Ecology

1.Gibson, L. et al. Primary forests are irreplaceable for sustaining tropical biodiversity. Nature 478, 378–383 (2011).ADS 
CAS 
Article 

Google Scholar 
2.Luyssaert, S. et al. Old-growth forests as global carbon sinks. Nature 455, 213–215 (2008).ADS 
CAS 
Article 

Google Scholar 
3.WWF. Living planet report 2020 – bending the curve of biodiversity loss. (WWF, Gland, Switzerland, 2020).4.Grantham, H. S. et al. Anthropogenic modification of forests means only 40% of remaining forests have high ecosystem integrity. Nat. Commun. 11, 5978. https://doi.org/10.1038/s41467-020-19493-3 (2020).ADS 
CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
5.Steffen, W. et al. Planetary boundaries: Guiding human development on a changing planet. Science 347, 1259855. https://doi.org/10.1126/science.1259855 (2015).CAS 
Article 
PubMed 

Google Scholar 
6.Balmford, A. et al. Economic reasons for conserving wild nature. Science 297, 950–953 (2002).ADS 
CAS 
Article 

Google Scholar 
7.Hockings, M. Systems for assessing the effectiveness of management in protected areas. Bioscience 53, 823–832. https://doi.org/10.1641/0006-3568(2003)053[0823:Sfateo]2.0.Co;2 (2003).Article 

Google Scholar 
8.Reboredo Segovia, A. L., Romano, D. & Armsworth, P. R. Who studies where? Boosting tropical conservation research where it is most needed. Front. Ecol. Environ. 18, 159–166. https://doi.org/10.1002/fee.2146 (2020).Article 

Google Scholar 
9.Geldmann, J. et al. Effectiveness of terrestrial protected areas in reducing habitat loss and population declines. Biol. Conserv. 161, 230–238. https://doi.org/10.1016/j.biocon.2013.02.018 (2013).Article 

Google Scholar 
10.Heino, M. et al. Forest loss in protected areas and intact forest landscapes: A global analysis. PLoS ONE 10, e0138918. https://doi.org/10.1371/journal.pone.0138918 (2015).CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
11.Joppa, L. N. & Pfaff, A. High and far: Biases in the location of protected areas. PLoS ONE 4, e8273. https://doi.org/10.1371/journal.pone.0008273 (2009).ADS 
CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
12.Ferraro, P. et al. More strictly protected areas are not necessarily more protective: Evidence from bolivia, costa rica, indonesia, and thailand. Environ. Res. Lett. 8, 025011 (2013).ADS 
Article 

Google Scholar 
13.Joppa, L. N. & Pfaff, A. Global protected area impacts. Proc. R. Soc. London B Biol. Sci. 278, 1633–1638 (2011).
Google Scholar 
14.Geldmann, J., Manica, A., Burgess, N. D., Coad, L. & Balmford, A. A global-level assessment of the effectiveness of protected areas at resisting anthropogenic pressures. Proc. Natl. Acad. Sci. 116, 23209–23215. https://doi.org/10.1073/pnas.1908221116 (2019).ADS 
CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
15.Allan, J. R. et al. Recent increases in human pressure and forest loss threaten many natural world heritage sites. Biol. Conserv. 206, 47–55. https://doi.org/10.1016/j.biocon.2016.12.011 (2017).Article 

Google Scholar 
16.Watson, J., Edward, M. & Venter, O. Mapping the continuum of humanity’s footprint on land. One Earth 1, 175–180. https://doi.org/10.1016/j.oneear.2019.09.004 (2019).Article 

Google Scholar 
17.Joppa, L. & Pfaff, A. Reassessing the forest impacts of protection. Ann. N. Y. Acad. Sci. 1185, 135–149. https://doi.org/10.1111/j.1749-6632.2009.05162.x (2010).ADS 
Article 
PubMed 

Google Scholar 
18.Gaveau, D. L. A. et al. Evaluating whether protected areas reduce tropical deforestation in sumatra. J. Biogeogr. 36, 2165–2175. https://doi.org/10.1111/j.1365-2699.2009.02147.x (2009).Article 

Google Scholar 
19.Andam, K. S., Ferraro, P. J., Pfaff, A., Sanchez-Azofeifa, G. A. & Robalino, J. A. Measuring the effectiveness of protected area networks in reducing deforestation. Proc. Natl. Acad. Sci. 105, 16089–16094. https://doi.org/10.1073/pnas.0800437105 (2008).ADS 
Article 
PubMed 
PubMed Central 

Google Scholar 
20.Potapov, P. et al. The last frontiers of wilderness: Tracking loss of intact forest landscapes from 2000 to 2013. Sci. Adv. https://doi.org/10.1126/sciadv.1600821 (2017).21.Achard, F. et al. Determination of tropical deforestation rates and related carbon losses from 1990 to 2010. Glob. Change Biol. 20, 2540–2554. https://doi.org/10.1111/gcb.12605 (2014).ADS 
Article 

Google Scholar 
22.Hughes, A. C. Understanding the drivers of southeast asian biodiversity loss. Ecosphere 8, e01624. https://doi.org/10.1002/ecs2.1624 (2017).Article 

Google Scholar 
23.Sodhi, N. S., Koh, L. P., Brook, B. W. & Ng, P. K. L. Southeast asian biodiversity: An impending disaster. Trends Ecol. Evol. 19, 654–660. https://doi.org/10.1016/j.tree.2004.09.006 (2004).Article 
PubMed 

Google Scholar 
24.Estoque, R. C. et al. The future of southeast asia’s forests. Nat. Commun. 10, 1829–1829. https://doi.org/10.1038/s41467-019-09646-4 (2019).ADS 
CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
25.Stolton, S. et al. Reporting Progress in Protected Areas a Site Level Management Effectiveness Tracking Tool (Gland, 2007).
Google Scholar 
26.Coad, L. et al. Measuring impact of protected area management interventions: Current and future use of the global database of protected area management effectiveness. Philos. Trans. R. Soc. B Biol. Sci. 370, 20140281. https://doi.org/10.1098/rstb.2014.0281 (2015).Article 

Google Scholar 
27.CBD. Cop 10 decision x/2: Strategic Plan for Biodiversity 2011–2020 (Convention on Biological Diversity, 2011).28.UNFCCC. Adoption of the Paris Agreement (Proposal by the President Draft Decision -/CP.21, 2015).29.Gaveau, D. L. A. et al. Four Decades of Forest Persistence, Clearance and Logging on Borneo. Vol. 9 (2014).30.Bebber, D. P. & Butt, N. Tropical protected areas reduced deforestation carbon emissions by one third from 2000–2012. Sci. Rep. 7, 14005. https://doi.org/10.1038/s41598-017-14467-w (2017).ADS 
CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
31.Buřivalová, Z., Hart, S. J., Radeloff, V. C. & Srinivasan, U. Early warning sign of forest loss in protected areas. Curr. Biol. https://doi.org/10.1016/j.cub.2021.07.072 (2021).Article 
PubMed 

Google Scholar 
32.Apan, A., Suarez, L. A., Maraseni, T. & Castillo, J. A. The rate, extent and spatial predictors of forest loss (2000–2012) in the terrestrial protected areas of the philippines. Appl. Geogr. 81, 32–42. https://doi.org/10.1016/j.apgeog.2017.02.007 (2017).Article 

Google Scholar 
33.Graham, V., Nurhidayah, L. & Astuti, R. Reference Module in Earth Systems and Environmental Sciences (Elsevier, 2019).
Google Scholar 
34.Graham, V., Laurance, S. G., Grech, A., McGregor, A. & Venter, O. A comparative assessment of the financial costs and carbon benefits of redd+ strategies in southeast asia. Environ. Res. Lett. 11, 114022. https://doi.org/10.1088/1748-9326/11/11/114022 (2016).ADS 
CAS 
Article 

Google Scholar 
35.Mascia, M. B. et al. Protected area downgrading, downsizing, and degazettement (paddd) in africa, asia, and latin america and the caribbean, 1900–2010. Biol. Conserv. 169, 355–361. https://doi.org/10.1016/j.biocon.2013.11.021 (2014).Article 

Google Scholar 
36.Geldmann, J. et al. A global analysis of management capacity and ecological outcomes in terrestrial protected areas. Conserv Lett 11, e12434 (2018).Article 

Google Scholar 
37.Graham, V. et al. Management resourcing and government transparency are key drivers of biodiversity outcomes in southeast asian protected areas. Biol. Conserv. 253, 108875. https://doi.org/10.1016/j.biocon.2020.108875 (2021).Article 

Google Scholar 
38.Gill, D. A. et al. Capacity shortfalls hinder the performance of marine protected areas globally. Nature 543, 665 (2017).ADS 
CAS 
Article 

Google Scholar 
39.Coad, L. et al. Widespread shortfalls in protected area resourcing undermine efforts to conserve biodiversity. Front Ecol Environ 17, 259–264. https://doi.org/10.1002/fee.2042 (2019).Article 

Google Scholar 
40.Carranza, T., Manica, A., Kapos, V. & Balmford, A. Mismatches between conservation outcomes and management evaluation in protected areas: A case study in the brazilian cerrado. Biol. Conserv. 173, 10–16. https://doi.org/10.1016/j.biocon.2014.03.004 (2014).Article 

Google Scholar 
41.Nolte, C. & Agrawal, A. Linking management effectiveness indicators to observed effects of protected areas on fire occurrence in the amazon rainforest. Conserv. Biol. 27, 155–165. https://doi.org/10.1111/j.1523-1739.2012.01930.x (2013).Article 
PubMed 

Google Scholar 
42.Nolte, C., Agrawal, A. & Barreto, P. Setting priorities to avoid deforestation in amazon protected areas: Are we choosing the right indicators?. Environ. Res. Lett. 8, 015039. https://doi.org/10.1088/1748-9326/8/1/015039 (2013).ADS 
Article 

Google Scholar 
43.Eklund, J., Coad, L., Geldmann, J. & Cabeza, M. What constitutes a useful measure of protected area effectiveness? A case study of management inputs and protected area impacts in madagascar. Conserv. Sci. Pract. https://doi.org/10.1111/csp2.107 (2019).Article 

Google Scholar 
44.Bennett, N. J. et al. Conservation social science: Understanding and integrating human dimensions to improve conservation. Biol. Conserv. 205, 93–108. https://doi.org/10.1016/j.biocon.2016.10.006 (2017).Article 

Google Scholar 
45.Schleicher, J., Peres, C. A. & Leader-Williams, N. Conservation performance of tropical protected areas: How important is management?. Conserv. Lett. https://doi.org/10.1111/conl.12650 (2019).Article 

Google Scholar 
46.Baccini, A. et al. Estimated carbon dioxide emissions from tropical deforestation improved by carbon-density maps. Nat. Clim. Change 2, 182–185 (2012).ADS 
CAS 
Article 

