Ecology

Falkowski PG. The role of phytoplankton photosynthesis in global biogeochemical cycles. Photosynth Res. 1994;39:235–58.CAS 
PubMed 
Article 

Google Scholar 
Field CB, Behrenfeld MJ, Randerson JT, Falkowski P. Primary production of the biosphere: integrating terrestrial and oceanic components. Science. 1998;281:237.CAS 
PubMed 
Article 

Google Scholar 
Amin SA, Parker MS, Armbrust EV. Interactions between diatoms and bacteria. Microbiol Mol Biol Rev. 2012;76:667–84.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Bell W, Mitchell R. Chemotactic and growth response of marine bacteria to algal extracellular products. Biol Bull. 1972;143:265–77.Article 

Google Scholar 
Seymour JR, Amin SA, Raina J-B, Stocker R. Zooming in on the phycosphere: the ecological interface for phytoplankton–bacteria relationships. Nat Microbiol. 2017;2:17065.CAS 
PubMed 
Article 

Google Scholar 
Azam F, Malfatti F. Microbial structuring of marine ecosystems. Nat Rev Microbiol. 2007;5:782–91.CAS 
PubMed 
Article 

Google Scholar 
Meyer N, Bigalke A, Kaulfuß A, Pohnert G. Strategies and ecological roles of algicidal bacteria. FEMS Microbiol Rev. 2017;41:880–99.CAS 
PubMed 
Article 

Google Scholar 
Windler M, Bova D, Kryvenda A, Straile D, Gruber A, Kroth PG. Influence of bacteria on cell size development and morphology of cultivated diatoms. Phycol Res. 2014;62:269–81.Article 

Google Scholar 
Buhmann MT, Schulze B, Forderer A, Schleheck D, Kroth PG. Bacteria may induce the secretion of mucin-like proteins by the diatom Phaeodactylum tricornutum. J Phycol. 2016;52:463–74.CAS 
PubMed 
Article 

Google Scholar 
van Tol HM, Amin SA, Armbrust EV. Ubiquitous marine bacterium inhibits diatom cell division. ISME J. 2017;11:31–42.PubMed 
Article 
CAS 

Google Scholar 
Amin SA, Hmelo LR, van Tol HM, Durham BP, Carlson LT, Heal KR, et al. Interaction and signaling between a cosmopolitan phytoplankton and associated bacteria. Nature 2015;522:98–101.CAS 
PubMed 
Article 

Google Scholar 
Durham BP, Sharma S, Luo H, Smith CB, Amin SA, Bender SJ, et al. Cryptic carbon and sulfur cycling between surface ocean plankton. Proc Natl Acad Sci. 2015;112:453–7.CAS 
PubMed 
Article 

Google Scholar 
Durham BP, Dearth SP, Sharma S, Amin SA, Smith CB, Campagna SR, et al. Recognition cascade and metabolite transfer in a marine bacteria-phytoplankton model system. Environ Microbiol 2017;19:3500–13.CAS 
PubMed 
Article 

Google Scholar 
Grossart H-P, Levold F, Allgaier M, Simon M, Brinkhoff T. Marine diatom species harbour distinct bacterial communities. Environ Microbiol. 2005;7:860–73.CAS 
PubMed 
Article 

Google Scholar 
Crenn K, Duffieux D, Jeanthon C. Bacterial epibiotic communities of ubiquitous and abundant marine diatoms are distinct in short- and long-term associations. Front Microbiol. 2018;9:2879.PubMed 
PubMed Central 
Article 

Google Scholar 
Behringer G, Ochsenkühn MA, Fei C, Fanning J, Koester JA, Amin SA. Bacterial communities of diatoms display strong conservation across strains and time. Front Microbiol. 2018;9:659.PubMed 
PubMed Central 
Article 

Google Scholar 
Schäfer H, Abbas B, Witte H, Muyzer G. Genetic diversity of ‘satellite’ bacteria present in cultures of marine diatoms. FEMS Microbiol Ecol 2002;42:25–35.PubMed 

