;font-stretch:normal;font-style:normal;font-synthesis:weight style;font-variant:normal;font-variant-alternates:normal;font-variant-caps:normal;font-variant-east-asian:normal;font-variant-ligatures:normal;font-variant-numeric:normal;font-variant-position:normal;font-weight:400;grid-auto-columns:auto;grid-auto-flow:row;grid-auto-rows:auto;grid-column-end:auto;grid-column-gap:0;grid-column-start:auto;grid-row-end:auto;grid-row-gap:0;grid-row-start:auto;grid-template-areas:none;grid-template-columns:none;grid-template-rows:none;height:auto;hyphens:manual;image-orientation:0deg;image-rendering:auto;image-resolution:1dppx;ime-mode:auto;inline-size:auto;isolation:auto;justify-content:flexStart;left:auto;letter-spacing:normal;line-break:auto;line-height:normal;list-style-image:none;list-style-position:outside;list-style-type:disc;margin-block-end:0;margin-block-start:0;margin-bottom:0;margin-inline-end:0;margin-inline-start:0;margin-left:0;margin-right:0;margin-top:0;mask-clip:borderBox;mask-composite:add;mask-image:none;mask-mode:matchSource;mask-origin:borderBox;mask-position:0% 0%;mask-repeat:repeat;mask-size:auto;mask-type:luminance;max-height:none;max-width:none;min-block-size:0;min-height:0;min-inline-size:0;min-width:0;mix-blend-mode:normal;object-fit:fill;object-position:50% 50%;offset-block-end:auto;offset-block-start:auto;offset-inline-end:auto;offset-inline-start:auto;opacity:1;order:0;orphans:2;outline-color:initial;outline-offset:0;outline-style:none;outline-width:medium;overflow:visible;overflow-wrap:normal;overflow-x:visible;overflow-y:visible;padding-block-end:0;padding-block-start:0;padding-bottom:0;padding-inline-end:0;padding-inline-start:0;padding-left:0;padding-right:0;padding-top:0;page-break-after:auto;page-break-before:auto;page-break-inside:auto;perspective:none;perspective-origin:50% 50%;pointer-events:auto;position:static;quotes:initial;resize:none;right:auto;ruby-align:spaceAround;ruby-merge:separate;ruby-position:over;scroll-behavior:auto;scroll-snap-coordinate:none;scroll-snap-destination:0 0;scroll-snap-points-x:none;scroll-snap-points-y:none;scroll-snap-type:none;shape-image-threshold:0;shape-margin:0;shape-outside:none;tab-size:8;table-layout:auto;text-align:initial;text-align-last:auto;text-combine-upright:none;text-decoration-color:currentcolor;text-decoration-line:none;text-decoration-style:solid;text-emphasis-color:currentcolor;text-emphasis-position:over right;text-emphasis-style:none;text-indent:0;text-justify:auto;text-orientation:mixed;text-overflow:clip;text-rendering:auto;text-shadow:none;text-transform:none;text-underline-position:auto;top:auto;touch-action:auto;transform:none;transform-box:borderBox;transform-origin:50% 50% 0;transform-style:flat;transition-delay:0s;transition-duration:0s;transition-property:all;transition-timing-function:ease;vertical-align:baseline;visibility:visible;white-space:normal;widows:2;width:auto;will-change:auto;word-break:normal;word-spacing:normal;word-wrap:normal;writing-mode:horizontalTb;z-index:auto;-webkit-appearance:none;-moz-appearance:none;-ms-appearance:none;appearance:none;margin:0}.LiveAreaSection-193358632{width:100%}.LiveAreaSection-193358632 .login-option-buybox{display:block;width:100%;font-size:17px;line-height:30px;color:#222;padding-top:30px;font-family:Harding,Palatino,serif}.LiveAreaSection-193358632 .additional-access-options{display:block;font-weight:700;font-size:17px;line-height:30px;color:#222;font-family:Harding,Palatino,serif}.LiveAreaSection-193358632 .additional-login >li:not(:first-child)::before{transform:translateY(-50%);content:”;height:1rem;position:absolute;top:50%;left:0;border-left:2px solid #999}.LiveAreaSection-193358632 .additional-login >li:not(:first-child){padding-left:10px}.LiveAreaSection-193358632 .additional-login >li{display:inline-block;position:relative;vertical-align:middle;padding-right:10px}.BuyBoxSection-683559780{display:flex;flex-wrap:wrap;flex:1;flex-direction:row-reverse;margin:-30px -15px 0}.BuyBoxSection-683559780 .box-inner{width:100%;height:100%}.BuyBoxSection-683559780 .readcube-buybox{background-color:#f3f3f3;flex-shrink:1;flex-grow:1;flex-basis:255px;background-clip:content-box;padding:0 15px;margin-top:30px}.BuyBoxSection-683559780 .subscribe-buybox{background-color:#f3f3f3;flex-shrink:1;flex-grow:4;flex-basis:300px;background-clip:content-box;padding:0 15px;margin-top:30px}.BuyBoxSection-683559780 .subscribe-buybox-nature-plus{background-color:#f3f3f3;flex-shrink:1;flex-grow:4;flex-basis:100%;background-clip:content-box;padding:0 15px;margin-top:30px}.BuyBoxSection-683559780 .title-readcube{display:block;margin:0;margin-right:20%;margin-left:20%;font-size:24px;line-height:32px;color:#222;padding-top:30px;text-align:center;font-family:Harding,Palatino,serif}.BuyBoxSection-683559780 .title-buybox{display:block;margin:0;margin-right:29%;margin-left:29%;font-size:24px;line-height:32px;color:#222;padding-top:30px;text-align:center;font-family:Harding,Palatino,serif}.BuyBoxSection-683559780 .title-asia-buybox{display:block;margin:0;margin-right:5%;margin-left:5%;font-size:24px;line-height:32px;color:#222;padding-top:30px;text-align:center;font-family:Harding,Palatino,serif}.BuyBoxSection-683559780 .asia-link{color:#069;cursor:pointer;text-decoration:none;font-size:1.05em;font-family:-apple-system,BlinkMacSystemFont,”Segoe UI”,Roboto,Oxygen-Sans,Ubuntu,Cantarell,”Helvetica Neue”,sans-serif;line-height:1.05em6}.BuyBoxSection-683559780 .access-readcube{display:block;margin:0;margin-right:10%;margin-left:10%;font-size:14px;color:#222;padding-top:10px;text-align:center;font-family:-apple-system,BlinkMacSystemFont,”Segoe UI”,Roboto,Oxygen-Sans,Ubuntu,Cantarell,”Helvetica Neue”,sans-serif;line-height:20px}.BuyBoxSection-683559780 .access-asia-buybox{display:block;margin:0;margin-right:5%;margin-left:5%;font-size:14px;color:#222;padding-top:10px;text-align:center;font-family:-apple-system,BlinkMacSystemFont,”Segoe UI”,Roboto,Oxygen-Sans,Ubuntu,Cantarell,”Helvetica Neue”,sans-serif;line-height:20px}.BuyBoxSection-683559780 .access-buybox{display:block;margin:0;margin-right:30%;margin-left:30%;font-size:14px;color:#222;opacity:.8px;padding-top:10px;text-align:center;font-family:-apple-system,BlinkMacSystemFont,”Segoe UI”,Roboto,Oxygen-Sans,Ubuntu,Cantarell,”Helvetica Neue”,sans-serif;line-height:20px}.BuyBoxSection-683559780 .usps-buybox{display:block;margin:0;margin-right:30%;margin-left:30%;font-size:14px;color:#222;opacity:.8px;text-align:center;font-family:-apple-system,BlinkMacSystemFont,”Segoe UI”,Roboto,Oxygen-Sans,Ubuntu,Cantarell,”Helvetica Neue”,sans-serif;line-height:20px}.BuyBoxSection-683559780 .price-buybox{display:block;font-size:30px;color:#222;font-family:-apple-system,BlinkMacSystemFont,”Segoe UI”,Roboto,Oxygen-Sans,Ubuntu,Cantarell,”Helvetica Neue”,sans-serif;padding-top:30px;text-align:center}.BuyBoxSection-683559780 .price-from{font-size:14px;padding-right:10px;color:#222;font-family:-apple-system,BlinkMacSystemFont,”Segoe UI”,Roboto,Oxygen-Sans,Ubuntu,Cantarell,”Helvetica Neue”,sans-serif;line-height:20px}.BuyBoxSection-683559780 .issue-buybox{display:block;font-size:13px;text-align:center;color:#222;font-family:-apple-system,BlinkMacSystemFont,”Segoe UI”,Roboto,Oxygen-Sans,Ubuntu,Cantarell,”Helvetica Neue”,sans-serif;line-height:19px}.BuyBoxSection-683559780 .no-price-buybox{display:block;font-size:13px;line-height:18px;text-align:center;padding-right:10%;padding-left:10%;padding-bottom:20px;padding-top:30px;color:#222;font-family:-apple-system,BlinkMacSystemFont,”Segoe UI”,Roboto,Oxygen-Sans,Ubuntu,Cantarell,”Helvetica Neue”,sans-serif}.BuyBoxSection-683559780 .vat-buybox{display:block;margin-top:5px;margin-right:20%;margin-left:20%;font-size:11px;color:#222;padding-top:10px;padding-bottom:15px;text-align:center;font-family:-apple-system,BlinkMacSystemFont,”Segoe UI”,Roboto,Oxygen-Sans,Ubuntu,Cantarell,”Helvetica Neue”,sans-serif;line-height:17px}.BuyBoxSection-683559780 .button-container{display:flex;padding-right:20px;padding-left:20px;justify-content:center}.BuyBoxSection-683559780 .button-container >*{flex:1px}.BuyBoxSection-683559780 .button-container >a:hover,.Button-505204839:hover,.Button-1078489254:hover,.Button-2808614501:hover{text-decoration:none}.BuyBoxSection-683559780 .readcube-button{background:#fff;margin-top:30px}.BuyBoxSection-683559780 .button-asia{background:#069;border:1px solid #069;border-radius:0;cursor:pointer;display:block;padding:9px;outline:0;text-align:center;text-decoration:none;min-width:80px;margin-top:75px}.BuyBoxSection-683559780 .button-label-asia,.ButtonLabel-3869432492,.ButtonLabel-3296148077,.ButtonLabel-1566022830{display:block;color:#fff;font-size:17px;line-height:20px;font-family:-apple-system,BlinkMacSystemFont,”Segoe UI”,Roboto,Oxygen-Sans,Ubuntu,Cantarell,”Helvetica Neue”,sans-serif;text-align:center;text-decoration:none;cursor:pointer}.Button-505204839,.Button-1078489254,.Button-2808614501{background:#069;border:1px solid #069;border-radius:0;cursor:pointer;display:block;padding:9px;outline:0;text-align:center;text-decoration:none;min-width:80px;max-width:320px;margin-top:10px}.Button-505204839 .readcube-label,.Button-1078489254 .readcube-label,.Button-2808614501 .readcube-label{color:#069}
/* style specs end */Subscribe to Nature+Get immediate online access to the entire Nature family of 50+ journals$29.99monthlySubscribeSubscribe to JournalGet full journal access for 1 year$199.00only $3.90 per issueSubscribeAll prices are NET prices. VAT will be added later in the checkout.Tax calculation will be finalised during checkout.Buy articleGet time limited or full article access on ReadCube.$32.00BuyAll prices are NET prices.