Google Scholar 
47.Walker, W. S. et al. The role of forest conversion, degradation, and disturbance in the carbon dynamics of amazon indigenous territories and protected areas. Proc. Natl. Acad. Sci. 117, 3015–3025. https://doi.org/10.1073/pnas.1913321117 (2020).ADS 
CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
48.Wolosin, M. & Harris, N. Tropical Forests and Climate Change: The Latest Science (World Resources Institute, 2018).
Google Scholar 
49.Griscom, B. W. et al. Natural climate solutions. Proc. Natl. Acad. Sci. 114, 11645–11650. https://doi.org/10.1073/pnas.1710465114 (2017).ADS 
CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
50.Schleicher, J. et al. Statistical matching for conservation science. Conserv. Biol. https://doi.org/10.1111/cobi.13448 (2019).Article 
PubMed 
PubMed Central 

Google Scholar 
51.Rights and Resources Initiative. Who owns the world’s land? A global baseline of formally recognized Indigenous and community land rights. (Rights and Resources Initiative, Washington DC, 2015).52.Santika, T. et al. Community forest management in indonesia: Avoided deforestation in the context of anthropogenic and climate complexities. Glob. Environ. Chang. 46, 60–71. https://doi.org/10.1016/j.gloenvcha.2017.08.002 (2017).Article 

Google Scholar 
53.Dudley, N., Shadie, P. & Stolton, S. Guidelines for Applying Protected Area Management Categories Including IUCN WCPA Best Practice Guidance on Recognising Protected Areas and Assigning Management Categories and Governance Types. (IUCN, 2013).
Google Scholar 
54.Nelson, A. & Chomitz, K. M. Effectiveness of strict vs. Multiple use protected areas in reducing tropical forest fires: A global analysis using matching methods. PLoS ONE 6, e22722, https://doi.org/10.1371/journal.pone.0022722 (2011).55.Ferraro, P. J., Hanauer, M. M. & Sims, K. R. E. Conditions associated with protected area success in conservation and poverty reduction. Proc. Natl. Acad. Sci. https://doi.org/10.1073/pnas.1011529108 (2011).Article 
PubMed 
PubMed Central 

Google Scholar 
56.Oldekop, J. A., Holmes, G., Harris, W. E. & Evans, K. L. A global assessment of the social and conservation outcomes of protected areas. Conserv. Biol. 30, 133–141. https://doi.org/10.1111/cobi.12568 (2016).CAS 
Article 
PubMed 

Google Scholar 
57.Buchner, B. et al. The Global Landscape of Climate Finance 2015 (Climate Policy Initiative, 2015).
Google Scholar 
58.Climate Focus. Progress on the New York Declaration on Forests: Finance for Forests (Climate Focus, 2017).
Google Scholar 
59.Scharlemann, J. P. W. et al. Securing tropical forest carbon: The contribution of protected areas to redd. Oryx 44, 352–357 (2010).Article 

Google Scholar 
60.Hansen, M. C. et al. High-resolution global maps of 21st-century forest cover change. Science 342, 850–853. https://doi.org/10.1126/science.1244693 (2013).ADS 
CAS 
Article 
PubMed 

Google Scholar 
61.Curtis, P. G., Slay, C. M., Harris, N. L., Tyukavina, A. & Hansen, M. C. Classifying drivers of global forest loss. Science 361, 1108–1111. https://doi.org/10.1126/science.aau3445 (2018).ADS 
CAS 
Article 

Google Scholar 
62.Zarin, D. J. et al. Tree Biomass Loss: CO2 Emissions from Aboveground Woody Biomass Loss in the Tropics. www.globalforestwatch.org (2020).63.Coad, L. et al. Measuring impact of protected area management interventions: Current and future use of the global database of protected area management effectiveness. Philos. Trans. R. Soc. London B Biol. Sci. 370 (2015).64.Ho, D., Imai, K., King, G. & Stuart, E. Matchit: Nonparametric preprocessing for parametric causal inference. J. Stat. Softw. 42 (2011).65.Hosonuma, N. et al. An assessment of deforestation and forest degradation drivers in developing countries. Environ. Res. Lett. 7, 044009. https://doi.org/10.1088/1748-9326/7/4/044009 (2012).ADS 
Article 

Google Scholar 
66.Ewers, R. M. & Rodrigues, A. S. Estimates of reserve effectiveness are confounded by leakage. Trends Ecol. Evol. 23, 113–116 (2008).Article 

Google Scholar 
67.Oliveira, P. J. et al. Land-use allocation protects the peruvian amazon. Science 317, 1233–1236 (2007).ADS 
CAS 
Article 

Google Scholar 
68.Negret, P. J. et al. Effects of spatial autocorrelation and sampling design on estimates of protected area effectiveness. Conserv. Biol. 34, 1452–1462. https://doi.org/10.1111/cobi.13522 (2020).Article 
PubMed 
PubMed Central 

Google Scholar 
69.Miettinen, J., Shi, C., Tan, W. J. & Liew, S. C. 2010 land cover map of insular southeast asia in 250-m spatial resolution. Remote Sens. Lett. 3, 11–20. https://doi.org/10.1080/01431161.2010.526971 (2012).Article 

Google Scholar 
70.Stuart, E., Rubin, D. & Osborne, J. Best Practices in Quantitative Methods (Sage Publications, 2007).
Google Scholar 
71.Barton, K. & Barton, M. K. Package ‘mumin’. Version 1, 18 (2015).
Google Scholar  More

1.Pernthaler J. Predation on prokaryotes in the water column and its ecological implications. Nat Rev Microbiol. 2005;3:537–46.CAS 
PubMed 

Google Scholar 
2.Gast RJ, Sanders RW, Caron DA. Ecological strategies of protists and their symbiotic relationships with prokaryotic microbes. Trends Microbiol. 2009;17:563–9.CAS 
PubMed 

Google Scholar 
3.Wein T, Romero Picazo D, Blow F, Woehle C, Jami E, Reusch TBH, et al. Currency, exchange, and inheritance in the evolution of symbiosis. Trends Microbiol. 2019;27:836–49.CAS 
PubMed 

Google Scholar 
4.Ushida K, Newbold CJ, Jouany J-P. Interspecies hydrogen transfer between the rumen ciliate Polyplastron multivesiculatum and Methanosarcina barkeri. J Gen Appl Microbiol. 1997;43:129–31.CAS 
PubMed 

Google Scholar 
5.D’Souza G, Shitut S, Preussger D, Yousif G, Waschina S, Kost C. Ecology and evolution of metabolic cross-feeding interactions in bacteria. Nat Prod Rep. 2018;35:455–88.PubMed 

Google Scholar 
6.Graf JS, Schorn S, Kitzinger K, Ahmerkamp S, Woehle C, Huettel B, et al. Anaerobic endosymbiont generates energy for ciliate host by denitrification. Nature. 2021;591:445–50.CAS 
PubMed 
PubMed Central 

Google Scholar 
7.Bell T, Bonsall MB, Buckling A, Whiteley AS, Goodall T, Griffiths RI. Protists have divergent effects on bacterial diversity along a productivity gradient. Biol Lett. 2010;6:639–42.PubMed 
PubMed Central 

Google Scholar 
8.Johnke J, Baron M, de Leeuw M, Kushmaro A, Jurkevitch E, Harms H, et al. A generalist protist predator enables coexistence in multitrophic predator-prey systems containing a phage and the bacterial predator bdellovibriot. Front Ecol Evol. 2017;5:536.
Google Scholar 
9.Leibold MA. A graphical model of keystone predators in food webs: trophic regulation of abundance, incidence, and diversity patterns in communities. Am Nat. 1996;147:784–812.
Google Scholar 
10.Glücksman E, Bell T, Griffiths RI, Bass D. Closely related protist strains have different grazing impacts on natural bacterial communities. Environ Microbiol. 2010;12:3105–13.PubMed 

Google Scholar 
11.Espinoza-Vergara G, Hoque MM, McDougald D, Noorian P. The impact of protozoan predation on the pathogenicity of Vibrio cholerae. Front Microbiol. 2020;11:17.PubMed 
PubMed Central 

Google Scholar 
12.Gao Z, Karlsson I, Geisen S, Kowalchuk G, Jousset A. Protists: puppet masters of the rhizosphere microbiome. Trends Plant Sci. 2019;24:165–76.CAS 
PubMed 

Google Scholar 
13.Rosenberg K, Bertaux J, Krome K, Hartmann A, Scheu S, Bonkowski M. Soil amoebae rapidly change bacterial community composition in the rhizosphere of Arabidopsis thaliana. ISME J. 2009;3:675–84.CAS 
PubMed 

Google Scholar 
14.Chudnovskiy A, Mortha A, Kana V, Kennard A, Ramirez JD, Rahman A, et al. Host-protozoan interactions protect from mucosal infections through activation of the inflammasome. Cell. 2016;167:444–.e14.CAS 
PubMed 
PubMed Central 

Google Scholar 
15.Nieves-Ramírez ME, Partida-Rodríguez O, Laforest-Lapointe I, Reynolds LA, Brown EM, Valdez-Salazar A, et al. Asymptomatic intestinal colonization with protist blastocystis is strongly associated with distinct microbiome ecological patterns. mSystems. 2018;3:e00007–18.PubMed 
PubMed Central 

Google Scholar 
16.Mizrahi I. Rumen symbioses. In: Eugene Rosenberg, Edward F. DeLong, Stephen Lory, Erko Stackebrandt, Thompson F, editors. The Prokaryotes. Springer Berlin Heidelberg; 2013. p. 533–44.17.Sylvester JT, Karnati SKR, Yu Z, Morrison M, Firkins JL. Development of an assay to quantify rumen ciliate protozoal biomass in cows using real-time PCR. J Nutr. 2004;134:3378–84.CAS 
PubMed 

Google Scholar 
18.Newbold CJ, de la Fuente G, Belanche A, Ramos-Morales E, McEwan NR. The role of ciliate protozoa in the rumen. Front Microbiol. 2015;6:1313.PubMed 
PubMed Central 

Google Scholar 
19.Firkins JL, Yu Z, Park T, Plank JE. Extending Burk Dehority’s perspectives on the role of ciliate protozoa in the rumen. Front Microbiol. 2020;11:123.PubMed 
PubMed Central 

Google Scholar 
20.Williams AG, Coleman GS. The rumen protozoa. New York, NY: Springer Science & Business Media; 2012.21.Solomon R, Jami E. Rumen protozoa: from background actors to featured role in microbiome research. Environ Microbiol Rep. 2021;13:45–49.PubMed 

Google Scholar 
22.Shabat SKB, Sasson G, Doron-Faigenboim A, Durman T, Yaacoby S, Berg Miller ME, et al. Specific microbiome-dependent mechanisms underlie the energy harvest efficiency of ruminants. ISME J. 2016;10:2958.CAS 
PubMed 
PubMed Central 

Google Scholar 
23.Lima J, Auffret MD, Stewart RD, Dewhurst RJ, Duthie C-A, Snelling TJ, et al. Identification of rumen microbial genes involved in pathways linked to appetite, growth, and feed conversion efficiency in cattle. Front Genet. 2019;10:701.CAS 
PubMed 
PubMed Central 

Google Scholar 
24.Jami E, White BA, Mizrahi I. Potential role of the bovine rumen microbiome in modulating milk composition and feed efficiency. PLoS ONE. 2014;9:e85423.PubMed 
PubMed Central 