Google Scholar 
Shibl AA, Isaac A, Ochsenkühn MA, Cárdenas A, Fei C, Behringer G, et al. Diatom modulation of select bacteria through use of two unique secondary metabolites. Proc Natl Acad Sci. 2020;117:27445–55.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Fu H, Uchimiya M, Gore J, Moran MA. Ecological drivers of bacterial community assembly in synthetic phycospheres. Proc Natl Acad Sci. 2020;117:3656–3662.Stock W, Blommaert L, De Troch M, Mangelinckx S, Willems A, Vyverman W, et al. Host specificity in diatom-bacteria interactions alleviates antagonistic effects. FEMS Microbiol Ecol. 2019;95:fiz171.Segev E, Wyche TP, Kim KH, Petersen J, Ellebrandt C, Vlamakis H, et al. Dynamic metabolic exchange governs a marine algal-bacterial interaction. Elife. 2016;5:e17473.PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
Wagner-Döbler I, Ballhausen B, Berger M, Brinkhoff T, Buchholz I, Bunk B, et al. The complete genome sequence of the algal symbiont Dinoroseobacter shibae: a hitchhiker’s guide to life in the sea. ISME J. 2010;4:61–77.PubMed 
Article 
CAS 

Google Scholar 
Frank O, Michael V, Päuker O, Boedeker C, Jogler C, Rohde M, et al. Plasmid curing and the loss of grip – the 65-kb replicon of Phaeobacter inhibens DSM 17395 is required for biofilm formation, motility and the colonization of marine algae. Syst Appl Microbiol. 2015;38:120–7.CAS 
PubMed 
Article 

Google Scholar 
Paul C, Pohnert G. Interactions of the algicidal bacterium Kordia algicida with diatoms: regulated protease excretion for specific algal lysis. PLoS One. 2011;6:e21032.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Stock F, Bilcke G, De Decker S, Osuna-Cruz CM, Van den Berge K, Vancaester E, et al. distinctive growth and transcriptional changes of the diatom Seminavis robusta in response to quorum sensing related compounds. Front Microbiol. 2020;11:1240.PubMed 
PubMed Central 
Article 

Google Scholar 
Guillard RRL Culture of Phytoplankton for Feeding Marine Invertebrates. In: Smith WL, Chanley MH, editors. Culture of marine invertebrate animals: proceedings — 1st conference on culture of marine invertebrate animals greenport. Boston, MA: Springer US; 1975. p. 29–60.Rasband WS (2016). ImageJ, U.S. National Institutes of Health, Bethesda, MD, USA. Available at: http://imagej.nih.gov/ij/, 1997–2015.DuBois M, Gilles KA, Hamilton JK, Rebers PA, Smith F. Colorimetric method for determination of sugars and related substances. Anal Chem. 1956;28:350–6.CAS 
Article 

Google Scholar 
Bolger AM, Lohse M, Usadel B. Trimmomatic: a flexible trimmer for Illumina sequence data. Bioinformatics. 2014;30:2114–20.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Robinson MD, McCarthy DJ, Smyth GK. edgeR: a Bioconductor package for differential expression analysis of digital gene expression data. Bioinformatics. 2010;26:139–40.CAS 
PubMed 
Article 

Google Scholar 
Langfelder P, Horvath S. WGCNA: an R package for weighted correlation network analysis. BMC Bioinform. 2008;9:559.Article 
CAS 

Google Scholar 
Alexa A, Rahnenfuhrer J (2021). topGO: Enrichment analysis for gene ontology. R package version 2.46.0.Csardi G, Nepusz T (2006). “The igraph software package for complex network research.” InterJournal, Complex Systems, 1695. https://igraph.org.Wei Q, Khan IK, Ding Z, Yerneni S, Kihara D. NaviGO: interactive tool for visualization and functional similarity and coherence analysis with gene ontology. BMC Bioinform. 2017;18:177.Article 
CAS 

Google Scholar 
Shapiro HM (2003). Physical parameters and their uses. In: Shapiro HM (ed). Practical Flow Cytometry. John Wiley & Sons, Inc.: New York, NY, USA, pp. 273-85.Clercq AD, Inzé D. Cyclin-dependent kinase inhibitors in yeast, animals, and plants: a functional comparison. Crit Rev Biochem Mol Biol. 2006;41:293–313.PubMed 
Article 
CAS 

