Philippa Kaur

Galloway, J. N., Townsend, W. H., Erisman, J. W., Bekunda, M. & Cai, Z. Transformation of the nitrogen cycle: Recent trends, questions and potential solutions. Science 320, 889–892 (2008).ADS 
CAS 
PubMed 
Article 

Google Scholar 
Phoenix, G. K. et al. Impacts of atmospheric nitrogen deposition: Responses of multiple plant and soil parameters across contrasting ecosystems in long-term field experiments. Glob. Change Biol. 18, 1197–1215 (2012).ADS 
Article 

Google Scholar 
Pons, T. L., van der, Werf, A. & Lambers, H. Photosynthetic nitrogen use efficiency of inherently slow- and fast-growing species: Possible explanations for observed differences. In A Whole Plant Perspective on Carbon-Nitrogen Interactions (eds Roy, J., Garnier, E.) 61–77 (SPB Academic Publishing, The Hague, 1994).Ai, Z. M., Xue, S., Wang, G. L. & Liu, G. B. Responses of Non-structural carbohydrates and C:N: P stoichiometry of Bothriochloa ischaemum to nitrogen addition on the Loess Plateau, China. J. Plant Growth Regul. 36, 714–722 (2017).CAS 
Article 

Google Scholar 
Marklein, A. R. & Houlton, B. Z. Nitrogen inputs accelerate phosphorus cycling rates across a wide variety of terrestrial ecosystems. New Phytol. 193, 696–704 (2012).CAS 
PubMed 
Article 

Google Scholar 
Dietze, M. C. et al. Nonstructural carbon in woody plants. Annu. Rev. Plant Biol. 65, 667–687 (2014).CAS 
PubMed 
Article 

Google Scholar 
Hartmann, H. & Trumbore, S. Understanding the roles of nonstructural carbohydrates in forest trees -from what we can measure to what we want to know. New Phytol. 211, 386–403 (2016).CAS 
PubMed 
Article 

Google Scholar 
Yang, Q. P. et al. Different responses of non-structural carbohydrates in above-ground tissues/organs and root to extreme drought and re-watering in Chinese fir (Cunninghamia lanceolata) saplings. Trees 30, 1863–1871 (2016).CAS 
Article 

Google Scholar 
Peng, Z. T. et al. Non-structural carbohydrates regulated by nitrogen and phosphorus fertilization varied with organs and fertilizer levels in Moringa oleifera Seedlings. J. Plant Growth Regul. 40, 1777–1786 (2021).CAS 
Article 

Google Scholar 
Nardini, A. et al. Rooting depth, water relations and non-structural carbohydrate dynamics in three woody angiosperms deferentially affected by an extreme summer drought. Plant Cell Environ. 39, 618–627 (2016).CAS 
PubMed 
Article 

Google Scholar 
Wang, F. C. et al. Effects of experimental nitrogen addition on nutrients and nonstructural carbohydrates of dominant understory plants in a Chinese Fir plantation. Forests 10, 155 (2019).Article 

Google Scholar 
Elser, J. J. et al. Growth rate-stoichiometry couplings in diverse biota. Ecol. Lett. 6, 936–943 (2003).Article 

Google Scholar 
Jinm, X. M. et al. Ecological stoichiometry and biomass response of Agropyron michnoi under simulated N deposition in a sandy grassland, China. J. Arid Land. 12, 741–751 (2020).Article 

Google Scholar 
Jing, H. et al. Nitrogen addition changes the stoichiometry and growth rate of different organsin pinus tabuliformis seedlings. Front. Plant Sci. 8, 1922 (2017).PubMed 
PubMed Central 
Article 

Google Scholar 
Zhan, S. X., Wang, Y., Zhu, Z. C., Li, W. H. & Bai, Y. F. Nitrogen enrichment alters plant N: P stoichiometry and intensifies phosphorus limitation in a steppe ecosystem. Environ. Exp. Bot. 134, 21–32 (2017).CAS 
Article 

Google Scholar 
Stiling, P. & Cornelissen, T. How does elevated carbon dioxide (CO2) affect plant–herbivore interactions? A field experiment and meta-analysis of CO2 -mediated changes on plant chemistry and herbivore performance. Glob. Change Biol. 13, 1823–1842 (2007).ADS 
Article 

Google Scholar 
Wang, X. G. et al. Responses of C:N: P stoichiometry of plants from a Hulunbuir grassland to salt stress, drought and nitrogen addition. Phyton-Int. J. Exp. Bot. 87, 123–132 (2018).
Google Scholar 
Liu, X. J. et al. Enhanced nitrogen deposition over China. Nature 494, 459–462 (2013).ADS 
CAS 
PubMed 
Article 

Google Scholar 
Huang, W. J., Houlton, B. Z., Marklein, A. R., Liu, J. X. & Zhou, G. Y. Plant stoichiometric responses to elevated CO2 vary with nitrogen and phosphorus inputs: Evidence from a global-scale meta-analysis. Sci. Rep. 5, 18225 (2015).ADS 
CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Wang, X. et al. Effects of nutrient addition on nitrogen, phosphorus and non-structural carbohydrates concentrations in leaves of dominant plant species in a semiarid steppe. Chin. J. Ecol. 33, 1795–1802 (2014).
Google Scholar 
Yang, D. X., Song, L. & Jin, G. Z. The soil C:N: P stoichiometry is more sensitive than the leaf C:N: P stoichiometry to nitrogen addition: A four-year nitrogen addition experiment in a Pinus koraiensis plantation. Plant Soil 442, 183–198 (2019).CAS 
Article 

Google Scholar 
Chong, P. F., Zhan, J., Li, Y. & Jia, X. Y. Carbon dioxide and precipitation alter Reaumuria soongorica root morphology by regulating the levels of soluble sugars and phytohormones. Acta Physiol. Plant 41, 184 (2019).Article 
CAS 

Google Scholar 
Ma, X. Z. & Wang, X. P. Biomass partitioning and allometric relations of the Reaumuria soongorica shrub in Alxa steppe desert in NW China. For. Ecol. Manag. 468, 118–178 (2020).Article 

Google Scholar 
He, F. L., Bao, A. K., Wang, S. M. & Jin, H. X. NaCl stimulates growth and alleviates drought stress in the salt-secreting xerophyte Reaumuria soongorica. Environ. Exp. Bot. 162, 433–443 (2019).CAS 
Article 

Google Scholar 
Xu, D. H. et al. Photosynthetic parameters and carbon reserves of a resurrection plant Reaumuria soongorica during dehydration and rehydration. Plant Growth Regul. 60, 183–190 (2010).CAS 
Article 

Google Scholar 
Zhang, H. et al. miRNA–mRNA integrated analysis reveals roles for miRNAs in a typical halophyte, Reaumuria soongorica, during seed germination under salt stress. Plants 9, 351 (2020).CAS 
PubMed Central 
Article 

Google Scholar 
Bai, Y. M., Li, Y., Shan, L. S., Su, M. & Zhang, W. T. Effects of precipitation change and nitrogen addition on root morphological characteristics of Reaumuria soongorica. Arid Zone Res. 37, 1284–1292 (2020).
Google Scholar 
Hedwall, P. O., Nordin, A., Strengbom, J., Brunet, J. & Olsson, B. Does background nitrogen deposition affect the response of boreal vegetation to fertilization?. Oecologia 173, 615–624 (2013).ADS 
CAS 
PubMed 
Article 

Google Scholar 
Wang, G., Fahey, T. J., Xue, S. & Liu, F. Root morphology and architecture respond to N addition in Pinus tabuliformis, west China. Oecologia 171, 583–590 (2013).ADS 
PubMed 
Article 

Google Scholar 
Grechi, I. et al. Effect of light and nitrogen supply on internal C: N balance and control of root-to-shoot biomass allocation in grapevine. Environ. Exp. Bot. 59, 139–149 (2007).CAS 
Article 

Google Scholar 
Xiao, L., Liu, G., Li, P. & Xue, S. Nitrogen addition has a stronger effect on stoichiometries of non-structural carbohydrates, nitrogen and phosphorus in Bothriochloa ischaemum than elevated CO2. Plant Growth Regul. 83, 325–334 (2017).CAS 
Article 

Google Scholar 
Quentin, A. G. et al. Non-structural carbohydrates in woody plants compared among laboratories. Tree Physiol. 35, 1146–1165 (2015).CAS 
PubMed 

Google Scholar 
White, L. M. Carbohydrate reserves of grasses: A review. J. Range Manag. 26, 13–18 (1973).CAS 
Article 

Google Scholar 
Millard, P., Sommerkorn, M. & Grelet, G. A. Environmental change and carbon limitation in trees: A biochemical, ecophysiological and ecosystem appraisal. New Phytol. 175, 11–28 (2007).CAS 
PubMed 
Article 

Google Scholar 
Zhang, T., Cao, Y., Chen, Y. M. & Liu, G. B. Non-structural carbohydrate dynamics in Robinia pseudoacacia saplings under three levels of continuous drought stress. Trees 29, 1837–1849 (2015).CAS 
Article 

Google Scholar 
Chapin, F. S., Schulze, E. D. & Mooney, H. A. The ecology and economics of storage in plants. Annu. Rev. Ecol. Syst. 21, 423–447 (1990).Article 

Google Scholar 
Sardans, J., Rivas-Ubach, A. & Peñuelas, J. The C:N: P stoichiometry of organisms and ecosystems in a changing world: A review and perspectives. Perspect. Plant Ecol. Evolut. Syst. 14, 33–47 (2012).Article 

Google Scholar 
Xia, J. Y. & Wan, S. Q. Global response patterns of terrestrial plant species to nitrogen addition. New Phytol. 179, 428–439 (2008).CAS 
PubMed 
Article 

Google Scholar 
Yang, Y. H., Luo, Y. Q., Lu, M., Schädel, C. & Han, W. X. Terrestrial C: N stoichiometry in response to elevated CO2 and N addition: a synthesis of two meta-analyses. Plant Soil 343, 393–400 (2011).CAS 
Article 

Google Scholar 
Mayor, J. R., Wright, S. J. & Turner, B. L. Species-specific responses of foliar nutrients to long-term nitrogen and phosphorus additions in a lowland tropical forest. J. Ecol. 102, 36–44 (2014).CAS 
Article 

Google Scholar 
Koerselman, A. & Meuleman, A. F. The vegetation ratio: A new tool to detect the nature of nutrient limitation. J. Appl. Ecol. 33, 1441–1450 (1996).Article 

Google Scholar 
Gusewell, S. N: P ratios in terrestrial plants: variation and functional significance. New Phytol. 164, 243–266 (2004).PubMed 
Article 

Google Scholar 
Wang, S., Shan, L. S., Li, Y., Zhang, Z. Z. & Ma, J. Effect of Precipitation on the Stoichiometric Characteristics of Carbon, Nitrogen and Phosphorus of Reaumuria soongarica and Salsola passerina. Acta Bot. Boreal. Occident. Sin. 40, 0335–0344 (2020).ADS 

Google Scholar 
Niu, D. C., Li, Q., Jiang, S. G., Chang, P. J. & Fu, H. Seasonal variations of leaf C:N: P stoichiometry of six shrubs in desert of China’s Alxa Plateau. Chin. J. Plant Ecol. 37, 317–325 (2013).Article 