Google Scholar 
25.Delgado B, Bach A, Guasch I, González C, Elcoso G, Pryce JE, et al. Whole rumen metagenome sequencing allows classifying and predicting feed efficiency and intake levels in cattle. Sci Rep. 2019;9:11.PubMed 
PubMed Central 

Google Scholar 
26.Wallace RJ, Sasson G, Garnsworthy PC, Tapio I, Gregson E, Bani P, et al. A heritable subset of the core rumen microbiome dictates dairy cow productivity and emissions. Sci Adv. 2019;5:eaav8391.CAS 
PubMed 
PubMed Central 

Google Scholar 
27.Belanche A, de la Fuente G, Pinloche E, Newbold CJ, Balcells J. Effect of diet and absence of protozoa on the rumen microbial community and on the representativeness of bacterial fractions used in the determination of microbial protein synthesis. J Anim Sci. 2012;90:3924–36.CAS 
PubMed 

Google Scholar 
28.Belanche A, de la Fuente G, Moorby JM, Newbold CJ. Bacterial protein degradation by different rumen protozoal groups. J Anim Sci. 2012;90:4495–504.CAS 
PubMed 

Google Scholar 
29.Belanche A, de la Fuente G, Newbold CJ. Effect of progressive inoculation of fauna-free sheep with holotrich protozoa and total-fauna on rumen fermentation, microbial diversity and methane emissions. FEMS Microbiol Ecol. 2015;91:fiu026.PubMed 

Google Scholar 
30.Hackmann TJ, Firkins JL. Maximizing efficiency of rumen microbial protein production. Front Microbiol. 2015;6:465.PubMed 
PubMed Central 

Google Scholar 
31.Popova M, Martin C, Rochette Y, Graviou D, Morgavi DP. Methanogenesis kinetics and fermentation patterns in the rumen of sheep with or without protozoa. In: Ruminant physiology: digestion, metabolism and effects of nutrition on reproduction and welfare. Netherlands: Wageningen Academic publishers; 2009. 320.32.Levy B, Jami E. Exploring the prokaryotic community associated within the rumen ciliate protozoa population. Front Microbiol. 2018;9:2526.PubMed 
PubMed Central 

Google Scholar 
33.Borrel G, Brugère J-F, Gribaldo S, Schmitz RA, Moissl-Eichinger C. The host-associated archaeome. Nat Rev Microbiol. 2020;18:622–36.CAS 
PubMed 

Google Scholar 
34.Lloyd D, Williams AG, Amann R, Hayes AJ, Durrant L, Ralphs JR. Intracellular prokaryotes in rumen ciliate protozoa: Detection by confocal laser scanning microscopy after in situ hybridization with fluorescent 16S rRNA probes. Eur J Protistol. 1996;32:523–31.
Google Scholar 
35.Jouany JP. Effect of rumen protozoa on nitrogen utilization by ruminants. J Nutr. 1996;126:1335S–46S.CAS 
PubMed 

Google Scholar 
36.Coleman GS, Sandford DC. The engulfment and digestion of mixed rumen bacteria and individual bacterial species by single and mixed species of rumen ciliate protozoa grown in-vivo. J Agric Sci. 1979;92:729–42.
Google Scholar 
37.Zachut M, Honig H, Striem S, Zick Y, Boura-Halfon S, Moallem U. Periparturient dairy cows do not exhibit hepatic insulin resistance, yet adipose-specific insulin resistance occurs in cows prone to high weight loss. J Dairy Sci. 2013;96:5656–69.CAS 
PubMed 

Google Scholar 
38.National Research Council. 2001. Nutrient Requirements of Dairy Cattle: Seventh Revised Edition. Washington, DC: The National Academies Press; 2001.39.Bradford MM. A rapid and sensitive method for the quantitation of microgram quantities of protein utilizing the principle of protein-dye binding. Anal Biochem. 1976;72:248–54.CAS 
PubMed 

Google Scholar 
40.Stevenson DM, Weimer PJ. Dominance of Prevotella and low abundance of classical ruminal bacterial species in the bovine rumen revealed by relative quantification real-time PCR. Appl Microbiol Biotechnol. 2007;75:165–74.CAS 
PubMed 

Google Scholar 
41.NIH HMP Working Group, Peterson J, Garges S, Giovanni M, McInnes P, Wang L, et al. The NIH human microbiome project. Genome Res. 2009;19:2317–23.
Google Scholar 
42.Tapio I, Shingfield KJ, McKain N, Bonin A, Fischer D, Bayat AR, et al. Oral samples as non-invasive proxies for assessing the composition of the rumen microbial community. PLoS ONE. 2016;11:e0151220.PubMed 
PubMed Central 

Google Scholar 
43.Wobbrock JO, Findlater L, Gergle D, Higgins JJ. The aligned rank transform for nonparametric factorial analyses using only ANOVA procedures. Proceedings of the SIGCHI Conference on Human Factors in Computing Systems. New York, NY, USA: Association for Computing Machinery; 2011. p. 143–6.44.Elkin LA, Kay M, Higgins JJ, Wobbrock JO. An aligned rank transform procedure for multifactor contrast tests. https://arxiv.org/abs/2102.11824.45.Bolyen E, Rideout JR, Dillon MR, Bokulich NA, Abnet CC, Al-Ghalith GA, et al. Reproducible, interactive, scalable and extensible microbiome data science using QIIME 2. Nat Biotechnol. 2019;37:852–7.CAS 
PubMed 
PubMed Central 

Google Scholar 
46.Callahan BJ, McMurdie PJ, Rosen MJ, Han AW, Johnson AJA, Holmes SP. DADA2: high-resolution sample inference from Illumina amplicon data. Nat Methods. 2016;13:581–3.CAS 
PubMed 
PubMed Central 

Google Scholar 
47.Quast C, Pruesse E, Yilmaz P, Gerken J, Schweer T, Yarza P, et al. The SILVA ribosomal RNA gene database project: improved data processing and web-based tools. Nucleic Acids Res. 2013;41:D590–6.CAS 
PubMed 

Google Scholar 
48.Hammer Ø, Harper DAT, Ryan PD. PAST: paleontological statistics software package for education and data analysis. Palaeontol Electron. 2001;4:9.
Google Scholar 
49.Oksanen J. vegan: community ecology package. R package version 2.5-7. 2011. http://cran.r-project.org/package=vegan.50.van den Boogaart KG, Tolosana-Delgado R. ‘compositions’: a unified R package to analyze compositional data. Comput Geosci. 2008;34:320–38.
Google Scholar 
51.Krzywinski M, Altman N, Blainey P. Points of significance: nested designs. For studies with hierarchical noise sources, use a nested analysis of variance approach. Nat Methods. 2014;11:977–8.CAS 
PubMed 

Google Scholar 
52.R Core Team. R: A language and environment for statistical computing. Vienna, Austria: R Foundation for Statistical Computing; 2019.53.Chernomor O, von Haeseler A, Minh BQ. Terrace aware data structure for phylogenomic inference from supermatrices. Syst Biol. 2016;65:997–1008.PubMed 
PubMed Central 

Google Scholar 
54.Letunic I, Bork P. Interactive Tree Of Life (iTOL) v4: recent updates and new developments. Nucleic Acids Res. 2019;47:256–9.
Google Scholar 
55.Belanche A, de la Fuente G, Newbold CJ. Study of methanogen communities associated with different rumen protozoal populations. FEMS Microbiol Ecol. 2014;90:663–77.CAS 
PubMed 

Google Scholar 
56.Ungerfeld EM. Metabolic hydrogen flows in rumen fermentation: principles and possibilities of interventions. Front Microbiol. 2020;11:589.PubMed 
PubMed Central 

Google Scholar 
57.Bonder MJ, Kurilshikov A, Tigchelaar EF, Mujagic Z, Imhann F, Vila AV, et al. The effect of host genetics on the gut microbiome. Nat Genet. 2016;48:1407–12.CAS 
PubMed 

Google Scholar 
58.Henderson G, Cox F, Ganesh S, Jonker A, Young W, Global Rumen Census Collaborators, et al. Rumen microbial community composition varies with diet and host, but a core microbiome is found across a wide geographical range. Sci Rep.2015;5:1–15.
Google Scholar 
59.Fukami T. Historical contingency in community assembly: integrating niches, species pools, and priority effects. Annu Rev Ecol Evol Syst. 2015;46:1–23.
Google Scholar 
60.Shaani Y, Zehavi T, Eyal S, Miron J, Mizrahi I. Microbiome niche modification drives diurnal rumen community assembly, overpowering individual variability and diet effects. ISME J. 2018;12:2446–57.CAS 
PubMed 
PubMed Central 

Google Scholar 
61.Paul RG, Williams AG, Butler RD. Hydrogenosomes in the rumen entodiniomorphid ciliate Polyplastron multivesiculatum. J Gen Microbiol. 1990;136:1981–9.CAS 
PubMed 

Google Scholar 
62.Greening C, Geier R, Wang C, Woods LC, Morales SE, McDonald MJ, et al. Diverse hydrogen production and consumption pathways influence methane production in ruminants. ISME J. 2019;13:2617–32.CAS 
PubMed 
PubMed Central 

Google Scholar 
63.Gong J, Qing Y, Zou S, Fu R, Su L, Zhang X, et al. Protist-bacteria associations: gammaproteobacteria and alphaproteobacteria are prevalent as digestion-resistant bacteria in ciliated protozoa. Front Microbiol. 2016;7:498.PubMed 
PubMed Central 

Google Scholar 
64.Park T, Yu Z. Do ruminal ciliates select their preys and prokaryotic symbionts? Front Microbiol. 2018;9:1710.PubMed 
PubMed Central 

Google Scholar 
65.Matz C, Nouri B, McCarter L, Martinez-Urtaza J. Acquired type III secretion system determines environmental fitness of epidemic Vibrio parahaemolyticus in the interaction with bacterivorous protists. PLoS ONE. 2011;6:e20275.CAS 
PubMed 
PubMed Central 

Google Scholar 
66.Kamke J, Soni P, Li Y, Ganesh S, Kelly WJ, Leahy SC, et al. Gene and transcript abundances of bacterial type III secretion systems from the rumen microbiome are correlated with methane yield in sheep. BMC Res Notes. 2017;10:367.PubMed 
PubMed Central 

Google Scholar 
67.Jami E, Mizrahi I. Composition and similarity of bovine rumen microbiota across individual animals. PLoS ONE. 2012;7:e33306.CAS 
PubMed 
PubMed Central 

Google Scholar 
68.Brulc JM, Antonopoulos DA, Miller ME, Wilson MK, Yannarell AC, Dinsdale EA, et al. Gene-centric metagenomics of the fiber-adherent bovine rumen microbiome reveals forage specific glycoside hydrolases. Proc Natl Acad Sci USA. 2009;106:1948–53.CAS 
PubMed 
PubMed Central 

Google Scholar 
69.Indugu N, Vecchiarelli B, Baker LD, Ferguson JD, Vanamala JKP, Pitta DW. Comparison of rumen bacterial communities in dairy herds of different production. BMC Microbiol. 2017;17:190.PubMed 
PubMed Central 

Google Scholar 
70.Pope PB, Smith W, Denman SE, Tringe SG, Barry K, Hugenholtz P, et al. Isolation of Succinivibrionaceae implicated in low methane emissions from Tammar wallabies. Science. 2011;333:646–8.CAS 
PubMed 