Google Scholar 
Zinser ER. The microbial contribution to reactive oxygen species dynamics in marine ecosystems. Environ Microbiol Rep. 2018;10:412–27.CAS 
PubMed 
Article 

Google Scholar 
Whalen KE, Kirby C, Nicholson RM, O’Reilly M, Moore BS, Harvey EL. The chemical cue tetrabromopyrrole induces rapid cellular stress and mortality in phytoplankton. Sci Rep. 2018;8:15498.PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
Sheyn U, Rosenwasser S, Ben-Dor S, Porat Z, Vardi A. Modulation of host ROS metabolism is essential for viral infection of a bloom-forming coccolithophore in the ocean. ISME J. 2016;10:1742–54.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Finkel ZV, Irwin AJ, Schofield O. Resource limitation alters the ¾ size scaling of metabolic rates in phytoplankton. Mar Ecol Prog Ser. 2004;273:269–80.Article 

Google Scholar 
De Troch M, Chepurnov V, Gheerardyn H, Vanreusel A, Ólafsson E. Is diatom size selection by harpacticoid copepods related to grazer body size? J Exp Mar Biol Ecol. 2006;332:1–11.Article 

Google Scholar 
Finkel ZV. Light absorption and size scaling of light-limited metabolism in marine diatoms. Limnol Oceanogr. 2001;46:86–94.CAS 
Article 

Google Scholar 
Wilhelm T, Said M, Naim V. DNA replication stress and chromosomal instability: dangerous liaisons. Genes. 2020;11:642.CAS 
PubMed Central 
Article 

Google Scholar 
Gelot C, Magdalou I, Lopez BS. Replication stress in mammalian cells and its consequences for mitosis. Genes. 2015;6:267–98.Vogt E, Kirsch-Volders M, Parry J, Eichenlaub-Ritter U. Spindle formation, chromosome segregation and the spindle checkpoint in mammalian oocytes and susceptibility to meiotic error. Mutat. Res. – Genet. Toxicol. Environ. Mutagen. 2008;651:14–29.CAS 

Google Scholar 
Van de Meene AML, Pickett-Heaps JD. Valve morphogenesis in the centric diatom Rhizosolenia setigera (Bacillariophyceae, Centrales) and its taxonomic implications. Eur J Phycol. 2004;39:93–104.Article 

Google Scholar 
Pollara SB, Becker JW, Nunn BL, Boiteau R, Repeta D, Mudge MC, et al. Bacterial quorum-sensing signal arrests phytoplankton cell division and impacts virus-induced mortality. mSphere. 2021;6:e00009–21.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Von Dassow P, Petersen TW, Chepurnov VA, Virginia Armbrust E. Inter- and intraspecific relationships between nuclear DNA content and cell size in selected members of the centric diatom genus Thalassiosira (Bacillariophyceae). J Phycol. 2008;44:335–49.Article 
CAS 

Google Scholar 
Pokrzywinski KL, Tilney CL, Warner ME, Coyne KJ. Cell cycle arrest and biochemical changes accompanying cell death in harmful dinoflagellates following exposure to bacterial algicide IRI-160AA. Sci Rep. 2017;7:45102.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Durkin CA, Mock T, Armbrust EV. Chitin in diatoms and its association with the cell wall. Eukaryot Cell. 2009;8:1038.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Wildermuth MC. Modulation of host nuclear ploidy: a common plant biotroph mechanism. Curr Opin Plant Biol. 2010;13:449–58.CAS 
PubMed 
Article 

Google Scholar 
Cho J-C, Giovannoni SJ. Croceibacter atlanticus gen. nov., sp. nov., A Novel Marine Bacterium in the Family Flavobacteriaceae. Syst Appl Microbiol. 2003;26:76–83.CAS 
PubMed 
Article 

Google Scholar 
Trivedi P, Leach JE, Tringe SG, Sa T, Singh BK. Plant–microbiome interactions: from community assembly to plant health. Nat Rev Microbiol. 2020;18:607–21.CAS 
PubMed 
Article 