Google Scholar 
Kleyer, M. & Minden, V. Why functional ecology should consider all plant organs: An allocation-based perspective. Basic Appl. Ecol. 16, 1–9 (2015).Article 

Google Scholar 
Yemm, E. & Willis, A. The estimation of carbohydrates in plant extracts by anthrone. Biochem. J. 57, 508–514 (1954).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Bao, S. D. Soil and Agricultural Chemistry Analysis 3rd edn. (China Agriculture Press, Beijing, 2000).
Google Scholar  More

Incentivizing farmers to shift from conventional to regenerative practices could help fulfil the United Nations Food Systems commitments to transform food supply chains — as well as reducing carbon emissions (see L. A. Schulte et al. Nature Sustain. 5, 384–388; 2022).
Competing Interests
The authors declare no competing interests. More

Cole, J., Findlay, S. & Pace, M. Bacterial production in fresh and saltwater ecosystems: a cross-system overview. Mar. Ecol. Prog. Ser. 43, 1–10 (1988).ADS 
Article 

Google Scholar 
Azam, F. et al. The Ecological Role of Water-Column Microbes in the Sea. Mar. Ecol. Prog. Ser. 10, 257–263 (1983).ADS 
Article 

Google Scholar 
Cotner, J. B. & Biddanda, B. A. Small players, large role: Microbial influence on biogeochemical processes in pelagic aquatic ecosystems. Ecosystems. 5, 105–121 (2002).CAS 
Article 

Google Scholar 
Falkowski, P. G., Fenchel, T. & Delong, E. F. The microbial engines that drive earth’s biogeochemical cycles. Science. 320, 1034–1039 (2008).ADS 
CAS 
PubMed 
Article 

Google Scholar 
Newton, R. J., Jones, S. E., Eiler, A., McMahon, K. D. & Bertilsson, S. A Guide to the Natural History of Freshwater Lake Bacteria. Microbiol. Mol. Biol. Rev. 75, 14–49 (2011).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Coleman, M. L. et al. Genomic islands and the ecology and evolution of Prochlorococcus. Science. 311, 1768–1770 (2006).ADS 
CAS 
PubMed 
Article 

Google Scholar 
Franzosa, E. A. et al. Sequencing and beyond: Integrating molecular ‘omics’ for microbial community profiling. Nat. Rev. Microbiol. 13, 360–372 (2015).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Hanson, C., Fuhrman, J., Horner-Devine, M. & Martiny, J. Beyond biogeographic patterns: processes shaping the microbial landscape. Nat. Rev. Microbiol. 10, 497–506 (2012).CAS 
PubMed 
Article 

Google Scholar 
Dai, A. & Trenberth, K. E. Estimates of freshwater discharge from continents: Latitudinal and seasonal variations. J. Hydrometeorol. 3, 660–687 (2002).ADS 
Article 

Google Scholar 
White, W. R. World water: resources, usage and the role of man-made reservoirs. Report No. FR/R0012. Fundation for Water Research, (2010).Clark, E. A., Sheffield, J., van Vliet, M. T. H., Nijssen, B. & Lettenmaier, D. P. Continental runoff into the oceans (1950–2008). J. Hydrometeorol. 16, 1502–1520 (2015).ADS 
Article 

Google Scholar 
Stevaux, J. C., Paes, R. J., Franco, A. A., Mário, M. L. & Fujita, R. H. Morphodynamics in the confluence of large regulated rivers: The case of Paraná and Paranapanema Rivers. Lat. Am. J. Sedimentol. Basin Anal. 16, 101–109 (2009).
Google Scholar 
Brêda, J. P. L. F. et al. Climate change impacts on South American water balance from a continental-scale hydrological model driven by CMIP5 projections. Clim. Change 159, 503–522 (2020).ADS 
Article 

Google Scholar 
Llames, M. E. & Zagarese, H. E. Lakes and Reservoirs of South America. In Encyclopedia of Inland Waters vol.2 (ed. Linkens, G. E.). (Oxford: Elsevier, 2009).Cabrera, A. L. & Willink, A. Biogeografia De America Latina 2da edn (Organización de los Estados Americanos, 1980).Morrone, J. J. Biogeografía de América Latina y el Caribe 1st edn. (Nature, 2001).Morrone, J. J. Biogeographical regionalisation of the neotropical region. Zootaxa 3782, 1–110 (2014).PubMed 
Article 

Google Scholar 
Antonelli, A. et al. Amazonia is the primary source of Neotropical biodiversity. Proc. Natl. Acad. Sci. USA 115, 6034–6039 (2018).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Sarmento, H. New paradigms in tropical limnology: The importance of the microbial food web. Hydrobiologia 686, 1–14 (2012).Article 

Google Scholar 
Meerhoff, M. et al. Environmental Warming in Shallow Lakes. A Review of Potential Changes in Community Structure as Evidenced from Space-for-Time Substitution Approaches. Adv. Ecol. Res. 46, 259–349 (2012).Article 

Google Scholar 
Herlemann, D. P. R. et al. Transitions in bacterial communities along the 2000 km salinity gradient of the Baltic Sea. ISME J. 5, 1571–1579 (2011).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Metz, S. & Huber, P. et al. A georeferenced rRNA amplicon database of aquatic microbiomes from South America (Dataset), Zenodo, https://doi.org/10.5281/zenodo.6802178 (2022).Callahan, B. J., Sankaran, K., Fukuyama, J. A., McMurdie, P. J. & Holmes, S. P. Bioconductor Workflow for Microbiome Data Analysis: from raw reads to community analyses. F1000 Research 5, 1492 (2016).PubMed 
PubMed Central 
Article 

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

Google Scholar 
Edgar, R. C. UNOISE2: improved error-correction for Illumina 16S and ITS amplicon sequencing. Preprint at https://www.biorxiv.org/content/10.1101/081257v1 (2016).Altschul, S. F., Gish, W., Miller, W., Myers, E. W. & Lipman, D. J. Basic local alignment search tool. J. Mol. Biol. 215, 403–410 (1990).CAS 
PubMed 
Article 

Google Scholar 
Quast, C. et al. The SILVA ribosomal RNA gene database project: Improved data processing and web-based tools. Nucleic Acids Res. 41, D590–D596 (2013).CAS 
PubMed 
Article 

Google Scholar 
Griffith, G. E., Omernik, J. M. & Azevedo, S. H. Ecological classification of the Western Hemisphere http://ecologicalregions.info/htm/ecoregions.htm (1998).Salcedo, J. C. R. South America: Argentina, Bolivia, and Peru https://www.worldwildlife.org/ecoregions/nt1002 Accessed (2018).Vidal, J. Geografía del Perú: las ocho regiones naturales, la regionalización transversal, la microregionalización 9th edn (PEISA, 1987).Paruelo, J. M., Beltran, A., Jobbagy, E., Sala, O. E. & Golluscio, R. A. The climate of Patagonia: General patterns and controls on biotic processes. Ecol. Austral 8, 85–101 (1998).
Google Scholar 
Iriondo, M. Quaternary lakes of Argentina. Palaeogeogr. Palaeoclimatol. Palaeoecol. 70, 81–88 (1989).Article 

Google Scholar 
Soto, D. & Campos, H. in Ecología de los bosques templados de Chile vol. 1 (eds. Khalin, J. M. & Villagrán, C.) (Editorial Universitaria, 1995).Modenutti, B. et al. Structure and dynamic of food webs in Andean North Patagonian freshwater systems: Organic matter, light and nutrient relationships. Ecol. Austral 20, 95–114 (2010).
Google Scholar 
Modenutti, B. E. et al. Structure and dynamics of food webs in Andean lakes. Lakes Reserv. Res. Manag. 3, 179–186 (1998).Article 

Google Scholar 
Quirós, R. & Drago, E. The environmental state of Argentinean lakes: An overview. Lakes Reserv. Res. Manag. 4, 55–64 (1999).Article 

Google Scholar 
Morris, D. P. et al. The attenuation of solar UV radiation in lakes and the role of dissolved organic carbon. Limnol. Oceanogr. 40, 1381–1391 (1995).ADS 
CAS 
Article 

Google Scholar 
Bastidas Navarro, M., Balseiro, E. & Modenutti, B. Bacterial Community Structure in Patagonian Andean Lakes Above and Below Timberline: From Community Composition to Community Function. Microb. Ecol. 68, 528–541 (2014).PubMed 
Article 

Google Scholar 
Modenutti, B. et al. Environmental changes affecting light climate in oligotrophic mountain lakes: The deep chlorophyll maxima as a sensitive variable. Aquat. Sci. 75, 361–371 (2013).CAS 
Article 

Google Scholar 
Bastidas Navarro, M., Martyniuk, N., Balseiro, E. & Modenutti, B. Effect of glacial lake outburst floods on the light climate in an Andean Patagonian lake: implications for planktonic phototrophs. Hydrobiologia 816, 39–48 (2018).CAS 
Article 

Google Scholar 
Sioli, H. Hydrochemistry and Geology in the Brazilian Amazon Region. Amazoniana 1, 267–277 (1968).
Google Scholar 
Fick, S. E. & Hijmans, R. J. WorldClim 2: new 1-km spatial resolution climate surfaces for global land areas. Int. J. Climatol. 37, 4302–4315 (2017).Article 

Google Scholar 
Salati, E. & Vose, P. B. Amazon Basin: A system in equilibrium. Science. 225, 129–138 (1984).ADS 
CAS 
PubMed 
Article 

Google Scholar 
Melack, J. M. & Forsberg, B. R. In The Biogeochemistry of the Amazon Basin Vol. 1 (eds. MacCLain, M. E., Victoria, R. & Richey, J. E.). (Oxford Scholarship Online, 2001).Junk, W. J., Bayley, P. B. & Sparks, R. E. The flood pulse concept in river-floodplain systems. Can. J. Fish. Aquat. Sci. 106, 110–127 (1989).
Google Scholar 
Ratter, J. A., Ribeiro, J. F. & Bridgewater, S. The Brazilian cerrado vegetation and threats to its biodiversity. Ann. Bot. 80, 223–230 (1997).Article 

Google Scholar 
Haridasan, M. Nutritional adaptations of native plants of the cerrado biome in acid soils. Braz. J. Plant Physiol. 20, 183–195 (2008).Article 

Google Scholar 
Vasconcelos, V., de Carvalho Júnior, O. A., de Souza Martins, É. & Couto Júnior, A. F. in World Geomorphological Landscapes. Vol. 1 (eds. Vieira, B., Salgado, A. & Santos, L.) (Springer, 2015).Bichsel, D. et al. Water quality of rural ponds in the extensive agricultural landscape of the Cerrado (Brazil). Limnology 17, 239–246 (2016).CAS 
Article 

Google Scholar 
Cunha, D. G. F., Calijuri, M., do, C. & Lamparelli, M. C. A trophic state index for tropical/subtropical reservoirs (TSItsr). Ecol. Eng. 60, 126–134 (2013).Article 

Google Scholar 
Morellato, L. P. C. & Haddad, C. F. B. Introduction: The Brazilian atlantic forest. Biotropica 32, 786–792 (2000).Article 