Google Scholar 
71.Saleem M, Fetzer I, Dormann CF, Harms H, Chatzinotas A. Predator richness increases the effect of prey diversity on prey yield. Nat Commun. 2012;3:1305.PubMed 

Google Scholar 
72.Simek K, Vrba J, Pernthaler J, Posch T, Hartman P, Nedoma J, et al. Morphological and compositional shifts in an experimental bacterial community influenced by protists with contrasting feeding modes. Appl Environ Microbiol. 1997;63:587–95.CAS 
PubMed 
PubMed Central 

Google Scholar 
73.Socolar J, Washburne A. Prey carrying capacity modulates the effect of predation on prey diversity. Am Nat. 2015;186:333–47.PubMed 

Google Scholar 
74.Gutierrez J. Observations on bacterial feeding by the rumen ciliate Isotricha prostoma. J Protozool. 1958;5:122–6.
Google Scholar 
75.Coleman GS. The metabolism of Escherichia coli and other bacteria by Entodinium caudatum. J Gen Microbiol. 1964;37:209–23.CAS 
PubMed 

Google Scholar 
76.Canter EJ, Cuellar-Gempeler C, Pastore AI, Miller TE, Mason OU. Predator identity more than predator richness structures aquatic microbial assemblages in Sarracenia purpurea leaves. Ecology. 2018;99:652–60.PubMed 

Google Scholar 
77.Paine RT. Food web complexity and species diversity. Am Nat. 1966;100:65–75.
Google Scholar 
78.Audebert C, Even G, Cian A, Loywick A, Merlin S, Blastocystis Investigation Group,et al. Colonization with the enteric protozoa Blastocystis is associated with increased diversity of human gut bacterial microbiota. Sci Rep. 2016;6:25255.CAS 
PubMed 
PubMed Central 

Google Scholar 
79.Chabé M, Lokmer A, Ségurel L. Gut protozoa: friends or foes of the human gut microbiota? Trends Parasitol. 2017;33:925–34.PubMed 

Google Scholar 
80.Asgari M, Steiner CF. Interactive effects of productivity and predation on zooplankton diversity. Oikos. 2017;126:1617–24.CAS 

Google Scholar 
81.Tokura M, Ushida K, Miyazaki K, Kojima Y. Methanogens associated with rumen ciliates. FEMS Microbiol Ecol. 1997;22:137–43.CAS 

Google Scholar 
82.Irbis C, Ushida K. Detection of methanogens and proteobacteria from a single cell of rumen ciliate protozoa. J Gen Appl Microbiol. 2004;50:203–12.CAS 
PubMed 

Google Scholar 
83.Karakoç C, Radchuk V, Harms H, Chatzinotas A. Interactions between predation and disturbances shape prey communities. Sci Rep. 2018;8:2968.PubMed 
PubMed Central 

Google Scholar  More

1.Bond-Lamberty, B. & Thomson, A. Temperature-associated increases in the global soil respiration record. Nature 464, 579–582 (2010).CAS 

Google Scholar 
2.Raich, J. W., Potter, C. S. & Bhagawati, D. Interannual variability in global soil respiration, 1980–94. Glob. Change Biol. 8, 800–812 (2002).
Google Scholar 
3.Davidson, E. A. & Janssens, I. A. Temperature sensitivity of soil carbon decomposition feedbacks to climate change. Nature 440, 165–173 (2006).CAS 

Google Scholar 
4.Feng, X., Simpson, A. J., Wilson, K. P., Williams, D. D. & Simpson, M. J. Increased cuticular carbon sequestration and lignin oxidation in response to soil warming. Nat. Geosci. 1, 836–839 (2008).CAS 

Google Scholar 
5.Heimann, H. & Reichstein, R. Terrestrial ecosystem carbon dynamics and climate feedbacks. Nature 451, 289–292 (2008).CAS 

Google Scholar 
6.Fang, C. et al. Impacts of warming and nitrogen addition on soil autotrophic and heterotrophic respiration in a semi-arid environment. Agr. Forest Meteorol. 248, 449–457 (2018).
Google Scholar 
7.Wang, Q., Liu, S., Wang, Y., Tian, P. & Sun, T. Influences of N deposition on soil microbial respiration and its temperature sensitivity depend on N type in a temperate forest. Agr. Forest Meteorol. 260–261, 240–246 (2018).
Google Scholar 
8.Zhong, Y. Q. W., Yan, W. M., Zong, Y. Z. & Shangguan, Z. P. The effects of nitrogen enrichment on soil CO2 fluxes depending on temperature and soil properties. Global Ecol. Biogeogr. 25, 475–488 (2016).
Google Scholar 
9.Yu, G. R. et al. Stabilization of atmospheric nitrogen deposition in China over the past decade. Nat. Geosci. 12, 424–429 (2019).CAS 

Google Scholar 
10.Coucheney, E., Strömgren, M., Lerch, T. Z. & Herrmann, A. M. Long-term fertilization of a boreal Norway spruce forest increases the temperature sensitivity of soil organic carbon mineralization. Ecol. Evol. 3, 5177–5188 (2013).
Google Scholar 
11.Jiang, J. S., Guo, S. L., Wang, R., Liu, Q. F. & Sun, Q. Q. Effects of nitrogen fertilization on soil respiration and temperature sensitivity in spring maize field in semi-arid regions on loess plateau. Environ. Sci. 36, 1802–1809 (2015).
Google Scholar 
12.Wang, Q., Zhao, X., Tian, P., Liu, S. & Sun, Z. Bioclimate and arbuscular mycorrhizal fungi regulate continental biogeographic variations in effect of nitrogen deposition on the temperature sensitivity of soil organic carbon decomposition. Land Degrad. Dev. 32, 936–945 (2021).
Google Scholar 
13.Schindlbacher, A., Zechmeister-Boltenstern, S. & Jandl, R. Carbon losses due to soil warming: do autotrophic and heterotrophic soil respiration respond equally? Glob. Change Biol. 15, 901–903 (2009).
Google Scholar 
14.Carey, J. C. et al. Temperature response of soil respiration largely unaltered with experimental warming. Proc. Natl Acad. Sci. 113, 13797–13802 (2016).CAS 

Google Scholar 
15.Lyu, M., Giardina, C. P. & Litton, C. M. Interannual variation in rainfall modulates temperature sensitivity of carbon allocation and flux in a tropical montane wet forest. Glob. Change Biol. 27, 3824–3836 (2021).
Google Scholar 
16.Wang, Q. et al. Global synthesis of temperature sensitivity of soil organic carbon decomposition: latitudinal patterns and mechanisms. Funct. Ecol. 33, 514–523 (2019).
Google Scholar 
17.Li, J. et al. Biogeographic variation in temperature sensitivity of decomposition in forest soils. Glob. Change Biol. 26, 1873–1885 (2020).
Google Scholar 
18.Delgado-Baquerizo, M. et al. Palaeoclimate explains a unique proportion of the global variation in soil bacterial communities. Nat. Ecol. Evol. 1, 1339–1347 (2017).
Google Scholar 
19.Delgado-Baquerizo, M. et al. Climate legacies drive global soil carbon stocks in terrestrial ecosystems. Sci. Adv. 3, e1602008 (2017).
Google Scholar 
20.Delgado-Baquerizo, M. et al. Ecological drivers of soil microbial diversity and soil biological networks in the southern hemisphere. Ecology 99, 583–596 (2018).
Google Scholar 
21.Ding, J. Y. & Eldridge, D. J. Contrasting global effects of woody plant removal on ecosystem structure, function and composition. Perspect. Plant Ecol. 39, 125460 (2019).
Google Scholar 
22.Eldridge, D. J. & Delgado-Baquerizo, M. The influence of climatic legacies on the distribution of dryland biocrust communities. Glob. Change Biol. 25, 327–336 (2019).
Google Scholar 
23.Pärtel, M., Chiarucci, A., Chytrý, M. & Pillar, V. D. Mapping plant community ecology. J. Veg. Sci. 26, 1–3 (2017).
Google Scholar 
24.Richter, D. D. & Yaalon, D. H. “The changing model of soil” revisited. Soil Sci. Soc. Am. J. 76, 766–778 (2012).CAS 

Google Scholar 
25.Lyons, S. K. et al. Holocene shifts in the assembly of plant and animal communities implicate human impacts. Nature 529, 80–83 (2016).
Google Scholar 
26.Schmidt, M. W. I. et al. Persistence of soil organic matter as an ecosystem property. Nature 478, 49–56 (2011).CAS 

Google Scholar 
27.Delgado-Baquerizo, M. et al. Carbon content and climate variability drive global soil bacterial diversity patterns. Ecol. Monogr. 86, 373–390 (2016).
Google Scholar 
28.Maestre, F. T., Delgado-Baquerizo, M., Jeffries, T. C., Eldridge, D. J. & Singh, B. K. Increasing aridity reduces soil microbial diversity and abundance in global drylands. Proc. Natl Acad. Sci. 112, 15684–15689 (2015).CAS 

Google Scholar 
29.Monger, C. et al. Legacy effects in linked ecological–soil–geomorphic systems of drylands. Front. Ecol. Environ. 13, 13–19 (2016).
Google Scholar 
30.Cox, P. M., Betts, R. A., Jones, C. D., Spall, S. A. & Totterdell, I. J. Acceleration of global warming due to carbon-cycle feedbacks in a coupled climate model. Nature 408, 184–187 (2000).CAS 

Google Scholar 
31.Fierer, N., Colman, B. P., Schimel, J. P. & Jackson, R. B. Predicting the temperature dependence of microbial respiration in soil: a continental-scale analysis. Glob. Biogeochem. Cy. 20, GB3026 (2006).
Google Scholar 
32.Peng, S., Piao, S., Wang, T., Sun, J. & Shen, Z. Temperature sensitivity of soil respiration in different ecosystems in China. Soil Biol. Biochem. 41, 1008–1014 (2009).CAS 

Google Scholar 
33.Xu, Z. et al. Temperature sensitivity of soil respiration in China’s forest ecosystems: patterns and controls. Appl. Soil Ecol. 93, 105–110 (2015).
Google Scholar 
34.Niu, B. et al. Warming homogenizes apparent temperature sensitivity of ecosystem respiration. Sci. Adv. 7, eabc7358 (2021).
Google Scholar 
35.Janssens, I. A. et al. Reduction of forest soil respiration in response to nitrogen deposition. Nat. Geosci. 3, 315–322 (2010).CAS 

Google Scholar 
36.Yan, G. Y. et al. Sequestration of atmospheric CO2 in boreal forest carbon pools in northeastern China: Effects of nitrogen deposition. Agr. Forest Meteorol. 248, 70–81 (2018).
Google Scholar 
37.Du, E. Z. et al. Global patterns of terrestrial nitrogen and phosphorus limitation. Nat. Geosci. 13, 221–226 (2020).CAS 

Google Scholar 
38.Chen, Z. M. et al. Nitrogen fertilization stimulated soil heterotrophic but not autotrophic respiration in cropland soils: A greater role of organic over inorganic fertilizer. Soil Biol. Biochem. 116, 253–264 (2018).CAS 