Google Scholar 
Morris JJ, Lenski RE, Zinser ER. The black queen hypothesis: evolution of dependencies through adaptive gene loss. mBio. 2012;3:e00036–12.PubMed 
PubMed Central 
Article 

Google Scholar 
Ndhlovu A, Durand PM, Ramsey G. Programmed cell death as a black queen in microbial communities. Mol Ecol. 2021;30:1110–9.CAS 
PubMed 
Article 

Google Scholar 
Schreiber F, Littmann S, Lavik G, Escrig S, Meibom A, Kuypers MMM, et al. Phenotypic heterogeneity driven by nutrient limitation promotes growth in fluctuating environments. Nat Microbiol. 2016;1:16055.CAS 
PubMed 
Article 

Google Scholar 
Sengupta A, Carrara F, Stocker R. Phytoplankton can actively diversify their migration strategy in response to turbulent cues. Nature 2017;543:555–8.CAS 
PubMed 
Article 

Google Scholar 
Levy SF, Ziv N, Siegal ML. Bet hedging in yeast by heterogeneous, age-correlated expression of a stress protectant. PLoS Biol. 2012;10:e1001325.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Ackermann M. A functional perspective on phenotypic heterogeneity in microorganisms. Nat Rev Microbiol. 2015;13:497–508.CAS 
PubMed 
Article 

Google Scholar 
Blair PM, Land ML, Piatek MJ, Jacobson DA, Lu T-YS, Doktycz MJ, et al. Exploration of the biosynthetic potential of the populus microbiome. mSystems. 2018;3:e00045–18.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Helfrich EJN, Vogel CM, Ueoka R, Schäfer M, Ryffel F, Müller DB, et al. Bipartite interactions, antibiotic production and biosynthetic potential of the Arabidopsis leaf microbiome. Nat Microbiol. 2018;3:909–19.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Long RA, Qureshi A, Faulkner DJ, Azam F. 2-n-Pentyl-4-quinolinol produced by a marine Alteromonas sp. and its potential ecological and biogeochemical roles. Appl Environ Microbiol. 2003;69:568–76.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Calabrese EJ. Hormesis: from mainstream to therapy. Cell Commun Signal. 2014;8:289–91.Article 

Google Scholar 
Chen WM, Sheu FS, Sheu SY. Novel l-amino acid oxidase with algicidal activity against toxic cyanobacterium Microcystis aeruginosa synthesized by a bacterium Aquimarina sp. Enzym Microb Technol. 2011;49:372–9.CAS 
Article 

Google Scholar 
El-Aouar Filho RA, Nicolas A, De Paula Castro TL, Deplanche M, De Carvalho Azevedo VA, Goossens PL, et al. Heterogeneous family of cyclomodulins: smart weapons that allow bacteria to hijack the eukaryotic cell cycle and promote infections. Front Cell Infect Microbiol. 2017;7:364.Ricci V, Giannouli M, Romano M, Zarrilli R. Helicobacter pylori gamma-glutamyl transpeptidase and its pathogenic role. World J Gastroenterol. 2014;20:630–8.PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
Teeling H, Fuchs Bernhard M, Becher D, Klockow C, Gardebrecht A, Bennke Christin M, et al. Substrate-controlled succession of marine bacterioplankton populations induced by a phytoplankton bloom. Science 2012;336:608–11.CAS 
PubMed 
Article 

Google Scholar  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

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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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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

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

Duarte, C. M. et al. The soundscape of the Anthropocene ocean. Science 371, eaba4658 (2021).CAS 
PubMed 
Article 

Google Scholar 
Bailey, H., Brookes, K. L. & Thompson, P. M. Assessing environmental impacts of offshore wind farms: Lessons learned and recommendations for the future. Aquat. Biosyst. 10, 1–13 (2014).Article 

Google Scholar 
Dahl, P. H., de Jong, C. A. & Popper, A. N. The underwater sound field from impact pile driving and its potential effects on marine life. Acoust. Today. 11, 18–25 (2015).
Google Scholar 
Mooney, T. A., Andersson, M. H. & Stanley, J. Acoustic impacts of offshore wind energy on fishery resources. Oceanography 33, 82–95 (2020).Article 