Google Scholar 
Galindo-Leal, C. & Câmara, I. de G. The Atlantic Forest of South America: Biodiversity status, threats, and outlook 1st edn (Island Press, 2003).Joly, C. A., Metzger, J. P. & Tabarelli, M. Experiences from the Brazilian Atlantic Forest: Ecological findings and conservation initiatives. New Phytologist 204, 459–473 (2014).PubMed 
Article 

Google Scholar 
Caliman, A. et al. Temporal coherence among tropical coastal lagoons: A search for patterns and mechanisms. Brazilian J. Biol. 70, 803–814 (2010).CAS 
Article 

Google Scholar 
Junger, P. C. et al. Salinity Drives the Virioplankton Abundance but Not Production in Tropical Coastal Lagoons. Microb. Ecol. 75, 52–63 (2018).CAS 
PubMed 
Article 

Google Scholar 
Depetris, P. J., Probst, J. L., Pasquini, A. I. & Gaiero, D. M. The geochemical characteristics of the Paraná River suspended sediment load: An initial assessment. Hydrol. Process. 17, 1267–1277 (2003).ADS 
Article 

Google Scholar 
Orfeo, O. & Stevaux, J. Hydraulic and morphological characteristics of middle and upper reaches of the Paraná River (Argentina and Brazil). Geomorphology 44, 309–322 (2002).ADS 
Article 

Google Scholar 
Neiff, J. J. Large rivers of South America: toward the new approach. Verh. Internat. Verein. Limnol 26, 167–180 (1996).
Google Scholar 
Unrein, F. Changes in phytoplankton community along a transversal section of the Lower Paraná floodplain, Argentina. Hydrobiologia 468, 123–134 (2002).Article 

Google Scholar 
Devercelli, M. Changes in phytoplankton morpho-functional groups induced by extreme hydroclimatic events in the Middle Paraná river (Argentina). Hydrobiologia 639, 5–19 (2010).CAS 
Article 

Google Scholar 
Huber, P. et al. Environmental heterogeneity determines the ecological processes that govern bacterial metacommunity assembly in a floodplain river system. ISME J. 14, 2951–2966 (2020).PubMed 
PubMed Central 
Article 

Google Scholar 
Olson, D. M. et al. Terrestrial ecoregions of the world: A new map of life on Earth. Bioscience 51, 933–938 (2001).Article 

Google Scholar 
Peel, M. C., Finlayson, B. L. & McMahon, T. A. Updated world map of the Köppen-Geiger climate classification. Hydrol. Earth Syst. Sci. 11, 1633–1644 (2007).ADS 
Article 

Google Scholar 
Conde, D., Arocena, R. & Recursos, R.-G. L. acuáticos superficiales de Uruguay: ambientes, algunas problemáticas y desafios para la gestión. Ambios 10, 1–7 (2003).
Google Scholar 
Martin, L. & Suguio, K. Variation of coastal dynamics during the last 7000 years recorded in beach-ridge plains associated with river mouths: example from the central Brazilian coast. Palaeogeogr. Palaeoclimatol. Palaeoecol. 99, 119–140 (1992).Article 

Google Scholar 
Alonso, C. et al. Environmental dynamics as a structuring factor for microbial carbon utilization in a subtropical coastal lagoon. Front. Microbiol. 4, 1664–302X (2013).Article 
CAS 

Google Scholar 
Amaral, V., Graeber, D., Calliari, D. & Alonso, C. Strong linkages between DOM optical properties and main clades of aquatic bacteria. Limnol. Oceanogr. 61, 906–918 (2016).ADS 
Article 

Google Scholar 
Rennella, A. M. M., Quiro, R. & Quirós, R. The effects of hydrology on plankton biomass in shallow lakes of the Pampa Plain. Hydrobiologia 556, 181–191 (2006).Article 

Google Scholar 
Diaz, M., Pedrozo, F. & Baccala, N. Summer classification of Southern Hemisphere temperate lakes (Patagonia, Argentina). Lakes Reserv. Res. Manag. 5, 213–229 (2000).Article 

Google Scholar 
Izaguirre, I. et al. Influence of fish introduction and water level decrease on lakes of the arid Patagonian plateaus with importance for biodiversity conservation. Glob. Ecol. Conserv. 14, e00391 (2018).Article 

Google Scholar 
Porcel, S., Saad, J. F., Sabio y García, C. A. & Izaguirre, I. Microbial planktonic communities in lakes from a Patagonian basaltic plateau: influence of the water level decrease. Aquat. Sci. 81, 51 (2019).Article 
CAS 

Google Scholar 
Bernal, M. C. et al. Spatial variation of picoplankton communities along a cascade reservoir system in Patagonia, Argentina. J. Limnol. 80, 84–99 (2021).
Google Scholar 
Leinonen, R. et al. The European nucleotide archive. Nucleic Acids Res. 39, 44–47 (2011).Article 
CAS 

Google Scholar 
ENA European Nucleotide Archive https://identifiers.org/ena.embl:PRJNA217932 (2013).ENA European Nucleotide Archive https://identifiers.org/ena.embl:PRJNA302313 (2017).ENA European Nucleotide Archive https://identifiers.org/ena.embl:PRJNA294718 (2022).ENA European Nucleotide Archive https://identifiers.org/ena.embl:PRJNA309832 (2016).ENA European Nucleotide Archive https://identifiers.org/ena.embl:PRJNA326475 (2016).ENA European Nucleotide Archive https://identifiers.org/ena.embl:PRJEB48609 (2022).ENA European Nucleotide Archive https://identifiers.org/ena.embl:PRJNA289691 (2015).ENA European Nucleotide Archive https://identifiers.org/ena.embl:PRJNA414894 (2018).ENA European Nucleotide Archive https://identifiers.org/ena.embl:PRJNA323673 (2016).ENA European Nucleotide Archive https://identifiers.org/ena.embl:PRJNA356055 (2017).ENA European Nucleotide Archive https://identifiers.org/ena.embl:PRJNA310230 (2017).ENA European Nucleotide Archive https://identifiers.org/ena.embl:PRJNA390178 (2019).ENA European Nucleotide Archive https://identifiers.org/ena.embl:PRJNA411849 (2017).ENA European Nucleotide Archive https://identifiers.org/ena.embl:PRJNA725228 (2021).ENA European Nucleotide Archive https://identifiers.org/ena.embl:PRJNA292014 (2015).ENA European Nucleotide Archive https://identifiers.org/ena.embl:PRJNA310230 (2017).ENA European Nucleotide Archive https://identifiers.org/ena.embl:PRJNA411849 (2017).ENA European Nucleotide Archive https://identifiers.org/ena.embl:PRJNA316315 (2017).ENA European Nucleotide Archive https://identifiers.org/ena.embl:PRJNA406945 (2017).ENA European Nucleotide Archive https://identifiers.org/ena.embl:PRJNA515842 (2019).ENA European Nucleotide Archive https://identifiers.org/ena.embl:PRJNA310230 (2017).ENA European Nucleotide Archive https://identifiers.org/ena.embl:PRJNA321235 (2016).ENA European Nucleotide Archive https://identifiers.org/ena.embl:SAMN07998328 (2015).ENA European Nucleotide Archive https://identifiers.org/ena.embl:SAMN07998330 (2015).ENA European Nucleotide Archive https://identifiers.org/ena.embl:PRJEB36116 (2020).ENA European Nucleotide Archive https://identifiers.org/ena.embl:PRJEB29989 (2019).ENA European Nucleotide Archive https://identifiers.org/ena.embl:PRJNA788397 (2021).ENA European Nucleotide Archive https://identifiers.org/ena.embl:PRJEB48353 (2022).ENA European Nucleotide Archive https://identifiers.org/ena.embl:PRJEB37379 (2020).ENA European Nucleotide Archive https://identifiers.org/ena.embl:PRJEB46122 (2021).ENA European Nucleotide Archive https://identifiers.org/ena.embl:PRJEB40710 (2020).ENA European Nucleotide Archive https://identifiers.org/ena.embl:PRJEB40864 (2020).ENA European Nucleotide Archive https://identifiers.org/ena.embl:PRJEB40854 (2020).ENA European Nucleotide Archive https://identifiers.org/ena.embl:PRJNA268541 (2015). More

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Chocolate, a serious contender for the world’s most beloved food, is made from the seed kernels of the cacao tree (Theobroma cacao). But despite its popularity, Justine Vansynghel at the University of Würzburg in Germany and her colleagues found that nobody had quantified how species living on small-scale cacao farms collectively affect production1.

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doi: https://doi.org/10.1038/d41586-022-02908-0