Google Scholar 
39.Chen, F. et al. Effects of N addition and precipitation reduction on soil respiration and its components in a temperate forest. Agr. Forest Meteorol. 271, 336–345 (2019).
Google Scholar 
40.Zhang, C. et al. Effects of simulated nitrogen deposition on soil respiration components and their temperature sensitivities in a semiarid grassland. Soil Biol. Biochem. 75, 113–123 (2014).CAS 

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

Google Scholar 
42.Li, Y. et al. Soil acid cations induced reduction in soil respiration under nitrogen enrichment and soil acidification. Sci. Total Environ. 615, 1535–1546 (2018).CAS 

Google Scholar 
43.Sanderman, J. Comment on “Climate legacies drive global soil carbon stocks in terrestrial ecosystems”. Sci. Adv. 4, e1701482 (2018).
Google Scholar 
44.Ding, J. et al. The paleoclimatic footprint in the soil carbon stock of the Tibetan permafrost region. Nat. Commun. 10, 4195 (2019).
Google Scholar 
45.Gershenson, A., Bader, N. E. & Cheng, W. X. Effects of substrate availability on the temperature sensitivity of soil organic matter decomposition. Glob. Change Biol. 15, 176–183 (2009).
Google Scholar 
46.Doetterl, S. et al. Soil carbon storage controlled by interactions between geochemistry and climate. Nat. Geosci. 8, 780–783 (2015).CAS 

Google Scholar 
47.Bellard, C., Bertelsmeier, C., Leadley, P., Thuiller, W. & Courchamp, F. Impacts of climate change on the future of biodiversity. Ecol. Lett. 15, 365–377 (2012).
Google Scholar 
48.Li, J., Ziegler, S. E., Lane, C. S. & Billings, S. A. Legacies of native climate regime govern responses of boreal soil microbes to litter stoichiometry and temperature. Soil Biol. Biochem. 66, 204–213 (2013).CAS 

Google Scholar 
49.Xu, M. et al. High microbial diversity stabilizes the responses of soil organic carbon decomposition to warming in the subsoil on the Tibetan Plateau. Glob. Chang. Biol. 27, 2061–2075 (2021).
Google Scholar 
50.Du, Y. et al. The response of soil respiration to precipitation change is asymmetric and differs between grasslands and forests. Glob. Chang. Biol. 26, 6015–6024 (2020).
Google Scholar 
51.Meier, I. C. & Leuschner, C. Leaf size and leaf area index in Fagus sylvatica forests: competing effects of precipitation, temperature, and nitrogen availability. Ecosystems 11, 655–669 (2008).CAS 

Google Scholar 
52.Li, J., Pei, J., Pendall, E., Fang, C. & Nie, M. Spatial heterogeneity of temperature sensitivity of soil respiration: A global analysis of field observations. Soil Biol. Biochem. 141, 107675 (2020).CAS 

Google Scholar 
53.Katz, M. H. Multivariable Analysis: A Practical Guide for Clinicians and Public Health Researchers (Cambridge Univ. Press, Cambridge, 2006).54.Leff, J. W. et al. Consistent responses of soil microbial communities to elevated nutrient inputs in grasslands across the globe. Proc. Natl Acad. Sci. 112, 10967–10972 (2015).CAS 

Google Scholar 
55.Grace, J. B. Structural Equation Modeling Natural Systems (Cambridge Univ. Press, Cambridge, 2006).56.Lefcheck, J. S. PiecewiseSEM: piecewise structural equation modelling in r for ecology, evolution, and systematics. Methods Ecol. Evol 7, 573–579 (2016).
Google Scholar 
57.Bates, D. et al. lme4: Linear mixed-effects models using Eigen and S4. R package version 1, 1–13 (2017).
Google Scholar  More

Due to contrasts in oceanographic properties along the NAP24, the sampling grid was split in two subregions: north and south (Fig. 1; see “Methods”). The north and south subregions showed from the satellite data a much higher spring/summer (November–February) mean chlorophyll-a (Chl-a) in 2015/2016 than the decadal average time series (2010–2019; Table 1). In agreement with the El Niño effects10,16, the sea surface temperature (SST) and the air temperature showed substantially lower mean values during the spring/summer of 2015/2016 along the subregions (Table 1). However, there was an evident spatial/temporal variability in sea ice concentration/duration between the subregions, with a northward (southward) lower (higher) mean value during 2015/2016 in relation to the decadal average (Table 1). Along the south subregion during the spring/summer of 2015/2016, the increased Chl-a during January followed the decline in the sea ice concentration over the spring and early summer, concurrent with increased SST, which was markedly colder throughout the seasonal phytoplankton succession (Fig. 2a). These results to the south subregion are consistent with previous studies along the WAP, in which years characterized by longer sea ice cover in winter have led to higher phytoplankton biomass in the following summer associated with a more stable water column11,16,26. To the north subregion, however, although there was a similar pattern between Chl-a and SST, the increased Chl-a during January was not related with the sea ice retreat (Fig. 2b). Moreover, there was a clear difference between the Chl-a peaks (the highest Chl-a value reached) along the subregions from the satellite data. The Chl-a peak in the south subregion occurred in early January (10 January 2016, reaching 1.73 mg m–3), whereas in the north subregion the Chl-a peak was observed in late January (29 January 2016, reaching 2.23 mg m–3).Fig. 1: Study area.Location of hydrographic stations is marked by open circles (November), stars (January), and blue circles (February). The black dashed lines indicate the subregions (north and south) along the NAP and delimit the areas used to estimate average remote sensing measurements. The decadal-mean (2010–2019) remote sensing chlorophyll-a (Chl-a) is exhibited in the background, indicating the biomass (Chl-a) distribution of phytoplankton along the NAP in the last decade. An inset map in the lower right corner shows the location of the NAP within the Atlantic sector of the Southern Ocean.Full size imageTable 1 Biological production and ocean/atmosphere parameters by measurements of remote sensing and local meteorological stations during spring/summer in the NAP subregions.Full size tableFig. 2: Biological production and sea ice dynamics in the NAP seasonal phytoplankton succession of 2015/2016.Continuum remote sensing measurements of chlorophyll-a (Chl-a; solid green line), sea surface temperature (SST; solid blue line), and sea ice concentration (gray area) along the NAP, in south (a) and north (b) subregions during spring/summer of 2015/2016. The dashed green, blue and gray lines indicate the decadal average (2010–2019) of Chl-a, SST, and sea ice concentration, respectively. The solid light green lines represent the Chl-a interpolated values. The background shades show the in situ data sampling periods. It is important to note that Chl-a remote sensing data in Antarctic coastal waters are typically underestimated in respect to in situ Chl-a data (see Supplementary Fig. 1)12,29.Full size imageIt has been estimated that drifters entrained in the Gerlache Strait Current and the Bransfield Strait Current exit the Bransfield Strait in 10–20 days17, which is consistent with the interval of 19 days between both Chl-a peaks when considering the extreme distance between the subregions (see Fig. 1). These authors also estimated that drifters deployed in the Gerlache Strait Current were quickly advected out of the Gerlache Strait in less than 1 week (i.e., low residence time)17, which supports the similar diatom species assemblages identified in our microscopic analysis between stations of the Gerlache Strait and southwestern Bransfield Strait24. Therefore, it is plausible that phytoplankton growth in the north of the Gerlache Strait may be laterally advected northward into the Bransfield Strait, explaining the observed concomitant increase of satellite Chl-a data in both subregions from spring, associated with sea ice retreat southward (Fig. 2). In addition, as phytoplankton biomass tends to accumulate northward17,27,28, the advection processes could also explain the temporal and intensity differences of the Chl-a peaks along the subregions (see Fig. 2). This suggests that there was a link between the sea ice dynamics, phytoplankton biomass (Chl-a) and advection processes along the NAP during the spring/summer of 2015/2016, in which the sea ice melting first triggered an increase in phytoplankton biomass through water column stratification along the south subregion, and the advection processes led to a subsequent increase northward.The satellite Chl-a data require extensive validation with in situ data, especially in polar regions, where cloud cover is ubiquitous and performance is typically poor, due to not properly accurate Chl-a algorithms12,29. For that, although the mean Chl-a in 2015/2016 from the satellite data was approximately twice as large as the decadal average, there was a severe discrepancy in the mean Chl-a values observed between the in situ and remote sensing data (see Table 1 and Supplementary Table 1). This highlights the importance of the in situ dataset reported here, especially evident during February 2016, when the signal of an intense diatom bloom ( > 40 mg m–3 Chl-a)24 was not captured in the satellite data (Supplementary Fig. 1), supporting that phytoplankton biomass accumulation during this summer was much higher than recorded by remote sensing observations (see Table 1). In general, the in situ Chl-a achieved its maximum (40 mg m–3) and higher mean value (17.4 mg m–3) during February comparing to November and January (Supplementary Table 1).Phytoplankton community structure during the spring/summer of 2015/2016 was assessed through Chemical taxonomy (CHEMTAX) software, using accessory pigments versus in situ Chl-a concentrations measured via high-performance liquid chromatography (HPLC; see “Methods”). The main phytoplankton group over the season were diatoms, followed by haptophytes (Phaeocystis antarctica), cryptophytes, and dinoflagellates, according to the succession stage (Fig. 3a). Diatoms dominated the phytoplankton community composition in relation to the other groups along the whole in situ sampling period, although their relative biomass (to the total in situ Chl-a) was lower in some stations compared to others in different moments during spring/summer (Fig. 3a). To assess the degree to which the water column structure was a primary driver for development and intensity of diatom growth3,24, the mixed layer depth (MLD) and water column stability were calculated as a function of seawater potential density (see “Methods”). There was an inverse polynomial relationship between MLD and mean upper ocean stability (averaged over 5−150 m depth; hereafter referred to as upper ocean stability) (Fig. 3b). The significant positive exponential relationship between the upper ocean stability and diatom absolute concentrations (in situ Chl-a) demonstrates that stability, associated with MLD, was an important driver of diatom dynamics (Fig. 3b). This elucidates the increase in biological production during summer months of 2016, when upper ocean physical structures (MLD and stability) were sufficiently shallow and stable to produce the high phytoplankton biomass (in situ Chl-a) registered here. However, as MLD and stability showed similar values between summer months (Supplementary Table 1), only the upper ocean physical structures cannot be accounted for the high differences of in situ Chl-a values observed between diatom blooms in January (maximum of 12 mg m–3) and February (maximum of 40 mg m–3). Likewise, also not explaining these differences of in situ Chl-a values between summer months, macronutrients were highly abundant throughout the seasonal phytoplankton succession (Supplementary Table 1). Furthermore, although no measurements of dissolved iron, which can be considered as a limiting factor to primary productivity30, were carried out here, the Antarctic Peninsula continental shelves have been depicted as a substantial source of this micronutrient to the upper ocean, not limiting phytoplankton growth even during intense blooms31,32.Fig. 3: Phytoplankton community composition and upper ocean physical structures along the NAP seasonal phytoplankton succession of 2015/2016.a Relative biomass (to the total in situ chlorophyll-a; Chl-a) distribution of phytoplankton groups on surface, via HPLC/CHEMTAX analysis, during spring/summer of 2015/2016 along the NAP subregions. The black open circles indicate diatoms, the blue squares indicate Phaeocystis antarctica, the gray diamonds with crosses indicate cryptophytes, the green triangles indicate dinoflagellates, and the light gray open circles indicate green flagellates. b Exponential curve (R2 = 0.57; p 40% the community composition proportion in respect to the total Chl-a (considering the three fractionated size classes). Symbol color indicates the sampling month in respect to November (brown), January (gray), and February (black). The inset shows the polynomial inverse relationship (R2 = 0.51; p  70 µm in length; ref. 24), during January a large number ( > 2.5 × 106 cells L–1) of small ( More