Google Scholar 
Madsen, P. T., Wahlberg, M., Tougaard, J., Lucke, K. & Tyack, A. P. Wind turbine underwater noise and marine mammals: implications of current knowledge and data needs. Mar. Ecol. Prog. Ser. 309, 279–295 (2006).ADS 
Article 

Google Scholar 
Slabbekoorn, H. et al. A noisy spring: the impact of globally rising underwater sound levels on fish. Trends Ecol. Evol. 25, 419–427 (2010).PubMed 
Article 

Google Scholar 
Jones, I. T., Stanley, J. A. & Mooney, T. A. Impulsive pile driving noise elicits alarm responses in squid (Doryteuthis pealeii). Mar. Pollut. Bull. 150, 110792 (2020).CAS 
PubMed 
Article 

Google Scholar 
Roberts, L. & Elliott, M. Good or bad vibrations? Impacts of anthropogenic vibration on the marine epibenthos. Sci. Total. Environ. 595, 255–268 (2017).ADS 
CAS 
PubMed 
Article 

Google Scholar 
Hawkins, A. D., Hazelwood, R. A., Popper, A. N. & Macey, P. C. Substrate vibrations and their potential effects upon fishes and invertebrates. J. Acoust. Soc. Am. 149, 2782–2790 (2021).ADS 
PubMed 
Article 

Google Scholar 
Popper, A. N. et al. Offshore wind energy development: Research priorities for sound and vibration effects on fishes and aquatic invertebrates. J. Acoust. Soc. Am. 151, 205–215 (2022).PubMed 
Article 

Google Scholar 
Williams, R. et al. Impacts of anthropogenic noise on marine life: Publication patterns, new discoveries, and future directions in research and management. Ocean. Coast. Manag. 115, 17–24 (2015).Article 

Google Scholar 
Roberts, L., Cheesman, S., Breithaupt, T. & Elliott, M. Sensitivity of the mussel Mytilus edulis to substrate-borne vibration in relation to anthropogenically generated noise. Mar. Ecol. Prog. Ser. 538, 185–195 (2015).ADS 
CAS 
Article 

Google Scholar 
Day, R. D., McCauley, R. D., Fitzgibbon, Q. P., Hartmann, K. & Semmens, J. M. Exposure to seismic air gun signals causes physiological harm and alters behavior in the scallop Pecten fumatus. Proc. Natl. Acad. Sci. 114, E8537–E8546 (2017).CAS 
PubMed 
PubMed Central 

Google Scholar 
Newell, R. I. Ecosystem influences of natural and cultivated populations of suspension-feeding bivalve molluscs: a review. J. Shellfish. Res. 23, 51–62 (2004).
Google Scholar 
Wijsman, J.W.M., Troost, K., Fang, J. & Roncarati, A. Global production of marine bivalves. Trends and challenges. Goods and services of marine bivalves, (Eds. Small, A.D., Ferrerira, J.G., Grant, J., Petersen, J.K., Strand, O.) 7–26 (Springer, Cham, 2019).Perveen, R., Kishor, N. & Mohanty, S. R. Off-shore wind farm development: Present status and challenges. Renew. Sust. Energ. Rev. 29, 780–792 (2014).Article 

Google Scholar 
Vaissière, A. C., Levrel, H., Pioch, S. & Carlier, A. Biodiversity offsets for offshore wind farm projects: The current situation in Europe. Mar. Policy. 48, 172–183 (2014).Article 

Google Scholar 
Musial, W.D., Beiter, P.C., Spitsen, P., Nunemaker, J. & Gevorgian, V. 2018 offshore wind technologies market report. US Department of Energy (2019).Lacroix, D. & Pioch, S. The multi-use in wind farm projects: more conflicts or a win-win opportunity?. Aquat. Living. Resour. 24, 129–135 (2011).Article 