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Cliffs present a unique flora that has been little studied until now mainly because of the inherent difficulties to access this unique environment, as shown in Fig. 2. The techniques currently used to access plants on steep slopes and cliffs (e.g., abseiling, helicopter) are generally dangerous, costly and time consuming. Using a small aerial manipulator to sample plants on the cliffs can represent many advantages, including safety and portability, as well as the capability of reaching otherwise inaccessible locations easily, quickly and at low cost.Figure 2Examples of the cliff habitats of some critically endangered species on the Kauaʻi Island along with the count of known individuals as of February 2022.Full size imageHowever, several technical challenges make it difficult to develop suitable aerial manipulators for this task. Indeed, the sampling of plants on cliffs necessarily leads to significant collision risks, as well as contact forces and moments during sampling that can destabilize the drone. The samples collected would also need to be accessed from the side of the aerial platform22. Any weight (e.g., sampling tool, collected samples) located horizontally away from the center of mass of the drone creates large additional demands on the propulsion system of most drones. To collect specific plant parts in windy conditions (e.g., scion, flowers, seeds, etc.), precise and fast motion is required even in degraded Global Navigation Satellite System (GNSS) coverage near the cliffs. The great diversity of plant species and morphology found on cliffs, as well as the variety of targeted sections of plant, also represent a major design challenge. Finally, to maximize the adoption of this tool, it is also desirable that scientists with minimal training could use this platform. The next sections describe how these challenges were addressed through the development of the Mamba.Suspended sampling platformThere are a multitude of configurations that could have been explored to sample plants on cliffs. Some drones have manipulators rigidly attached to their structure20,23. However, these manipulators tend to have a limited reach to keep the center of mass within the propeller footprint and to minimize the inertia of the system. This could result in a high collision risk with the propellers in the uneven terrain found on cliffs. The contact forces created during the sampling operation also generate destabilizing moments through manipulators rigidly attached to the drone. To address these challenges, concepts involving a compliant manipulator operated from specialized drones were also explored10. Alternatively, some aerial manipulators were also passively suspended under the drone through a long rod21,24. This keeps the drone above potential obstacles within the environment, significantly reducing the operator’s mental demand and stress while also reducing the disturbances transmitted to the drone to a downward force aligned with the rod and yaw torque. To maintain these advantages while providing better precision, some projects have developed cable suspended platforms equipped with thrusters25,26. As these platforms do not have to counter gravity, the thrusters can be positioned to fight external disturbances more efficiently (e.g., wind, contact forces, drone movements). Existing systems however only stabilize the suspended platform close to its equilibrium point.The chosen concept for the Mamba, illustrated at Fig. 3, consists of a suspended platform that can stabilize itself far from its natural equilibrium to provide a large workspace. The lifting drone in this system stays safely away and above from steep cliff faces, while supporting the platform and providing rough positioning in space through better GNSS coverage. The platform is suspended 10 m below the lifting drone using four attachment points to prevent pitch and roll motions. The cable also acts as a low pass filter, isolating the platform from the fast drone movements required to fight wind disturbances. The suspended platform design can then focus on fast and precise positioning, while also being tolerant to contacts during sampling. To do so, four pairs of bidirectional actuators are used to control the motion in the plane of the pendulum (i.e., x and y translation, as well as yaw). Two pairs of actuators are installed in the x-direction to provide sufficient force to reach plants as far as 4 m from the equilibrium position. This corresponds to roughly 3.3 m from the tip of the lifting drone’s propellers.Figure 3(a) General concept of the Mamba and lifting drone during transit and sampling on cliffs. (b) Side view of the Mamba showing the components and cable installations. (c) Top view showing the antagonist thrusters configuration. (d) Close-up of the sampling tool and 2 degrees of freedom (DOF) wrist specifically designed to sample small fragile plants.Full size imageSince the Mamba is self-powered and has its own communication system, the lifting drone function is simply to lift the platform and hold it in place. This made it possible to select amongst the many commercially available products to accelerate the development of the Mamba. The DJI M300 was chosen as it comes equipped with a 360° optical obstacle avoidance vision system, an IP45 rating, and a flight time of 20 min with the Mamba attached (3.3 kg). It also advertised a four constellation GNSS receiver for better coverage around buildings, structures, and cliffs.Precise control in windsWinds under 20 km/h represent a gentle breeze on the Beaufort scale. At this level, the wind only moves the leaves, and not the branches, which allows for ideal sampling conditions. According to historical weather data from 2020, daily maximum winds are less than 20 km/h for 40 to 70% of the year, depending on the exact location on Kauaʻi Island (i.e., Lihuʻe International airport, as reported by the National Oceanic and Atmospheric Administration, and the Makaha Ridge Weather Station, as reported in the MesoWest database). This also implies that Kauaʻi experiences stronger winds on certain days which would make precise sampling difficult. Wind conditions are also more challenging near cliff faces, with increased turbulence and vertical airflow along the cliff.To allow operations on most days, while providing precise positioning and fast rejection of wind disturbances, the actuators of the Mamba are oriented in the horizontal plane. This allows the actuator forces to directly affect the motion of the suspended platform. Each actuator of the Mamba consists of a pair of brushless DC motors and 23 cm propellers capable of producing 7 N of force. The motors are installed in opposite directions, are always idling at their minimum rotation speed, and are commanded to only create force in their preferred direction. This antagonistic configuration avoids the low-velocity dead zone of a brushless motor during thrust reversal. This makes it possible to quickly revert the direction of the thrust and nearly triples the bandwidth of the actuators to approximately 2.5 Hz27. This configuration, however, comes at the expense of added mass and components.The Mamba is equipped with a flight controller that includes a control system, and a state estimator. To avoid degraded GNSS coverage issues, the state estimator only uses data from a high accuracy inertial measurement unit (IMU) to estimate the attitude of the platform. This provides the relative position of the platform with respect to the drone and is sufficient for teleoperation. Three separated proportional-derivative controllers are used for each of the DOF controlled by the actuators. This control system also provides attitude-hold assistance (i.e., pitch and roll, which correspond to x and y displacements, as well as yaw). This implies that if the user does not send any commands, the suspended platform maintains its current state.Figure 4 illustrates the stabilization accuracy of the Mamba when moving along a representative trajectory when suspended indoors from a 5.7 m cable (limited by ceiling height). This experiment confirmed that the sampling tool can maintain a position at a horizontal reach of 2.25 m with a precision of about 5 cm for 30 s. As the horizontal reach and precision are limited by the cable angular displacements (e.g., component of weight acting on the pendulum, IMU angular resolution), the resulting workspace when operating with a 10 m long cable would reach a radius of 4 m with a positioning accuracy of about 9 cm. To account for potential external disturbances like wind, the sampling tool was designed with an opening of 15 cm. This creates some margin for the pilot to align the target with the sampling mechanism. Field trials detailed below demonstrated that the Mamba actuators and controller could maintain a sufficiently stable position to sample plants in winds During the sampling phase, wind speed averaged 15.7 km/h with a standard deviation of 6.8 km/h, while wind gusts reached an average of 20.1 km/h with a standard deviation of 6.5 km/h. The maximum average wind speed recorded during sampling was 28 km/h with gusts up to 37 km/h. This represents a lower bound of the system performance, as no failure resulted from the wind conditions experienced during the trials. The a ttached Supplementary Video also demonstrates the stability of the system.Figure 4Representative motion of the sampling tool within its workspace based only on feedback from a high accuracy IMU and recorded using a motion capture system. The natural equilibrium point is at (0,0). The experiment starts with a 90° rotation around the z axis, followed by a forward movement along the x-axis of the Mamba and a lateral movement along its y-axis. The system then maintains this position for 30 s without any user inputs. Produced in MATLAB R2021a.Full size imageTeleoperated sampling of cliffs habitatsPlants growing on Kauaʻi cliffs exhibit a wide morphological variety. For this project, targets ranged from small herbaceous plants such as Euphorbia eleanoriae (plants  More