Data sourcesEighteen rice-producing countries were selected for our analysis (Supplementary Table 1). Those countries account for 88 and 86% of global rice production and harvested rice area2, respectively (FAOSTAT, 2015–2017). We followed two steps to select the dominant cropping systems in each country. Within each country, our study focused on the main rice-producing area(s) (Supplementary Tables 2 and 3). For example, in the case of Brazil, we selected the southern and northern regions, which together account for nearly all rice production in this country. In the case of Vietnam, we selected the Mekong Delta region, which accounts for nearly 60% of national rice production57. While we tried to cover all major rice cropping systems in each country, this was not possible in the case of rainfed lowland rice cropping systems in northeastern Thailand and eastern India because of lack of reliable estimates of yield potential and access to farmer yield and management data. Once the main rice-producing region(s) in each country was (were) identified, we then determined the dominant rice cropping system(s) for each of them (Supplementary Table 3). We note that “cropping system” refers to a unique combination of a number of rice crops planted on the same piece of land within a 12-month period (and their temporal arrangement), water regime (rainfed or irrigated), and ecosystem (upland or lowland) (Supplementary Fig. 1 and Supplementary Table 2). In our study, rice cropping systems are single-, double-, or triple-season rice; none of the cropping systems are ratoon rice. Following the previous examples, two cropping systems were selected for Brazil (rainfed upland single rice and lowland irrigated single rice in the northern and southern regions, respectively) and two systems (double and triple) were selected for the Mekong Delta region in Vietnam. These systems account for nearly all rice harvested areas in these regions. We distinguished between rice-based cropping systems sowing hybrid versus inbred cultivars in the southern USA. Across the 18 countries, this study included a total of 32 rice cropping systems, which, in turn, covered 51% of the global rice harvested area (Supplementary Tables 1 and 3). Note that the area coverage reported here corresponds to that accounted by 32 cropping systems (and not by the countries where the cropping systems were located). These systems portrayed a wide range of biophysical and socio-economic backgrounds (Supplementary Figs. 1 and 2 and Supplementary Tables 1 and 2), leading to average rice yields ranging from 2–10.4 Mg ha−1 (Supplementary Fig. 3). For data analysis purposes, rice cropping systems were classified into tropical and non-tropical9,58,59 and also based upon water regime and crop season.Agronomic information was collected via structure questionnaires completed by agricultural specialists in each country or region (Supplementary Table 6). The collected data included field size, tillage method, crop establishment method, degree of mechanization for each field operation, seeding rate, crop establishment, and harvest dates, nutrient fertilizer rates, manure type, and rate, pesticides (number of applications, products, and rates), irrigation amount (in irrigated systems), energy source for irrigation pumping, labor input, and straw management (Supplementary Tables 4 and 5). Average values for each cropping system reported by country experts were retrieved from survey data available from previous projects (Supplementary Table 7). Rice grain yield was reported at a standard moisture content of 140 g H2O kg−1 grain, separately for each crop cycle, using data from, at least, three recent cropping seasons in each cropping system. In the case of irrigated rice cropping system in Nigeria and Mali, data were only available for one crop cycle in double-season rice. In this case, we assumed management and actual yield to be identical in the two crop cycles.In all cases, and wherever possible, data were cross-validated with other independent datasets (e.g., FAOSTAT, World Bank, IFA, and published journal papers), which gives confidence about the representativeness and accuracy of the survey data. For example, we estimated area-weighted national yield according to actual yield provided for each cropping system and annual rice harvested area in each system for each of the 18 countries. Comparison of these yields against those reported by FAOSTAT2 showed a strong association and agreement between data sources (Supplementary Fig. 10). We also cross-validated actual yield, N fertilizer, labor, and irrigation from our database with those reported by previous studies (published after the year 2000) based on on-farm data collected in ten selected countries. Due to the lack of on-farm data on irrigation, we used published data collected from experiments that follow typical farmer irrigation practices. In the case of irrigation, our cross-validation differentiated between crop seasons (wet versus dry) in the case of irrigated double-season rice cropping systems. In all cases, average yield, N fertilizer, labor, and irrigation from our database fell within (or very close) the range of values reported in previously published studies for those same cropping systems (Supplementary Table 8). Measured daily weather data, including daily solar radiation, minimum and maximum temperatures, and precipitation, were derived from representative weather stations in each region (Supplementary Fig. 2 and Supplementary Table 9). Data on per-capita gross domestic product (GDP) during 2015–2017 were retrieved for each country to explore relationships between yield gap and economic development60 (Supplementary Fig. 9 and Supplementary Table 1).Estimation of yield gapsThe yield gap is defined as the difference between yield potential and average farmer yield. Estimates of yield potential for irrigated rice or water-limited yield potential for rainfed rice were adopted from Global Yield Gap Atlas (GYGA)61 (Supplementary Table 7). Yield potential simulation in GYGA was performed using crop growth and development model ORYZA2000 or ORYZA (v3) (except for APSIM in the case of India) and based on actual data on crop management, soil data, measured daily weather data, and representative rice varieties planted in each region (see details for yield potential simulation in Supplementary Information Text Section 1). Data on yield potential were not available for Australia (AUIS) in GYGA; hence, we used estimates of yield potential from Lacy et al.62. Yield potential (or water-limited yield potential for rainfed rice) and average yields were computed separately for each rice crop in each rice cropping system (Supplementary Fig. 3). The coefficient of variation (CV) of yield potential (or water-limited yield potential) was estimated for each cropping system (Supplementary Fig. 4). In this study, average rice yield was expressed as percentage of the yield potential (or water-limited yield potential for rainfed rice) for each cropping system (Fig. 1 and Supplementary Fig. 5). In those cropping systems where more than one rice crop is grown within a 12-month period, we estimated yield potential and average yield on both per-crop and annual basis by averaging and summing up the estimates for each rice crop, respectively. In the case of per-crop averages, for those cropping systems in which the harvested rice area changed between crop cycles, we weighted the values for each cycles based on the associated harvested rice area. However, for simplicity, the main text reports only the values on a per-crop basis; annual estimates are provided in the Supplementary Information. Normalizing average yield by the yield potential at each site provides a direct comparison of yield gap closure across systems with diverse biophysical backgrounds (e.g., variation in solar radiation, temperature, and water supply). Without this normalization, one might make biased assessment in relation to the available room for improving yield. For example, an actual yield of 8 Mg ha−1 is equivalent to 80% of yield potential in the cropping system of central China, whereas a yield of 8 Mg ha−1 achieved by irrigated rice farmers in Brazil only represents 55% of yield potential (Supplementary Fig. 3).Quantifying resource-use efficiencyWe assessed the performance of rice production by calculating the following metrics: global warming potential (GWP), fossil-fuel energy inputs, water supply (irrigation plus in-season precipitation), number of pesticide applications, nitrogen (N) balance, and labor input, each expressed on an area and yield-scaled basis (Figs. 2, 3 and 4 and Supplementary Figs. 6, 7 and 11). We estimated metrics on both per-crop and annual basis and report the values on a per-crop basis in the main text while the annual estimates are provided in the Supplementary Information. In the case of GWP, it includes CO2, CH4, and N2O emissions (expressed as CO2-eq) from (i) production, packaging, and transportation of agricultural inputs (seed, fertilizer, pesticides, machinery, etc.), (ii) fossil-fuel energy directly used for farm operations (including irrigation pumping), and (iii) CH4 and N2O emission during rice cultivation63. Emissions from agricultural inputs were calculated on application rates and associated GHG emissions factors (see details in Supplementary Information Text Section 2, Supplementary Table 10). In the case of fossil fuel used for field operations, it was calculated based on the number and type of farm operations and associated fuel requirements (Supplementary Table 11). Total N2O emissions were calculated as the sum of direct and indirect N2O emissions. A previous meta-analysis including rice showed that direct soil N2O emissions can be estimated from the magnitude of N-surplus, which was calculated as applied N inputs minus accumulated N in aboveground biomass at physiological maturity21. Therefore, direct soil N2O emissions for a given rice crop cycle were estimated following van Groenigen et al. N-balance approach21. Indirect N2O emissions were estimated based on the Intergovernmental Panel on Climate Change (IPCC) methodology64, assuming indirect N2O emissions represent 20% of direct N2O emissions. The CH4 emissions from rice paddy field were calculated following IPCC65. Following this approach, CH4 emissions are estimated considering the duration of the rice cultivation period, water regime during the cultivation period and during the pre-season before the cultivation period, and type and amount of organic amendment applied (e.g., straw, manure, compost) based on a baseline emission factor. We assumed no net change in soil carbon stocks as soil organic matter is typically at steady state in lowland rice66. We did not attempt to estimate changes on soil C in the upland rice system in Brazil. All emissions were converted to CO2-eq, with GWP for CH4 set at 25 relatives to CO2 and 298 for N2O on a per mass basis over a 100-year time horizon67. For each rice crop cycle in each of the 32 rice systems, GWP was calculated as the sum of CO2, CH4, and N2O emissions expressed as CO2-eq. (Details on N2O and CH4 emissions estimates and GWP calculations are provided in Supplementary Information Text Section 2).Calculation of energy inputs was similar to that of GWP and was based on the reported rates of agricultural inputs and field operations and associated embodied energy (see details for energy input estimates in Supplementary Information Text Section 2, Supplementary Table 12). Human labor was also included in the calculation of energy inputs. There was a strong positive relationship between energy input and GWP on both per-crop (r = 0.81; p  More

.readcube-buybox { display: none !important;}

Two of the three native reptiles on to Gran Canaria have nearly vanished from some parts of the Spanish island — eaten by an invasive snake species originally imported as a pet1.