Google Scholar 
FishstatJ. FishStatJ-Software for Fishery and Aquaculture Statistical Time Series. FAO Fisheries Division [online], Rome. Accessed April 10, 2022. (2020).Flanders Marine Institute. Maritime Boundaries Geodatabase: Maritime Boundaries and Exclusive Economic Zones (200NM), version 11. Available online at https://www.marineregions.org/ (2019).Kallehave, D., Byrne, B. W., LeBlanc Thilsted, C. & Mikkelsen, K. K. Optimization of monopiles for offshore wind turbines. Philos. Trans. R. Soc. A 373, 20140100 (2015).ADS 
Article 

Google Scholar 
Bruns, B., Stein, P., Kuhn, C., Sychla, H. & Gattermann, J. Hydro sound measurements during the installation of large diameter offshore piles using combinations of independent noise mitigation systems. Proceedings of the Inter-noise Conference 1–10 (Melbourne, Australia, 2014).Hunt, H. L. & Scheibling, R. E. Role of early post-settlement mortality in recruitment of benthic marine invertebrates. Mar. Ecol. Prog. Ser. 155, 269–301 (1997).ADS 
Article 

Google Scholar 
Pilditch, C. A. & Grant, J. Effect of variations in flow velocity and phytoplankton concentration on sea scallop (Placopecten magellanicus) grazing rates. J. Exp. Mar. Biol. Ecol. 240, 111–136 (1999).Article 

Google Scholar 
Chauvaud, L., Thouzeau, G. & Paulet, Y. M. Effects of environmental factors on the daily growth rate of Pecten maximus juveniles in the Bay of Brest (France). J. Exp. Mar. Biol. Ecol. 227, 83–111 (1998).Article 

Google Scholar 
Rheuban, J. E., Doney, S. C., Cooley, S. R. & Hart, D. R. Projected impacts of future climate change, ocean acidification, and management on the US Atlantic Sea scallop (Placopecten magellanicus) fishery. PLoS ONE 13, e0203536 (2018).PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
Hawkins, A. D., Pembroke, A. E. & Popper, A. N. Information gaps in understanding the effects of noise on fishes and invertebrates. Rev. Fish. Biol. Fish. 25, 39–64 (2015).Article 

Google Scholar 
Neo, Y. Y. et al. Temporal structure of sound affects behavioural recovery from noise impact in European seabass. Biol. Conserv. 178, 65–73 (2014).Article 

Google Scholar 
Sabet, S. S., Neo, Y. Y. & Slabbekoorn, H. The effect of temporal variation in sound exposure on swimming and foraging behaviour of captive zebrafish. Anim. Behav. 107, 49–60 (2015).Article 

Google Scholar 
Radford, A. N., Lèbre, L., Lecaillon, G., Nedelec, S. L. & Simpson, S. D. Repeated exposure reduces the response to impulsive noise in European seabass. Glob. Change. Biol. 22, 3349–3360 (2016).ADS 
Article 

Google Scholar 
Solan, M. et al. Anthropogenic sources of underwater sound can modify how sediment-dwelling invertebrates mediate ecosystem properties. Sci. Rep. 6, 1–9 (2016).Article 
CAS 

Google Scholar 
Hubert, J., Booms, E., Witbaard, R. & Slabbekoorn, H. Responsiveness and habituation to repeated sound exposures and pulse trains in blue mussels. J. Exp. Mar. Biol. Ecol. 547, 151668 (2022).Article 

Google Scholar 
Robson, A. A., Chauvaud, L., Wilson, R. P. & Halsey, L. G. Small actions, big costs: the behavioural energetics of a commercially important invertebrate. J. R. Soc. Interface. 9, 1486–1498 (2012).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Thomas, G. E. & Gruffydd, L. D. The types of escape reactions elicited in the scallop Pecten maximus by selected sea-star species. Mar. Biol. 10, 87–93 (1971).Article 

Google Scholar 
Livingstone, D. R., Dezwaan, A. & Thompson, R. J. Aerobic metabolism octopine production and phosphoarginine as sources of energy in the phasic and catch adductor muscles of the giant scallop Placopecten magellanicus during swimming and the subsequent recovery period. Comp. Biochem. Physiol. B. Biochem. Mol. Biol. 70, 35–44 (1981).Article 