Historical land use and cover changesExisting databases differed significantly in representing historical LUCC in China (Fig. 1). Generally, datasets agree on the direction of change in cropland area until 1980 in Liu and Tian18, Ramankutty19, Houghton20, and this study (Fig. 1b, c), while the magnitude of change varied greatly. Specifically, the total cropland expansion in China was comparable between our new data set and the LUH2-GCB from 1900 onwards (56 vs 60 Mha, Fig. 1b), but cropland area changes since 1980 diverged considerably (−14 vs 41 Mha, Fig. 1c). The differences were also evident across space and more distinct during the period of 1980 to 2019 (Fig. 2a–d), in which the cropland coverage was mainly declining in our reconstructed data but increasing in LUH2-GCB (Fig. 2b, d). We found that the distinct changes are derived from the abrupt cropland increases in the FAO data reported from China, upon which LUH2-GCB was based (see Supplementary Information 3).Fig. 1: Temporal, net changes of cropland and forest from 1900 (unit: Mha).Panel a–c: cropland; panel d–f: forest; the bar charts indicate the total accumulated areas (b, e) from 1900 and (c, f) from 1980 until the last available year; LUH2-GCB was the latest version of LUH2 data used in Global Carbon Budget assessments projects (LUH2 used in MsTMIP and TRENDY were showed in Supplementary Figs. S7 and S10); Houghton data were derived from Houghton and Nassikas20 and the data in 1900 were interpolated from 1850 and 1950; Liu&Tian and Ramankutty data were derived from the works of Liu and Tian16 and Ramankutty and Foley18; the open circles indicate the changes of cropland and forest areas derived from inventory-based benchmark data; details of the benchmark data for cropland and forest were presented in Yu et al.11 and Supplementary Information 1.2 of this study, respectively; error bars: one standard deviation from the mean.Full size imageFig. 2: Spatial distribution of the fractional coverage changes of cropland and forest in China (unit: %).Panels a–d: cropland; panel e–h: forest; panels a, b, e, and f indicate the results derived from this study; data in panels c, d, g, and h were from LUH2-GCB; panels a, c, e, and g show the changes from 1900 to 1980, whereas panels b, d, f, and h show the changes from 1980 to 2019; negative and positive values indicate coverage reduction and increment, respectively.Full size imageThe problems of cropland area expansion reported to FAO are likely caused by changes in the underlying database, in which the Chinese Agricultural Yearbook (CAY) was used prior to 1996, the China Land and Resources Statistical Yearbook (LRSY) from 1996 to 2007, and the National Land and Resources Bulletin (NLRB) after 2007 (Supplementary Information 3).These three datasets are not consistent with each other because surveying methods were distinct. For example, cropland area in CAY before 1982 used an extrapolation method (i.e. “production-to-acreage” approach) due to limited field survey data11. Specifically, the extrapolation method was widely adopted for convenience and for taxation purposes in the early period, such as in the framework of the first benchmark cropland survey conducted in 1953. Such methods assumed that low-productivity cropland occupied an area of 1/3–1/8 of a predetermined, “standard-productivity” cropland21, which greatly underestimates the acreages of low productivity cropland. Biases accumulated in this method persisted until the satellite era (1980s), while the 1953 surveying data were used as the baseline for CAY to update cropland area on an annual basis.Besides the survey method, policies also contributed to a bias of reported cropland area. To tackle rising food demands, cropland expansion was highly encouraged by the government before the 1980s, implementing an incentive policy to allow new tax-free cropland without reporting to the government for the first 3–5 years22,23. Even after the initial reporting free period, these newly cultivated croplands continued to be unreported due to political incentives to show increasing crop yield to the local authorities23,24.When the first comprehensive and systematic survey (i.e. the second national cropland survey conducted during 1985–1996) was completed, the cropland area was found to be larger than previously reported in CAY11. Similarly, the shift from the use of LRSY to NLRB also introduced a spurious cropland area increment from 2007 to 2010 as small, fragmented croplands were identified by better technologies adopted in NLRB, which had remained undetected previously (Supplementary Fig. S10).Thus, LUH2-GCB has inherited spurious temporal signals of abrupt cropland increment in FAO from the 1980s to 2010 (Fig. 1a and Supplementary Fig. S10). Therefore, if the areas of other land cover types (e.g. forest) are indirectly constrained from cropland area change, cropland area biases were mirrored in the area change of other land use types. This is the case for the LUH2-GCB and for Liu and Tian’s previous land use gridded datasets. Our new database, rebuilt from Yu et al.11, corrected these problems in temporal dynamics by assimilating multiple data sources (Fig. 1a). More specifically, we retrospectively reconstructed information about cropland and forest areas year by year, using tabular data from official agencies (Supplementary Information 1 and Supplementary Data 1). To further reduce the aforementioned biases, we used the most recent and authoritative record of provincial cropland and forest areas available as the benchmark, and then spatialized the cropland and forest distributions using gridded maps as ancillary data (Supplementary Information 1). The area changes were also validated using inventory-based benchmark data (Fig. 1a, d, details were presented in Yu et al.11 and Supplementary Information 1.2).Changes in forest area in China also varied dramatically among databases. Based on Ramankutty and Foley19 and LUH2-GCB, a net forest loss was found from 1900 to the last available year, at 33–108 Mha whereas Liu and Tian18 and Houghton and Nassikas20 reported a net increase of 15 Mha (1900–2005) and 70 Mha (1900–2015) in forest area, respectively (Fig. 1d, e).By assimilating multiple source records, reports, and national surveys, however, our newly reconstructed and intensively validated database (Supplementary Figs. S4, S5, and S8) with corrected biases suggests that the forest area increased by 58 Mha from 1900 to 2019 (Fig. 1e). In particular, our data suggest that there is a surprisingly large underestimation of forest expansion in all other databases (38–102 Mha) after 1980 (Fig. 1f). We performed spatial analyses and show that widespread forest expansion in our reconstructed data was represented as a forest decline in LUH2-GCB during the period 1980–2019 (Fig. 2f, h). These existing biases in the dataset during the last four decades can be simply removed using recently available and spatially explicit forest products (Supplementary Table S2).Bias in forest change might be explained by two reasons. First, gridded datasets inherited and transferred errors from the use of FAO-based cropland dataset in developing global land use databases such as HYDE and thus LUH2-GCB8. Second, the FAO forest area reported is an important reference data used in these databases. The FAO forest area is reported based on a “land use” definition, which underestimated gross “land cover” change signals between reported years (Supplementary Information 1.3). Specifically, the FAO forest area describes lands that have been forested and will continue to be used for forestry (e.g. cut-over area, fired-over area, unestablished afforestation land) (Supplementary Table S5). This approach overestimates forest area by including lands used for reforestation where no forest was yet created. Thus, for example, the FAO statistics reported a 157.2 Mha forest area in 1990 (Supplementary Fig. S7), which is ~30 Mha higher than officially released data.More importantly, newly established forests were underestimated in such an accounting approach. The forest area expansion in China reported in the FAO statistics was 61 Mha from 1990 to 2019, which is 30 Mha lower than the officially released data16. Our reconstructed dataset, in agreement with officially released forest area, uses a “land cover” definition that characterizes the distribution of annually established forests. Therefore, the FAO statistics – a data set with definition specified to describe the area of land use – should be used with caution for constraining the temporal evolution of forest cover distribution in gridded data reconstruction, and the modeling community should be alerted to treat the LUCC data appropriately.Nonetheless, the FAO and the related LUH2 products were the dominant LUCC forcing data used in multiple studies3,25, including various process-model-based intercomparison projects (e.g. MsTMIP, LUMIP, NMIP, TRENDY), annually released Global Carbon Budget reports2,26, and IPCC reports5, implying a potential bias of these assessments for the China region. In contrast, changes in forest area from our database were independently developed (Supplementary Information 1.2), intensively calibrated, and validated using officially released national forest inventories (NFIs, see Supplementary Figs. S4 and S5), which can help to reduce the potential bias of C balance assessment in China. More specifically, the total forest area and PF area in our database were compared with historical NFIs released by the National Forestry and Grassland Administration at provincial level since 1949 (Supplementary Figs. S4 and S5), which supports the reliability of our reconstructed data.Historical carbon stock changesTo illustrate the bias in the C balance of China when using previous LUCC dataset, we performed simulations with the DLEM model for the period 1900–2019 at a resolution of 0.5 × 0.5 degree forced by our new LUCC dataset. We validated the distribution and changes of C stock using published studies and previously reported inventory-based estimations (Supplementary Information 6 and 7). The model could capture well C dynamics in China using inventory-based forest C stock changes at both provincial and national levels as the validation data set (Supplementary Fig. S14).Our results show that the total C stock decreased by 6.9 ± 0.6 Pg from 1900 to 1980 and increased by 8.9 ± 0.8 Pg C from 1980 to 2019 (Fig. 3, derived from experiment S1 in Supplementary Table S10). Such a large C stock increment since the 1980s, which is dominated by vegetation biomass C accumulation, was not captured in the MsTMIP and TRENDY projects driven by different versions of the LUH2 data (Fig. 3). This is attributed to the fast expansion of forest area(s) that was not captured by this land use forcing (Fig. 1).Fig. 3: Temporal changes of carbon storage from 1900 to 2010s in China.Panel a–c indicate vegetation carbon, soil organic carbon, and total ecosystem carbon, respectively. Results derived from experiment designed to have all environmental factors vary historically from 1900 to the 2010s, for model design details of this study see Supplementary Information 8); pink color: MsTMIP (1900–2010); blue color: TRENDY (1900–2019); dark color: this study (1900–2019); the shade areas represent the ranges of 1 standard deviation; unit: Pg C.Full size imageWe found that the large-scale forest expansion in China alone has caused a substantial C accumulation since 1980 (0.21 ± 0.006 Pg C per year, Table 1). In contrast, the forest C sink of the TRENDY models is negligible (−0.02 ± 0.05 Pg C per year, Table 1). A moderate C source (0.10 ± 0.08 Pg C per year, Table 1) was even found in the MsTMIP models, since these models were driven by continuous forest area loss and cropland expansion since the 1980s (Supplementary Fig. S7).Table 1 Comparison of reported carbon fluxes from various biomes in ChinaFull size tableA recent atmospheric inversion-based study reported that China’s land ecosystems were a large CO2 sink of −1.11 ± 0.38 Pg C per year27, which seems to be ecologically implausible and critically sensitive to the assimilation of the CO2 record from one station28. The compilation of previous studies from inventory- and satellite-based estimation, atmospheric inversion, and process-based models suggested that the Chinese C sink was much smaller (−0.18– −0.45 Pg C per year; Table 1). Our model-simulated terrestrial sink (~−0.28 ± 0.06 Pg C per year) was in this range (Table 1).While our simulated C balance in different categories or biomes is close to previous estimations, three major differences are observed (Table 1). First, because the LUCC data used in previous global models suffered from biases as shown above, the national C sink was generally underestimated in these simulations (Table 1). Second, our estimation of the forest sink is around two to three times larger than the previous one during 1949–199829. This was mainly because forest area was underestimated by over 33% (53 Mha) in the previous study29 compared to the national forest inventory (NFI)16. This underestimation may stem from exclusion of economic and bamboo forests. The third major difference is the role of grassland soils in C balance during the period 1980–2000. China’s grassland soils were previously reported as a minor sink of −0.007–−0.022 Pg C per year from the 1980s to the 2000s (Table 1), while our simulations suggest that grassland soils were a C source of 0.062–0.066 Pg C per year. This discrepancy lies in the approaches used and the accounting boundaries between studies (i.e. whether the transitions of grassland were considered), in which LUCC impacts were represented differently. For example, impervious surfaces (part of urbanized area) expanded into ~15 Mha of natural lands in China from 1978 to 201730, which further drove redistribution of cropland into marginal lands with the majority converted from grassland, causing wind erosion, habitat loss, and more water and fertilizer consumption31. Earlier studies using a static grassland map exclude the C stock loss in the land-use transition32. Thus, the distinct roles of grassland soils (i.e. sink vs source) derived from our simulations and earlier studies are not contradictory but are due to differences in accounting boundaries.LUCC impacts on carbon stock changesOur DLEM simulation indicates that LUCC induced a C loss of 5.1 ± 0.7 Pg C from 1900 to 2010s (Fig. 4a), which is substantially lower than that from MsTMIP (13.8 ± 7.7 Pg C, 1900–2010) and TRENDY (9.4 ± 3.3 Pg C, 1900–2019; Fig. 4e, f and Supplementary Fig. S18d, g). From 1980 onward, LUCC increased C storage by 4.3 ± 0.7 Pg C, with the major contribution from vegetation biomass C increment in the southwestern and northeastern regions (Fig. 4d and Supplementary Fig. S19a). Nonetheless, this C increase in biomass was not captured in MsTMIP and TRENDY models (Fig. 4e, f and Supplementary Fig. S19d, g), which simulated that LUCC continued to reduce C stock by 7.5 ± 1.6 and 5.3 ± 2.3 Pg C during the period 1980 to the 2010s, respectively (Fig. 4 and Supplementary Fig. S20).Fig. 4: Spatial distribution of LUCC impacts on ecosystem carbon storage.Panel a–c: LUCC impacts for period of 1900–2019; panel d–f: LUCC impacts for period of 1980–2019 (d–f). Panels a and d are from this study; data in panels b and e are from MsTMIP; data in panels c and f are from TRENDY; negative and positive values indicate sink and source, respectively; green and yellow bar stacked in the insert indicate LUCC impacts on vegetation and soil organic carbon in Pg C; spatial map unit: g C m−2; error bars: one standard deviation from the mean of LUCC impacts on total carbon storage.Full size imageTo confirm that such discrepancy was induced by LUCC data but not the DLEM model, we set up additional DLEM simulations using the LUH2-GCB database (Supplementary Information 8). The simulated C losses induced by LUCC when DLEM was driven with LUH2-GCB were 6.5 ± 0.4 and 11.4 ± 0.6 Pg C during the periods of 1980–2019 and 1900–2019, which are close to MsTMIP and TRENDY simulations. These results confirm that the LUCC forcing database is the major contributor to the difference between our simulations and the MsTMIP and TRENDY projects. An earlier study reported that global LUCC-induced C emissions are substantially underestimated due to underrepresented tree harvesting and land clearing from shifting cultivation33. Our simulation revealed that regional LUCC-induced C emission could also be overestimated in China due to a bias in the LUCC data.There are also disputes over whether the LUCC induced a C sink in China since the 1990s or not (Supplementary Table S8). By using an updated LUCC database, our simulations revealed that LUCC was a strong C sink in China, and that its magnitude was larger than previous estimates since the 1990s (Supplementary Table S8). Our results using an improved LUCC forcing data can facilitate narrowing down the well-known, large uncertainty in LUCC-induced C change at regional scale.Attributions of different factors on C stock changes since 1980By using the DLEM model with factorial simulations (see Supplementary Information 8 for details), we examined the direct and interactive contributions of different drivers to terrestrial C stock change in China for the period 1980–2019, including LUCC, climate, forest management, N deposition, and CO2 fertilization (see Methods, Fig. 5). Note that historical C stock change is not equivalent to the sum of factorial attributions as the baseline conditions differ (see Supplementary Information 8).Fig. 5: Attributions of different environmental factors on carbon stock change in China from 1980 to 2019.Panels a–c indicate attributions of impacts on the changes of vegetation carbon, soil organic carbon, and total ecosystem carbon, respectively; CLM: climate; CO2: rising atmospheric CO2 concentration; Ndep: N deposition; Man: forest management; Nfer: N fertilizer and manure application.Full size imageOverall, 81.9% (6.5 Pg C) of the terrestrial C sink during this period was attributed to direct impacts of all major factors, while the interactive effect contributed 18.1% (1.43 Pg C; Fig. 5c). Among all the factors examined, LUCC was the dominant driver accounting for 50.3% (3.96 Pg C) of the total C increment during the period 1980–2019 (Fig. 5c), which was largely attributed to biomass C accumulation (70.0%; Fig. 5a, c). Tian et al.13 reported that LUCC’s contribution to the sink in China was at 0.05 Pg C yr−1 since the 1980s – an amount that is only about 30% of our simulations. The discrepancy is attributed to the different representation of forest expansion in model simulations, which was 65 Mha from 1980 to 2005 in our database but only ~14 Mha in Tian et al.13. Similarly, the increase in the global land sink during the recent period (1998–2012) was also mainly attributed to LUCC (i.e. decreased tropical forest area loss and increased afforestation in northern temperate regions), instead of CO2 or climate change34.Climate change enhanced biomass C stocks by 1.63 Pg but caused a soil C loss of 0.30 Pg, thus contributing to land sink of 1.41 Pg C (18.0% of the total with all factors) since 1980 (Fig. 5). Other global change factors, such as N fertilizer application, atmospheric N deposition, and rising CO2, had a relatively minor contribution (0.1–9.54%) to the terrestrial C sink. Therefore, conversely to previous studies13,35,36,37, we showed that LUCC was the dominant driver of the recent land C sink in China, and other factors including climate change, rising CO2, and N deposition, contributed much less (0.1–18.0%) to the C stock increment in China (Fig. 5c). Tian et al.13 pointed out that LUCC effects in China should not be ignored and that the CO2 fertilization effect might be overestimated in Piao et al.38.Our simulations confirm these statements, and further show that LUCC was actually the largest contributor to land sink in China since 1980 (Fig. 5). In those studies which did not account for the influence of LUCC separately, the effects of other global change factors may have been overestimated by including LUCC impacts. For example, Chen et al.39 and He et al.37 attributed China’s C sink into different components including climate change, leaf area index (LAI) change, rising CO2, and N deposition. Such partition inevitably masked the separate contribution from LUCC, because LAI changes are closely related to land-cover changes. Thus, the accurate representation of the LUCC should be prioritized in future modeling attribution studies.Carbon stock changes in each land cover type since 1980The contribution of the establishment of young and new forest plantations to C sink has received increasing attention3,40,41,42. Our simulation (experiment S1, see Methods section) revealed that the increase in terrestrial C stock was dominantly contributed by biomass C accumulation (76.3%) (Fig. 5), in which the natural and planted forests accounted for 65% (2.9 Pg C) and 35% (1.6 Pg C) during the last four decades. We examined the LUCC effect (i.e. the largest contributor of C stock increment in Fig. 5) on the C stock of different biomes and confirmed that forest was the major contributor of the net C accumulation in China since 1980, while other biomes, including cropland, grassland, shrubland, and wetland, were relatively stable, varying from −0.3 to 0.3 Pg C during the same period (Fig. 6). A recent study documented that forest expansion was essential for a large C sink in southern China during 2002–2017, where newly-established and existing forests contributed to 32% and 34% of land C sink in the region43. In comparison to the large biomass C increase since 1980 (3.0 Pg C, Fig. 6a), the SOC increase was much lower (0.7 Pg C) during the concurrent period, although SOC changes in each biome varied greatly (–3.4–8.6 Pg C; Fig. 6b) due to area change from land conversions. The biome-level analyses further revealed that the LUCC-induced C stock increment was dominantly contributed from forest and by area expansion, while C storage in grassland and shrubland was reduced by LUCC (Fig. 6).Fig. 6: LUCC-induced carbon storage changes by land cover types based on model simulations during 1980–2019.Panel a–c indicate vegetation carbon, soil organic carbon, and total ecosystem carbon, respectively; the widths of the red blocks indicate the estimation ranges of net changes in model simulations; purple error bars indicate one standard deviation of multiple model runs; negative and positive changes indicate carbon loss and gain, respectively.Full size imageThis study highlights the dominant role of LUCC in determining the terrestrial C sink in China. Because of inaccurate representations of land cover change in China, previous estimates of the terrestrial C sink have been strongly underestimated. In contrast, forest expansion and cropland abandonment have been overestimated in the U.S., resulting in an underestimated C emission since 19807. Hence, we highlighted that the global LUCC database should be further improved, which could potentially narrow down the C imbalance reported in global C budget accounting2. In contrast to the previous studies, we showed that the contributions of factors including rising CO2, N deposition, and climate change to the land C sink in China were much smaller than LUCC over the past four decades (1980-present time). Thus, reforestation projects could represent important climate change mitigation pathways, with co-benefits for biodiversity33. To achieve the ‘C neutrality’ goal as the Chinese government declared, future climate policy should be directed to improve land management, especially forest ecosystems.Implications for future LUCC data improvementsThis study provides a novel reconstruction of recent land use change in China and assesses its implications in quantifying for terrestrial C storage dynamics. The improved dataset more accurately depicts the spatiotemporal dynamics of LUCC in China because the historically contradictory surveying records were identified, which helped to correct the biased temporal signals. Specifically, the improved surveying methods and the socioeconomic factors have greatly shaped the LUCC signals. We advocate that these impacts should be considered in the reconstruction of the national and global LUCC dataset, especially in the areas that have been intensively disturbed by human activities as is the case of China. These endeavours will be worthwhile, as demonstrated by the large impact that these bias corrections have on China’s C dynamic assessments since 1900. Thus, accurate delineation of LUCC forcing should be stressed in global simulations, including C budget accounting, biodiversity assessments, and ecosystem services evaluations. More