Access options

Access through your institution

Change institution

Buy or subscribe

/* style specs start */
style{display:none!important}.LiveAreaSection-193358632 *{align-content:stretch;align-items:stretch;align-self:auto;animation-delay:0s;animation-direction:normal;animation-duration:0s;animation-fill-mode:none;animation-iteration-count:1;animation-name:none;animation-play-state:running;animation-timing-function:ease;azimuth:center;backface-visibility:visible;background-attachment:scroll;background-blend-mode:normal;background-clip:borderBox;background-color:transparent;background-image:none;background-origin:paddingBox;background-position:0 0;background-repeat:repeat;background-size:auto auto;block-size:auto;border-block-end-color:currentcolor;border-block-end-style:none;border-block-end-width:medium;border-block-start-color:currentcolor;border-block-start-style:none;border-block-start-width:medium;border-bottom-color:currentcolor;border-bottom-left-radius:0;border-bottom-right-radius:0;border-bottom-style:none;border-bottom-width:medium;border-collapse:separate;border-image-outset:0s;border-image-repeat:stretch;border-image-slice:100%;border-image-source:none;border-image-width:1;border-inline-end-color:currentcolor;border-inline-end-style:none;border-inline-end-width:medium;border-inline-start-color:currentcolor;border-inline-start-style:none;border-inline-start-width:medium;border-left-color:currentcolor;border-left-style:none;border-left-width:medium;border-right-color:currentcolor;border-right-style:none;border-right-width:medium;border-spacing:0;border-top-color:currentcolor;border-top-left-radius:0;border-top-right-radius:0;border-top-style:none;border-top-width:medium;bottom:auto;box-decoration-break:slice;box-shadow:none;box-sizing:border-box;break-after:auto;break-before:auto;break-inside:auto;caption-side:top;caret-color:auto;clear:none;clip:auto;clip-path:none;color:initial;column-count:auto;column-fill:balance;column-gap:normal;column-rule-color:currentcolor;column-rule-style:none;column-rule-width:medium;column-span:none;column-width:auto;content:normal;counter-increment:none;counter-reset:none;cursor:auto;display:inline;empty-cells:show;filter:none;flex-basis:auto;flex-direction:row;flex-grow:0;flex-shrink:1;flex-wrap:nowrap;float:none;font-family:initial;font-feature-settings:normal;font-kerning:auto;font-language-override:normal;font-size:medium;font-size-adjust:none;font-stretch:normal;font-style:normal;font-synthesis:weight style;font-variant:normal;font-variant-alternates:normal;font-variant-caps:normal;font-variant-east-asian:normal;font-variant-ligatures:normal;font-variant-numeric:normal;font-variant-position:normal;font-weight:400;grid-auto-columns:auto;grid-auto-flow:row;grid-auto-rows:auto;grid-column-end:auto;grid-column-gap:0;grid-column-start:auto;grid-row-end:auto;grid-row-gap:0;grid-row-start:auto;grid-template-areas:none;grid-template-columns:none;grid-template-rows:none;height:auto;hyphens:manual;image-orientation:0deg;image-rendering:auto;image-resolution:1dppx;ime-mode:auto;inline-size:auto;isolation:auto;justify-content:flexStart;left:auto;letter-spacing:normal;line-break:auto;line-height:normal;list-style-image:none;list-style-position:outside;list-style-type:disc;margin-block-end:0;margin-block-start:0;margin-bottom:0;margin-inline-end:0;margin-inline-start:0;margin-left:0;margin-right:0;margin-top:0;mask-clip:borderBox;mask-composite:add;mask-image:none;mask-mode:matchSource;mask-origin:borderBox;mask-position:0% 0%;mask-repeat:repeat;mask-size:auto;mask-type:luminance;max-height:none;max-width:none;min-block-size:0;min-height:0;min-inline-size:0;min-width:0;mix-blend-mode:normal;object-fit:fill;object-position:50% 50%;offset-block-end:auto;offset-block-start:auto;offset-inline-end:auto;offset-inline-start:auto;opacity:1;order:0;orphans:2;outline-color:initial;outline-offset:0;outline-style:none;outline-width:medium;overflow:visible;overflow-wrap:normal;overflow-x:visible;overflow-y:visible;padding-block-end:0;padding-block-start:0;padding-bottom:0;padding-inline-end:0;padding-inline-start:0;padding-left:0;padding-right:0;padding-top:0;page-break-after:auto;page-break-before:auto;page-break-inside:auto;perspective:none;perspective-origin:50% 50%;pointer-events:auto;position:static;quotes:initial;resize:none;right:auto;ruby-align:spaceAround;ruby-merge:separate;ruby-position:over;scroll-behavior:auto;scroll-snap-coordinate:none;scroll-snap-destination:0 0;scroll-snap-points-x:none;scroll-snap-points-y:none;scroll-snap-type:none;shape-image-threshold:0;shape-margin:0;shape-outside:none;tab-size:8;table-layout:auto;text-align:initial;text-align-last:auto;text-combine-upright:none;text-decoration-color:currentcolor;text-decoration-line:none;text-decoration-style:solid;text-emphasis-color:currentcolor;text-emphasis-position:over right;text-emphasis-style:none;text-indent:0;text-justify:auto;text-orientation:mixed;text-overflow:clip;text-rendering:auto;text-shadow:none;text-transform:none;text-underline-position:auto;top:auto;touch-action:auto;transform:none;transform-box:borderBox;transform-origin:50% 50% 0;transform-style:flat;transition-delay:0s;transition-duration:0s;transition-property:all;transition-timing-function:ease;vertical-align:baseline;visibility:visible;white-space:normal;widows:2;width:auto;will-change:auto;word-break:normal;word-spacing:normal;word-wrap:normal;writing-mode:horizontalTb;z-index:auto;-webkit-appearance:none;-moz-appearance:none;-ms-appearance:none;appearance:none;margin:0}.LiveAreaSection-193358632{width:100%}.LiveAreaSection-193358632 .login-option-buybox{display:block;width:100%;font-size:17px;line-height:30px;color:#222;padding-top:30px;font-family:Harding,Palatino,serif}.LiveAreaSection-193358632 .additional-access-options{display:block;font-weight:700;font-size:17px;line-height:30px;color:#222;font-family:Harding,Palatino,serif}.LiveAreaSection-193358632 .additional-login >li:not(:first-child)::before{transform:translateY(-50%);content:”;height:1rem;position:absolute;top:50%;left:0;border-left:2px solid #999}.LiveAreaSection-193358632 .additional-login >li:not(:first-child){padding-left:10px}.LiveAreaSection-193358632 .additional-login >li{display:inline-block;position:relative;vertical-align:middle;padding-right:10px}.BuyBoxSection-683559780{display:flex;flex-wrap:wrap;flex:1;flex-direction:row-reverse;margin:-30px -15px 0}.BuyBoxSection-683559780 .box-inner{width:100%;height:100%}.BuyBoxSection-683559780 .readcube-buybox{background-color:#f3f3f3;flex-shrink:1;flex-grow:1;flex-basis:255px;background-clip:content-box;padding:0 15px;margin-top:30px}.BuyBoxSection-683559780 .subscribe-buybox{background-color:#f3f3f3;flex-shrink:1;flex-grow:4;flex-basis:300px;background-clip:content-box;padding:0 15px;margin-top:30px}.BuyBoxSection-683559780 .title-readcube{display:block;margin:0;margin-right:20%;margin-left:20%;font-size:24px;line-height:32px;color:#222;padding-top:30px;text-align:center;font-family:Harding,Palatino,serif}.BuyBoxSection-683559780 .title-buybox{display:block;margin:0;margin-right:29%;margin-left:29%;font-size:24px;line-height:32px;color:#222;padding-top:30px;text-align:center;font-family:Harding,Palatino,serif}.BuyBoxSection-683559780 .title-asia-buybox{display:block;margin:0;margin-right:5%;margin-left:5%;font-size:24px;line-height:32px;color:#222;padding-top:30px;text-align:center;font-family:Harding,Palatino,serif}.BuyBoxSection-683559780 .asia-link{color:#069;cursor:pointer;text-decoration:none;font-size:1.05em;font-family:-apple-system,BlinkMacSystemFont,”Segoe UI”,Roboto,Oxygen-Sans,Ubuntu,Cantarell,”Helvetica Neue”,sans-serif;line-height:1.05em6}.BuyBoxSection-683559780 .access-readcube{display:block;margin:0;margin-right:10%;margin-left:10%;font-size:14px;color:#222;padding-top:10px;text-align:center;font-family:-apple-system,BlinkMacSystemFont,”Segoe UI”,Roboto,Oxygen-Sans,Ubuntu,Cantarell,”Helvetica Neue”,sans-serif;line-height:20px}.BuyBoxSection-683559780 .access-asia-buybox{display:block;margin:0;margin-right:5%;margin-left:5%;font-size:14px;color:#222;padding-top:10px;text-align:center;font-family:-apple-system,BlinkMacSystemFont,”Segoe UI”,Roboto,Oxygen-Sans,Ubuntu,Cantarell,”Helvetica Neue”,sans-serif;line-height:20px}.BuyBoxSection-683559780 .access-buybox{display:block;margin:0;margin-right:30%;margin-left:30%;font-size:14px;color:#222;opacity:.8px;padding-top:10px;text-align:center;font-family:-apple-system,BlinkMacSystemFont,”Segoe UI”,Roboto,Oxygen-Sans,Ubuntu,Cantarell,”Helvetica Neue”,sans-serif;line-height:20px}.BuyBoxSection-683559780 .price-buybox{display:block;font-size:30px;color:#222;font-family:-apple-system,BlinkMacSystemFont,”Segoe UI”,Roboto,Oxygen-Sans,Ubuntu,Cantarell,”Helvetica Neue”,sans-serif;padding-top:30px;text-align:center}.BuyBoxSection-683559780 .price-from{font-size:14px;padding-right:10px;color:#222;font-family:-apple-system,BlinkMacSystemFont,”Segoe UI”,Roboto,Oxygen-Sans,Ubuntu,Cantarell,”Helvetica Neue”,sans-serif;line-height:20px}.BuyBoxSection-683559780 .issue-buybox{display:block;font-size:13px;text-align:center;color:#222;font-family:-apple-system,BlinkMacSystemFont,”Segoe UI”,Roboto,Oxygen-Sans,Ubuntu,Cantarell,”Helvetica Neue”,sans-serif;line-height:19px}.BuyBoxSection-683559780 .no-price-buybox{display:block;font-size:13px;line-height:18px;text-align:center;padding-right:10%;padding-left:10%;padding-bottom:20px;padding-top:30px;color:#222;font-family:-apple-system,BlinkMacSystemFont,”Segoe UI”,Roboto,Oxygen-Sans,Ubuntu,Cantarell,”Helvetica Neue”,sans-serif}.BuyBoxSection-683559780 .vat-buybox{display:block;margin-top:5px;margin-right:20%;margin-left:20%;font-size:11px;color:#222;padding-top:10px;padding-bottom:15px;text-align:center;font-family:-apple-system,BlinkMacSystemFont,”Segoe UI”,Roboto,Oxygen-Sans,Ubuntu,Cantarell,”Helvetica Neue”,sans-serif;line-height:17px}.BuyBoxSection-683559780 .button-container{display:block;padding-right:20px;padding-left:20px}.BuyBoxSection-683559780 .button-container >a:hover,.Button-505204839:hover,.Button-1078489254:hover{text-decoration:none}.BuyBoxSection-683559780 .readcube-button{background:#fff;margin-top:30px}.BuyBoxSection-683559780 .button-asia{background:#069;border:1px solid #069;border-radius:0;cursor:pointer;display:block;padding:9px;outline:0;text-align:center;text-decoration:none;min-width:80px;margin-top:75px}.BuyBoxSection-683559780 .button-label-asia,.ButtonLabel-3869432492,.ButtonLabel-3296148077{display:block;color:#fff;font-size:17px;line-height:20px;font-family:-apple-system,BlinkMacSystemFont,”Segoe UI”,Roboto,Oxygen-Sans,Ubuntu,Cantarell,”Helvetica Neue”,sans-serif;text-align:center;text-decoration:none;cursor:pointer}.Button-505204839,.Button-1078489254{background:#069;border:1px solid #069;border-radius:0;cursor:pointer;display:block;padding:9px;outline:0;text-align:center;text-decoration:none;min-width:80px;margin-top:10px}.Button-505204839 .readcube-label,.Button-1078489254 .readcube-label{color:#069}
/* style specs end */Subscribe to JournalGet full journal access for 1 year$199.00only $3.90 per issueSubscribeAll prices are NET prices. VAT will be added later in the checkout.Tax calculation will be finalised during checkout.Rent or Buy articleGet time limited or full article access on ReadCube.from$8.99Rent or BuyAll prices are NET prices.