Additional access options:

Log in

Learn about institutional subscriptions

Nature 603, 395-396 (2022)
doi: https://doi.org/10.1038/d41586-022-00689-0

Referencesvan der Werf, G. R. et al. Earth Syst. Sci. Data 9, 697–720 (2017).Article 

Google Scholar 
Russell-Smith, J. et al. Clim. Change 140, 47–61 (2017).Article 

Google Scholar 
Bastin, J.-F. et al. Science 365, 76–79 (2019).PubMed 
Article 

Google Scholar 
Zhou, Y. et al. Nature 603, 445–449 (2022).Article 

Google Scholar 
Pausas, J. G. & Bond, W. J. Trends Ecol. Evol. 35, 767–775 (2020).PubMed 
Article 

Google Scholar 
Hanan, N. P., Sea, W. B., Dangelmayr, G. & Govender, N. Am. Nat. 171, 851–856 (2008).PubMed 
Article 

Google Scholar 
Higgins, S. I. et al. Ecology 88, 1119–1125 (2007).PubMed 
Article 

Google Scholar 
Veldman, J. W. et al. BioScience 65, 1011–1018 (2015).Article 

Google Scholar 
Bayen, P., Lykke, A. M. & Thiombiano, A. J. For. Res. 27, 313–320 (2016).Article 

Google Scholar 
Govender, N., Trollope, W. S. W. & Van Wilgen, B. W. J. Appl. Ecol. 43, 748–758 (2006).Article 

Google Scholar 
Download references

Competing Interests
The authors declare no competing interests.

Related Articles

Read the paper: Limited increases in savanna carbon stocks over decades of fire suppression

Southeast Amazonia is no longer a carbon sink

Soils linked to climate change

See all News & Views

Subjects

Ecology

Climate change

Latest on:

Ecology

Limited increases in savanna carbon stocks over decades of fire suppression
Article 16 MAR 22

Are there limits to economic growth? It’s time to call time on a 50-year argument
Editorial 16 MAR 22

Forest protection: invest in professionals and their careers
Correspondence 15 MAR 22

Climate change

On the role of atmospheric model transport uncertainty in estimating the Chinese land carbon sink
Matters Arising 16 MAR 22

New land-use-change emissions indicate a declining CO2 airborne fraction
Article 16 MAR 22

Antarctic sea ice hits lowest minimum on record
News 11 MAR 22

Jobs

Research Associate / Postdoc (m/f/x)

Technische Universität Dresden (TU Dresden)
01069 Dresden, Germany

Research Associate (m/f/x)

Technische Universität Dresden (TU Dresden)
01069 Dresden, Germany

Call for applications for Multiple PhD Positions

IMPRS for Many Particle Systems in Structured Environments
Dresden, Germany

Postdoctoral Fellow in Myalgic Encephalomyelitis/Chronic Fatigue Syndrome

The University of British Columbia (UBC)
Vancouver, Canada More