BirdLife International. Red List Update: Parrots of the Americas in Peril. https://www.birdlife.org/news/2021/02/08/red-list-update-parrots-of-the-americas-in-peril/ (2020).Berkunsky, I. et al. Current threats faced by Neotropical parrot populations. Biol. Cons. 214, 278–287. https://doi.org/10.1016/j.biocon.2017.08.016 (2017).Article 

Google Scholar 
ICMBIO—Instituto Chico Mendes de Conservação da Biodiversidade (Org.). Livro Vermelho da Fauna Brasileira Ameaçada de Extinção: Volume III-Aves 709. https://www.icmbio.gov.br/portal/images/stories/comunicacao/publicacoes/publicacoes-diversas/livro_vermelho_2018_vol3.pdf (Ministério do Meio Ambiente, 2018).CBRO—Comitê Brasileiro de Registros Ornitológicos. Listas das Aves do Brasil. 11th ed. http://www.cbro.org.br/wp-content/uploads/2020/06/avesbrasil_2014jan1.pdf (CBRO, 2014).Pacheco, J. F. et al. Annotated checklist of the birds of Brazil by the Brazilian Ornithological Records Committee—second edition. Ornithol. Res. 29(2), 94–105. https://doi.org/10.1007/s43388-021-00058-x (2021).Article 

Google Scholar 
IUCN—International Union for Conservation of Nature. The IUCN Red List of Threatened Species www.iucnredlist.org (2018).Guedes, N. M. R. Biologia reprodutiva da arara azul (Anodorhynchus hyacinthinus) no Pantanal—MS, Brasil. (Dissertação de Mestrado Universidade de São Paulo, São Paulo (1993).Guedes, N. M. R. et al. Technical Report Assessing the Impact of Fire on Blue Macaws, Pantanal, Mato Grosso do Sul, Brazil, p 13, Campo Grande, Instituto Arara Azul (2019).Guedes, N. M. R. Araras azuis: 15 anos de estudos no Pantanal. In Paper presented at IV Simpósio Sobre Recursos Naturais e Sócio-Econômicos do Pantanal, Corumbá: Embrapa Pantanal (2004).Guedes, N. M. R. Sucesso reprodutivo, mortalidade e crescimento de filhotes de araras azuis Anodorhynchus hyacinthinus (Aves, Psittacidae), no Pantanal, Brasil (Tese de doutorado Universidade Estadual Paulista, Botucatu, 2009)Guedes, N. M. R. & Harper, L. H. Hyacinth macaws in the Pantanal. In The Large Macaws (eds Abramson, J. et al.) 394–421 (Raintree Publications, 1995).
Google Scholar 
Vicente, E. C. & Guedes, N. M. Organophosphate poisoning of Hyacinth Macaws in the Southern Pantanal, Brazil. Sci. Rep. 11, 1–6. https://doi.org/10.1038/s41598-021-84228-3 (2021).CAS 
Article 

Google Scholar 
Guedes, N. M. R. et al. Assessment of fire impact on Hyacinth Macaws in Perigara, Pantanal—MT, Brazil, p 35, Campo Grande, Instituto Arara Azul (2020).Guedes, N. M. R. et al. Macaws survive fires and provide hope for resilience—Stubborn survivors. Pantanal Sci. Mag. 6, 36–41 (2021).
Google Scholar 
Oliveira, M. D. R. et al. Lack of protected areas and future habitat loss threaten the Hyacinth Macaw Anodorhynchus hyacinthinus and its main food and nesting resources. Ibis 163, 1217–1234 (2021).Article 

Google Scholar 
Ricklefs, R. E. Patterns of growth in birds. Ibis 110, 419–451. https://doi.org/10.1111/j.1474-919X.1968.tb00058.x (1968).Article 

Google Scholar 
Gebhardt-Henrich, S. & Richner, H. Causes of growth variation and its consequences for fitness. Oxford Ornithol. Ser. 8, 324–339 (1998).
Google Scholar 
Masello, J. F. & Quillfeldt, P. Body size, body condition and ornamental feathers of Burrowing Parrots: Variation between years and sexes, assortative mating and influences on breeding success. Emu Austral Ornithol. 103, 149–161. https://doi.org/10.1071/MU02036 (2003).Article 

Google Scholar 
Renton, K. Influence of environmental variability on the growth of Lilac-crowned Parrot nestlings. Ibis 144, 331–339. https://doi.org/10.1046/j.1474-919X.2002.00015.x (2002).Article 

Google Scholar 
Masello, J. F. & Quillfeldt, P. Chick growth and breeding success of the Burrowing Parrot. Condor 104, 574–586. https://doi.org/10.1650/0010-5422 (2002).Article 

Google Scholar 
Pacheco, M. A., Beissinger, S. R. & Bosque, C. Why grow slowly in a dangerous place? Postnatal growth, thermoregulation, and energetics of nestling green-rumped parrotlets (Forpus passerinus). Auk 127, 558–570. https://doi.org/10.1525/auk.2009.09190 (2010).Article 

Google Scholar 
Vigo, G., Williams, M. & Brightsmith, D. J. Growth of Scarlet Macaw (Ara macao) chicks in southeastern Peru. Neotrop. Ornithol. 22, 143–153 (2011).
Google Scholar 
Lyon, J. P. et al. Reintroduction success of threatened Australian trout cod (Maccullochella macquariensis) based on growth and reproduction. Mar. Freshw. Res. 63, 598–605. https://doi.org/10.1071/MF12034 (2012).Article 

Google Scholar 
Vigo-Trauco, G., Garcia-Anleu, R. & Brightsmith, D. J. Increasing survival of wild macaw chicks using foster parents and supplemental feeding. Diversity 13, 121. https://doi.org/10.3390/d13030121 (2021).Article 

Google Scholar 
Tellería, J. L., De La Hera, I. & Perez-Tris, J. Morphological variation as a tool for monitoring bird populations: A review. Ardeola 60, 191–224. https://doi.org/10.13157/arla.60.2.2013.191 (2013).Article 

Google Scholar 
Silva, J. S. V. Elementos fisiográficos para delimitação do ecossistema Pantanal: Discussão e proposta. Oecol. Brasil. 1, 349–458. https://doi.org/10.4257/OECO.1995.0101.22 (1995).Article 

Google Scholar 
Silva, J. S. V. & Abdon, M. M. Delimitação do Pantanal Brasileiro e suas Sub-Regiões. Pesq. Agropec. Bras. 33, 1703–1711 (1998).
Google Scholar 
Keuroghlian, A., Eaton, D. & Desbiez, A. L. J. The response of a landscape species, white-lipped peccaries, to seasonal resource fluctuations in a tropical wetland, the Brazilian Pantanal. Int. J. Biodivers. Conserv. 1, 87–97 (2009).
Google Scholar 
Donatelli, R. J., Posso, S. R. & Toledo, M. C. B. D. Distribution, composition and seasonality of aquatic birds in the Nhecolândia sub-region of South Pantanal, Brazil. Braz. J. Biol. 74, 844–853 (2014).CAS 
Article 

Google Scholar 
Donatelli, R. J. et al. Temporal and spatial variation of richness and abundance of the community of birds in the Pantanal wetlands of Nhecolândia (Mato Grosso do Sul, Brazil). Rev. Biol. Trop. 65, 1358–1380 (2017).Article 