Google Scholar 
Comeau, L. A., Babarro, J. M., Longa, A. & Padin, X. A. Valve-gaping behavior of raft-cultivated mussels in the Ría de Arousa Spain. Aquac. Rep. 9, 68–73 (2018).Article 

Google Scholar 
Wilson, R., Reuter, P. & Wahl, M. Muscling in on mussels: new insights into bivalve behaviour using vertebrate remote-sensing technology. Mar. Biol. 147, 1165–1172 (2005).Article 

Google Scholar 
Comeau, L. A. & Babarro, J. M. Narrow valve gaping in the invasive mussel Limnoperna securis: implications for competition with the indigenous mussel Mytilus galloprovincialis in NW Spain. Aquac. Int. 22, 1215–1227 (2014).CAS 
Article 

Google Scholar 
Comeau, L. A., Mayrand, E. & Mallet, A. Winter quiescence and spring awakening of the Eastern oyster Crassostrea virginica at its northernmost distribution limit. Mar. Biol. 159, 2269–2279 (2012).Article 

Google Scholar 
Palmer, B. A. et al. The image-forming mirror in the eye of the scallop. Science 358, 1172–1175 (2017).ADS 
CAS 
PubMed 
Article 

Google Scholar 
Chappell, D. R., Horan, T. M. & Speiser, D. I. Panoramic spatial vision in the bay scallop Argopecten irradians. Proc. R. Soc. B. 288, 20211730 (2021).PubMed 
PubMed Central 
Article 

Google Scholar 
Mat, A. M., Massabuau, J. C., Ciret, P. & Tran, D. Evidence for a plastic dual circadian rhythm in the oyster Crassostrea gigas. Chronobiol. Int. 29, 857–867 (2012).PubMed 
Article 

Google Scholar 
Friard, O. & Gamba, M. BORIS: a free, versatile open-source event-logging software for video/audio coding and live observations. Methods. Ecol. Evol. 7, 1325–1330 (2016).Article 

Google Scholar 
Dickie, L. M. & Medcof, J. C. Causes of mass mortalities of scallops (Placopecten magellanicus) in the southwestern Gulf of St Lawrence. J. Fish. Res. Board. Can. 20, 451–482 (1963).Article 

Google Scholar 
Coleman, S., Cleaver, C., Morse, D., Brady, D. C. & Kiffney, T. The coupled effects of stocking density and temperature on Sea Scallop (Placopecten magellanicus) growth in suspended culture. Aquac. Rep. 20, 100684 (2021).Article 

Google Scholar 
Methratta, E. T. Monitoring fisheries resources at offshore wind farms: BACI vs. BAG designs. ICES. J. Mar. Sci. 77, 890–900 (2020).Article 

Google Scholar 
ISO, 18406. Underwater acoustics measurement of radiated underwater sound from percussive pile driving. International Organization for Standardization (Geneva, Switzerland), 1–33 (2017).Madsen, P. T. Marine mammals and noise: Problems with root mean square sound pressure levels for transients. J. Acoust. Soc. Am. 117, 3952–3957 (2005).ADS 
CAS 
PubMed 
Article 

Google Scholar 
Bates, D., Mächler, M., Bolker, B. & Walker, S. Fitting linear mixed-effects models using lme4. J. Stat. Softw. 67, 1–48 (2015).Article 

Google Scholar 
Lenth, R.V. emmeans: Estimated marginal means, aka least squares means. R package version 1.3.5.1. Retrieved from http://CRAN.R-project.org/package=emmeans (2019).Kragh, I. M. et al. Signal-specific amplitude adjustment to noise in common bottlenose dolphins (Tursiops truncatus). J. Exp. Biol. 222, jeb216606 (2019).PubMed 
Article 

Google Scholar 
Warner, R. M. Spectral Analysis of Time-Series Data (Guilford Press, 1998).
Google Scholar 
Fisher, R. A. Tests of significance in harmonic analysis. Proc. Math. Phys. Eng. Sci. 125, 54–59 (1929).MATH 

Google Scholar  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

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