Google Scholar 
Tomas, W. M. et al. Sustainability agenda for the Pantanal Wetland: Perspectives on a collaborative interface for science, policy, and decision-making. Trop. Conserv. Sci. 12, 1–30. https://doi.org/10.1177/1940082919872634 (2019).ADS 
Article 

Google Scholar 
Harris, M. B. et al. Safeguarding the Pantanal wetlands: Threats and conservation initiatives. Conserv. Biol. 19, 714–720. https://doi.org/10.1111/j.1523-1739.2005.00708.x (2005).Article 

Google Scholar 
Santos Júnior, A. D., Aspectos populacionais de Sterculia apetala (Jacq.) Karst (Sterculiaceae) como subsídios ao plano de conservação da arara-azul no Sul do Pantanal, Mato Grosso do Sul, Brasil. (2006). https://repositorio.ufms.br/handle/123456789/521.Ricklefs, R. E. The optimization of growth rate in altricial birds. Ecology 65, 1602–1616 (1984).Article 

Google Scholar 
Bruford, M. W., Hanotte, O., Brookfield, J. F. Y. & Burke, T. Single-locus and multilocus DNA fingerprinting. In Molecular Genetic Analysis of Populations: A Practical Approach (ed. Hoelzel, A. R.) 225–269 (Oxford University Press, 1992).
Google Scholar 
Miyaki, C. Y. et al. Sex identification of parrots, toucans, and curassows by PCR: Perspectives for wild and captive population studies. Zoo Biol. 17(5), 415–423 (1998).Article 

Google Scholar 
Cavanaugh, J. E. & Neath, A. A. The Akaike information criterion: Background, derivation, properties, application, interpretation, and refinements. Wiley Interdiscip. Rev. Comput. Stat. 11, 1460. https://doi.org/10.1002/wics.1460 (2019).MathSciNet 
Article 

Google Scholar 
Motulsky H. J. GraphPad curve fitting guide. 2021. http://www.graphpad.com/guides/prism/7/curve-fitting/index.htm. Accessed 18 September.Saunders, D. A., Smith, G. T. & Rowley, I. The availability and dimensions of tree hollows that provide nest sites for cockatoos (Psittaciformes) in Western Australia. Wildl. Res. 9, 541–556. https://doi.org/10.1071/WR9820541 (1982).Article 

Google Scholar 
Navarro, J. L. & Bucher, E. H. Growth of monk parakeets. Wilson Bull. 102, 520–525 (1990).
Google Scholar 
Murtaugh, P. A. Performance of several variable-selection methods applied to real ecological data. Ecol. Lett. 12, 1061–1068 (2009).Article 

Google Scholar 
Waltman, J. R. & Beissinger, S. R. Breeding behavior of the Green-rumped Parrotlet. Wilson Bull. 104, 65–84 (1992).
Google Scholar 
Enkerlin-Hoeflich, E. C., Packard, J. M. & González-Elizondo, J. J. Safe field techniques for nest inspections and nestling crop sampling of parrots. J. Field Ornithol. 70, 8–17 (1999).
Google Scholar 
Barros, Y. de M. Biologia comportamental de Propyrrhura maracana (Aves, Psittacidae): Fundamentos para conservação in situ de Cyanopsitta spixii (Aves, Psittacidae) na Caatinga. (Tese de Doutorado Universidade Estadual de São Paulo, Rio Claro, 2001).Seixas, G. H. F. & Mourão, G. M. Growth of nestlings of the BlueFronted Amazon (Amazona aestiva) raised in the wild or in captivity. Ornitol. Neotrop. 14, 295–305 (2003).
Google Scholar 
Vigo-Trauco, G. Crecimiento de pichones de Guacamayo Escarlata, Ara macao (Linneus: 1758) en la Reserva Nacional Tambopata-Madre de Dios-Peru (Tese Universidad Nacional Agraria La Molina, 2007).
Google Scholar 
Tjørve, K. M. & Tjørve, E. The use of Gompertz models in growth analyses, and new Gompertz-model approach: An addition to the Unified-Richards family. PLoS One https://doi.org/10.1371/journal.pone.0178691 (2017).Article 
PubMed 
PubMed Central 
MATH 

Google Scholar 
Reed, J. M. The role of behavior in recent avian extinctions and endangerments. Conserv. Biol. 13, 232–241. https://doi.org/10.1046/j.1523-1739.1999.013002232.x (1999).Article 

Google Scholar 
Tjørve, K. M., Underhill, L. G. & Visser, G. H. Energetics of growth in semi-precocial shorebird chicks in a warm environment: The African black oystercatcher, Haematopus moquini. Zoology 110, 176–188. https://doi.org/10.1016/j.zool.2007.01.002 (2007).Article 
PubMed 

Google Scholar 
Tjørve, K. M., Underhill, L. G. & Visser, G. H. The energetic implications of precocial development for three shorebird species breeding in a warm environment. Ibis 150, 125–138 (2008).Article 

Google Scholar 
Ricklefs, R. E. Weight recession in nestling birds. Auk 85, 30–35. https://doi.org/10.2307/4083621 (1968).Article 

Google Scholar 
Huin, N. & Prince, P. A. Chick growth in albatrosses: Curve fitting with a twist. J. Avian Biol. 31, 418–425. https://doi.org/10.1034/j.1600-048X.2000.310318.x (2000).Article 

Google Scholar 
Corsini, M. et al. Growing in the city: Urban evolutionary ecology of avian growth rates. Evol. Appl. 14, 69–84. https://doi.org/10.1111/eva.13081 (2020).Article 
PubMed 
PubMed Central 

Google Scholar 
Barbosa, L. T. Avaliação do sucesso reprodutivo da arara-canindé (Ara ararauna—Psittacidae) e o desenvolvimento urbano de Campo Grande, Mato Grosso do Sul (Dissertação de mestrado Universidade Anhanguera Uniderp, Campo Grande, 2015).Giraldo-Deck, L. M. et al. Development of intraspecific size variation in black coucals, white-browed coucals and ruffs from hatching to fledging. J. Avian Biol. 51, e02440. https://doi.org/10.1111/jav.02440 (2020).Article 

Google Scholar 
Guedes et al. Annual Technical Report from the Instituto Arara Azul., Pantanal-MS, Brazil. 35p, Campo Grande, Instituto Arara Azul (2022). More

Ahmad, M., Saleem, M. A. & Sayyed, A. H. Efficacy of insecticide mixtures against pyrethroid-and organophosphate-resistant populations of Spodoptera litura (Lepidoptera: Noctuidae). Pest. Manag. Sci. 65, 266–274 (2009).CAS 

Google Scholar 
Shekhawat, S. S., Shafiq, A. M. & Basri, M. Effect of host plants on life table parameters of Spodoptera litura. Ind. J. Pure Appl. Biosci. 6, 324–332 (2018).
Google Scholar 
Sang, S. et al. Cross-resistance and baseline susceptibility of Spodoptera litura (Fabricius) (Lepidoptera: Noctuidae) to cyantraniliprole in the south of China. Pest Manag. Sci. 72, 922–928 (2016).CAS 
PubMed 

Google Scholar 
Ortega, D. S., Bacca, T., Silva, A. P. N., Canal, N. A. & Haddi, K. Control failure and insecticides resistance in populations of Rhyzopertha dominica (Coleoptera: Bostrichidae) from Colombia. J. Stored Prod. Res. 92, 101802 (2021).CAS 

Google Scholar 
Li, L. Pest biological control: Goals throughout my life. Annu. Rev. Entomol. 67, 1–10 (2022).PubMed 

Google Scholar 
Razaq, M., Shah, F. M., Ahmad, S. & Afzal, M. in Pest management for agronomic crops. Agronomic Crops (ed. Hasanuzzaman M.) 365–384 (Springer, 2019).Shah, F. M. & Razaq, M. in From agriculture to sustainable agriculture: Prospects for improving pest management in industrial revolution 4.0. Handbook of Smart Materials, Technologies, and Devices: Applications of Industry 4.0. Cham. (ed. Cham) 1–18 (Springer, 2020).Razaq, M. & Shah, F. M. in Biopesticides for management of arthropod pests and weeds. Biopesticides. Biopesticides Voulme 2: Advances in Bioinoculants 7–18 (Elsevier, 2022).Kishinevsky, M., Keasar, T. & Bar-Massada, A. Parasitoid abundance on plants: Effects of host abundance, plant species, and plant flowering state. Arthropod-Plant Interact. 11, 155–161 (2017).
Google Scholar 
Islam, Y. et al. Age-stage, two-sex life table and predation parameters of Harmonia axyridis Pallas (Coleoptera: Coccinellidae), reared on Acyrthosiphon pisum (Harris) (Hemiptera: Aphididae), at four different temperatures. Crop Prot. 2, 106029 (2022).
Google Scholar 
Furlong, M. J. & Zalucki, M. P. Climate change and biological control: The consequences of increasing temperatures on host–parasitoid interactions. Curr. Opin. Insect Sci. 20, 39–44 (2017).PubMed 

Google Scholar 
Islam, Y. et al. Functional response of Harmonia axyridis preying on Acyrthosiphon pisum nymphs: The effect of temperature. Sci. Rep. 11, 1–13 (2021).
Google Scholar 
Keva, O. et al. Increasing temperature and productivity change biomass, trophic pyramids and community-level omega-3 fatty acid content in subarctic lake food webs. Glo. Change Bio. 27, 282–296 (2021).ADS 
CAS 

Google Scholar 
Chi, H. et al. Age-stage, two-sex life table: An introduction to theory, data analysis, and application. Entomol. Gen. 40, 103–124 (2020).
Google Scholar 
Guedes, C. Preferência alimentar e estratégias de alimentação em Coccinellidae (Coleoptera). Oecol. Aust. 17, 59–80 (2013).
Google Scholar 
Hodek, I. & Honêk, A. Ecology of coccinellidae. Vol. 54 464 (Kulver Academic Publisher, 2013).Sutherland, A. M. & Parrella, M. P. Mycophagy in Coccinellidae: Review and synthesis. Biol. Control 51, 284–293 (2009).
Google Scholar 
Hagen, K. & Ks, H. The significance of predaceous Coccinellidae in biological and integrated control of insects. Entomophaga 7, 25–44 (1974).
Google Scholar 
Jawad, D. S., Rashid, Y. D. & Hamzah, A. G. in IOP Conference Series: Earth and Environmental Science. 012029 (IOP Publishing).Kumari, S., Suroshe, S. S., Kumar, D., Budhlakoti, N. & Yana, V. Foraging behaviour of Scymnus coccivora Ayyar against cotton mealybug Phenacoccus solenopsis Tinsley. Saudi J. Biol. Sci. 28, 3799–3805 (2021).PubMed 
PubMed Central 

Google Scholar 
Alloush, A. A. Developmental duration and predation rate of the coccidophagous coccinellid Rhyzobius lophanthae (Blaisdell) (Coleoptera: Coccinellidae) on Aspidiotus nerii Bouche. Bull. Entomol. Res. 109, 612–616 (2019).PubMed 

Google Scholar 
Koch, R., Hutchison, W., Venette, R. & Heimpel, G. Susceptibility of immature monarch butterfly, Danaus plexippus (Lepidoptera: Nymphalidae: Danainae), to predation by Harmonia axyridis (Coleoptera: Coccinellidae). Biol. Control 28, 265–270 (2003).
Google Scholar 
Islam, Y., Shah, F. M., Güncan, A., DeLong, J. P. & Zhou, X. Functional response of Harmonia axyridis to the larvae of Spodoptera litura: The combined effect of temperatures and prey instars. Front. Plant Sci. 13, 849574 (2022).PubMed 
PubMed Central 

Google Scholar 
Dixon, A. F. G. & Dixon, A. E. Insect predator-prey dynamics: ladybird beetles and biological control. (Cambridge University Press, 2000).Thompson, S. Nutrition and culture of entomophagous insects. Annu. Rev. Entomol. 44, 561–592 (1999).CAS 
PubMed 

Google Scholar 
Chaudhary, D. D., Kumar, B. & Mishra, G. Functional response in Coccinellid beetles (Coleoptera: Coccinellidae) is modified by prey-density experience. Can. Entomol. 154, 55068 (2022).
Google Scholar 
Castro, C., Almeida, L. & Penteado, S. The impact of temperature on biological aspects and life table of Harmonia axyridis (Pallas) (Coleoptera: Coccinellidae). Fla. Entomol. 94, 923–932 (2011).
Google Scholar 
Noman, Q. M., Shah, F. M., Mahmood, K. & Razaq, M. Population dynamics of Tephritid fruit flies in citrus and mango orchards of Multan, Southern Punjab, Pakistan. Pakistan J. Zool. 54, 325–330 (2021).
Google Scholar 
Eliopoulos, P. & Stathas, G. Life tables of Habrobracon hebetor (Hymenoptera: Braconidae) parasitizing Anagasta kuehniella and Plodia interpunctella (Lepidoptera: Pyralidae): Effect of host density. J. Econ. Entomol. 101, 982–988 (2008).CAS 
PubMed 

Google Scholar 
Yu, J.-Z., Chi, H. & Chen, B.-H. Comparison of the life tables and predation rates of Harmonia dimidiata (F.) (Coleoptera: Coccinellidae) fed on Aphis gossypii Glover (Hemiptera: Aphididae) at different temperatures. Biol. Control 64, 1–9 (2013).
Google Scholar 
Roy, H. E. & Ten Brown, P. M. years of invasion: Harmonia axyridis (Pallas)(Coleoptera: Coccinellidae) in Britain. Ecol. Entomol. 40, 336–348 (2015).PubMed 
PubMed Central 

Google Scholar 
Koch, R. The multicolored Asian lady beetle, Harmonia axyridis: a review of its biology, uses in biological control, and non-target impacts. J. Insect. Sci. 3, 5689 (2003).
Google Scholar 
de Castro-Guedes, C. F., de Almeida, L. M., do Rocio, C. P. S. & Moura, M. O. Effect of different diets on biology, reproductive variables and life and fertility tables of Harmonia axyridis (Pallas) (Coleoptera, Coccinellidae). Rev. Bras. Entomol. 60, 260–266 (2016).
Google Scholar 
Abdel-Salam, A. & Abdel-Baky, N. Life table and biological studies of Harmonia axyridis Pallas (Col., Coccinellidae) reared on the grain moth eggs of Sitotroga cerealella Olivier (Lep., Gelechiidae). J. Appl. Entomol. 125, 455–462 (2001).
Google Scholar 
Islam, Y. et al. Temperature-dependent functional response of Harmonia axyridis (Coleoptera: Coccinellidae) on the eggs of Spodoptera litura (Lepidoptera: Noctuidae) in laboratory. Insects 11, 583 (2020).PubMed Central 

Google Scholar 
Di, N. et al. Predatory ability of Harmonia axyridis (Coleoptera: Coccinellidae) and Orius sauteri (Hemiptera: Anthocoridae) for suppression of fall armyworm Spodoptera frugiperda (Lepidoptera: Noctuidae). Insects 12, 1063 (2021).PubMed 
PubMed Central 

Google Scholar 
Saljoqi, A.-U.-R., Khan, J. & Ali, G. Rearing of Spodoptera litura (Fabricius) on different artificial diets and its parasitization with Trichogramma chilonis (Ishii). Pak. J. Zool. 47, 1104 (2015).
Google Scholar 
Brown, P. M. et al. The global spread of Harmonia axyridis (Coleoptera: Coccinellidae): Distribution, dispersal and routes of invasion. Biocontrol 56, 623–641 (2011).
Google Scholar 
Chi, H. TWOSEX-MSChart: a computer program for the age-stage, two-sex life table analysis. Available from http://140.120.197.173/ecology/Download/TWOSEX-MSChart-B100000.rar. (2022).Chi, H. Life-table analysis incorporating both sexes and variable development rates among individuals. Environ. Entomol. 17, 26–34 (1988).
Google Scholar 
Chi, H. & Liu, H. Two new methods for the study of insect population ecology. Bull. Inst. Zool. Acad. Sin. 24, 225–240 (1985).
Google Scholar 
Goodman, D. Optimal life histories, optimal notation, and the value of reproductive value. Am. Nat. 119, 803–823 (1982).MathSciNet 

Google Scholar 
Chi, H. & Su, H.-Y. Age-stage, two-sex life tables of Aphidius gifuensis (Ashmead) (Hymenoptera: Braconidae) and its host Myzus persicae (Sulzer) (Homoptera: Aphididae) with mathematical proof of the relationship between female fecundity and the net reproductive rate. Environ. Entomol. 35, 10–21 (2006).
Google Scholar 
Tuan, S.J., Lee, C.C., Chi, H. Population and damage projection of Spodoptera litura (F.) on peanuts (Arachis hypogaea L.) under different conditions using the age-stage, two-sex life table. Pest Manag. Sci. 70, 805–813 (2014a).CAS 
PubMed 

Google Scholar 
Tuan, S.J., Lee, C.C., Chi, H. Erratum: Population and damage projection of Spodoptera litura (F.) on peanuts (Arachis hypogaea L.) under different conditions using the age-stage, two-sex life table. Pest Manag. Sci. 70, 1936 (2014b).CAS 

Google Scholar 
Chi, H. & Yang, T.-C. Two-sex life table and predation rate of Propylaea japonica Thunberg (Coleoptera: Coccinellidae) fed on Myzus persicae (Sulzer)(Homoptera: Aphididae). Environ. Entomol. 32, 327–333 (2003).
Google Scholar 
Chi, H. CONSUME-MSChart: a computer program for consumption rate analysis based on the age stage, two-sex life table analysis. http://140.120.197.173/ecology/Download/CONSUME-MSChart.rar. (2022).Akca, I., Ayvaz, T., Yazici, E., Smith, C. L. & Chi, H. Demography and population projection of Aphis fabae (Hemiptera: Aphididae): With additional comments on life table research criteria. J. Econ. Entomol. 108, 1466–1478 (2015).PubMed 

Google Scholar 
Akköprü, E. P., Atlıhan, R., Okut, H. & Chi, H. Demographic assessment of plant cultivar resistance to insect pests: A case study of the dusky-veined walnut aphid (Hemiptera: Callaphididae) on five walnut cultivars. J. Econ. Entomol. 108, 378–387 (2015).
Google Scholar 
Huang, Y. B. & Chi, H. Age-stage, two-sex life tables of Bactrocera cucurbitae (Coquillett) (Diptera: Tephritidae) with a discussion on the problem of applying female age-specific life tables to insect populations. Insect Sci. 19, 263–273 (2012).
Google Scholar 
Wei, M. et al. Demography of Cacopsylla chinensis (Hemiptera: Psyllidae) reared on four cultivars of Pyrus bretschneideri (Rosales: Rosaceae) and P. communis pears with estimations of confidence intervals of specific life table statistics. J. Econ. Entomol. 113, 2343–2353 (2020).PubMed 

Google Scholar 
Huang, H.-W., Chi, H. & Smith, C. L. Linking demography and consumption of Henosepilachna vigintioctopunctata (Coleoptera: Coccinellidae) fed on Solanum photeinocarpum (Solanales: Solanaceae): with a new method to project the uncertainty of population growth and consumption. J. Econ. Entomol. 111, 1–9 (2018).PubMed 

Google Scholar 
Chi, H.Timing of control based on the stage structure of pest populations: a simulation approach. J. Econ. Entomol. 83,
1143–1150 (1990).
Google Scholar 
Chi, H. TIMING-MSChart: a computer program for the population projection based on age-stage, two-sex life table. (http://140.120.197.173/Ecology/Download/TIMING-MSChart.rar). (2022).Mignault, M.-P., Roy, M. & Brodeur, J. Soybean aphid predators in Quebec and the suitability of Aphis glycines as prey for three Coccinellidae. BioControl 51, 89–106 (2006).
Google Scholar 
Brown, M. Intraguild responses of aphid predators on apple to the invasion of an exotic species, Harmonia axyridis. BioControl 48, 141–153 (2003).
Google Scholar 
Pervez, A., Chandra, S. & Kumar, R. Effect of dietary history on intraguild predation and cannibalism of ladybirds’ eggs. Int. J. Trop. Insect Sci. 41, 2637–2642 (2021).
Google Scholar 
Lundgren, J. G. Nutritional aspects of non-prey foods in the life histories of predaceous Coccinellidae. Biol. Control 51, 294–305 (2009).
Google Scholar 
Yu, J.Z. et al. Demography and mass-rearing Harmonia dimidiata (Coleoptera: Coccinellidae) using Aphis gossypii (Hemiptera: Aphididae) and eggs of Bactrocera dorsalis (Diptera: Tephritidae). J. Econ. Entomol. 111, 595–602 (2018).PubMed 

Google Scholar 
De Oliveira, R. T., dos Santos-Cividanes, T. M., Cividanes, F. J. & da Conceic, L. Harmonia axyridis Pallas (Coleoptera: Coccinellidae): Biological aspects and thermal requirements. Adv. Entomol. 2014, 5589 (2014).
Google Scholar 
Ali, S. et al. Using a two-sex life table tool to calculate the fitness of Orius strigicollis as a predator of Pectinophora gossypiella. Insects 11, 275 (2020).PubMed Central 

Google Scholar 
Merene, Y. Population dynamics and damages of onion thrips (Thripstabaci)(Thysanoptera: Thripidae) on onion in Northeastern Ethiopia. J. Entomol. Nematol. 7, 1–4 (2015).
Google Scholar 
Mou, D. F., Lee, C. C., Smith, C. & Chi, H. Using viable eggs to accurately determine the demographic and predation potential of Harmonia dimidiata (Coleoptera: Coccinellidae). J. Appl. Entomol. 139, 579–591 (2015).
Google Scholar 
Farhadi, R., Allahyari, H. & Chi, H. Life table and predation capacity of Hippodamia variegata (Coleoptera: Coccinellidae) feeding on Aphis fabae (Hemiptera: Aphididae). Biol. Control 59, 83–89 (2011).
Google Scholar 
Hance, T., van Baaren, J., Vernon, P. & Boivin, G. Impact of extreme temperatures on parasitoids in a climate change perspective. Annu. Rev. Entomol. 52, 107–126 (2007).CAS 
PubMed 

Google Scholar 
Ma, X., Zhu, J., Yan, W. & Zhao, C. Projections of desertification trends in Central Asia under global warming scenarios. Sci. Total Environ. 781, 146777 (2021).ADS 
CAS 
PubMed 

Google Scholar  More