Additional access options:

Log in

Learn about institutional subscriptions

doi: https://doi.org/10.1038/d41586-021-03647-4

References1.Piquet, J. C. & López-Darias, M. Proc. R. Soc. B https://doi.org/10.1098/rspb.2021.1939 (2021).Article 

Google Scholar 
Download references

Subjects

Conservation biology

Jobs

Research Associate (m/f/x)

Technische Universität Dresden (TU Dresden)
01069 Dresden, Germany

PhD Position in Cybersecurity

Interdisciplinary Centre for Security, Reliability and Trust (SnT), University of Luxembourg
Luxembourg, Luxembourg

Doctoral (PhD) position in Structural Proteomics with EU Training network ALLODD

Karolinska Institutet, doctoral positions
Solna, Sweden

Post-doctoral Fellow

Luxembourg Institute of Health (LIH)
Esch Sur Alzette, Luxembourg More

1.Winemiller, K. O. et al. Balancing hydropower and biodiversity in the Amazon, Congo, and Mekong. Science 351, 128–129 (2016).CAS 

Google Scholar 
2.Latrubesse, E. M. et al. Damming the rivers of the Amazon basin. Nature 546, 363–369 (2017).CAS 
PubMed 

Google Scholar 
3.ICOLD. International Commission on Large Dams. http://www.icold-cigb.org/ (2016).4.Zarfl, C., Lumsdon, A. E., Berlekamp, J., Tydecks, L. & Tockner, K. A global boom in hydropower dam construction. Aquat. Sci. 77, 161–170 (2015).
Google Scholar 
5.Gibson, L., Wilman, E. N. & Laurance, W. F. How green is ‘green’energy? Trends Ecol. Evol. 32, 922–935 (2017).PubMed 

Google Scholar 
6.Wu, H. et al. Effects of dam construction on biodiversity: a review. J. Clean. Prod. 221, 480–489 (2019).
Google Scholar 
7.Palmeirim, A. F., Peres, C. A. & Rosas, F. C. Giant otter population responses to habitat expansion and degradation induced by a mega hydroelectric dam. Biol. Conserv. 174, 30–38 (2014).
Google Scholar 
8.Fearnside, P. M. Decision making on amazon dams: politics trumps uncertainty in the Madeira River sediments controversy. Water Altern. 6, 313–325 (2013).9.Fearnside, P. M. Greenhouse gas emissions from Brazil’s Amazonian hydroelectric dams. Environ. Res. Lett. 11, 011002 (2016).
Google Scholar 
10.Finer, M. & Jenkins, C. N. Proliferation of hydroelectric dams in the Andean Amazon and implications for Andes-Amazon connectivity. PLoS ONE 7, e35126 (2012).CAS 
PubMed 
PubMed Central 

Google Scholar 
11.Chen, G., Powers, R. P., de Carvalho, L. M. & Mora, B. Spatiotemporal patterns of tropical deforestation and forest degradation in response to the operation of the Tucuruí hydroelectric dam in the Amazon basin. Appl. Geogr. 63, 1–8 (2015).
Google Scholar 
12.Hunter, W. C., Anderson, B. W. & Ohmart, R. D. Avian community structure changes in a mature floodplain forest after extensive flooding. J. Wildl. Manag. 51, 495–502 (1987).13.Andriolo, A. et al. Severe population decline of marsh deer, Blastocerus dichotomus (Cetartiodactyla: Cervidae), a threatened species, caused by flooding related to a hydroelectric power plant. Zool. Curitiba 30, 630–638 (2013).
Google Scholar 
14.Irving, G. J., Round, P. D., Savini, T., Lynam, A. J. & Gale, G. A. Collapse of a tropical forest bird assemblage surrounding a hydroelectric reservoir. Glob. Ecol. Conserv. 16, e00472 (2018).
Google Scholar 
15.Carbone, C. & Gittleman, J. L. A common rule for the scaling of carnivore density. Science 295, 2273–2276 (2002).CAS 
PubMed 

Google Scholar 
16.Quigley, H. et al. Panthera onca (errata version published in 2018). The IUCN Red List of Threatened Species 2017: e.T15953A123791436 (2017).17.Dinerstein, E. et al. The fate of wild tigers. BioScience 57, 508–514 (2007).
Google Scholar 
18.Goodrich, J. et al. Panthera tigris. The IUCN Red List of Threatened Species 2015: e.T15955A50659951 (2015).19.Dirzo, R. et al. Defaunation in the Anthropocene. Science 345, 401–406 (2014).CAS 
PubMed 
PubMed Central 

Google Scholar 
20.Roberge, J. & Angelstam, P. Usefulness of the umbrella species concept as a conservation tool. Conserv. Biol. 18, 76–85 (2004).
Google Scholar 
21.Jędrzejewski, W. et al. Estimating large carnivore populations at global scale based on spatial predictions of density and distribution—application to the jaguar (Panthera onca). PLoS ONE 13, e0194719 (2018).PubMed 
PubMed Central 

Google Scholar 
22.GTRP. Global Tiger Recovery Program. Glob. Tiger Initiat. Secr. (World Bank, 2010).23.Desbiez, A. L. & de Paula, R. C. Species conservation planning: the jaguar National Action Plan for Brazil. Cat News 7, 4–7 (2012).
Google Scholar 
24.Achard, F. et al. Determination of deforestation rates of the world’s humid tropical forests. Science 297, 999–1002 (2002).CAS 
PubMed 

Google Scholar 
25.Myers, N., Mittermeier, R. A., Mittermeier, C. G., Da Fonseca, G. A. & Kent, J. Biodiversity hotspots for conservation priorities. Nature 403, 853–858 (2000).CAS 

Google Scholar 
26.Terborgh, J. et al. Ecological meltdown in predator-free forest fragments. Science 294, 1923–1926 (2001).CAS 
PubMed 

Google Scholar 
27.Gibson, L. et al. Near-complete extinction of native small mammal fauna 25 years after forest fragmentation. Science 341, 1508–1510 (2013).CAS 
PubMed 
PubMed Central 

Google Scholar 
28.Sollmann, R., Torres, N. M. & Silveira, L. Jaguar conservation in Brazil: the role of protected areas. Cat News 4, 15 (2008).
Google Scholar 
29.Cullen Junior, L., Sana, D. A., Lima, F., de Abreu, K. C. & Uezu, A. Selection of habitat by the jaguar, Panthera onca (Carnivora: Felidae), in the upper Paraná River, Brazil. Zool. Curitiba 30, 379–387 (2013).
Google Scholar 
30.Eriksson, C. E. et al. Extensive aquatic subsidies lead to territorial breakdown and high density of an apex predator. Ecology https://doi.org/10.1002/ecy.3543 (2021).31.Sanderson, E. W. How many animals do we want to save? The many ways of setting population target levels for conservation. BioScience 56, 911–922 (2006).
Google Scholar 
32.Luskin, M. S., Albert, W. R. & Tobler, M. W. Sumatran tiger survival threatened by deforestation despite increasing densities in parks. Nat. Commun. 8, 1–9 (2017).
Google Scholar 
33.Wikramanayake, E. et al. A landscape‐based conservation strategy to double the wild tiger population. Conserv. Lett. 4, 219–227 (2011).
Google Scholar 
34.Sunarto, S. et al. Tigers need cover: multi-scale occupancy study of the big cat in Sumatran forest and plantation landscapes. PLoS ONE 7, e30859 (2012).CAS 
PubMed 
PubMed Central 

Google Scholar 
35.Hyde, J. L., Bohlman, S. A. & Valle, D. Transmission lines are an under-acknowledged conservation threat to the Brazilian Amazon. Biol. Conserv. 228, 343–356 (2018).
Google Scholar 
36.Espinosa, S., Celis, G. & Branch, L. C. When roads appear jaguars decline: Increased access to an Amazonian wilderness area reduces potential for jaguar conservation. PLoS ONE 13, e0189740 (2018).PubMed 
PubMed Central 

Google Scholar 
37.Thompson, P. L., Rayfield, B. & Gonzalez, A. Loss of habitat and connectivity erodes species diversity, ecosystem functioning, and stability in metacommunity networks. Ecography 40, 98–108 (2017).
Google Scholar 
38.Linkie, M., Haidir, I. A., Nugroho, A. & Dinata, Y. Conserving tigers Panthera tigris in selectively logged Sumatran forests. Biol. Conserv. 141, 2410–2415 (2008).
Google Scholar 
39.Sharma, S. et al. Forest corridors maintain historical gene flow in a tiger metapopulation in the highlands of central India. Proc. R. Soc. B Biol. Sci. 280, 20131506 (2013).
Google Scholar 
40.Kinnaird, M. F., Sanderson, E. W., O’Brien, T. G., Wibisono, H. T. & Woolmer, G. Deforestation trends in a tropical landscape and implications for endangered large mammals. Conserv. Biol. 17, 245–257 (2003).
Google Scholar 
41.Ramesh, K. et al. Status of tiger and prey species in Panna Tiger Reserve, Madhya Pradesh: capture-recapture and distance sampling estimates. Technical Report (Wildlife Institute of India, 2013).42.Romero‐Muñoz, A. et al. Habitat loss and overhunting synergistically drive the extirpation of jaguars from the Gran Chaco. Divers. Distrib. 25, 176–190 (2019).
Google Scholar 
43.Alho, C. J. Hydropower dams and reservoirs and their impacts on Brazil’s biodiversity and natural habitats: a review. World J. Adv. Res. Rev. 6, 205–215 (2020).
Google Scholar 
44.Dobson, A. et al. Habitat loss, trophic collapse, and the decline of ecosystem services. Ecology 87, 1915–1924 (2006).PubMed 

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

Google Scholar 
46.Fearnside, P. M. Brazil’s Balbina Dam: environment versus the legacy of the pharaohs in Amazonia. Environ. Manag. 13, 401–423 (1989).
Google Scholar 
47.Fearnside, P. M. Dams in the Amazon: Belo Monte and Brazil’s hydroelectric development of the Xingu River Basin. Environ. Manag. 38, 16–27 (2006).
Google Scholar 
48.Milder, J. C., Scherr, S. J. & Bracer, C. Trends and future potential of payment for ecosystem services to alleviate rural poverty in developing countries. Ecol. Soc. 15, 4 (2010).49.Ceballos, G. et al. Jaguar distribution, biological corridors and protected areas in Mexico: from science to public policies. Landsc. Ecol. https://doi.org/10.1007/s10980-021-01264-0 (2021).50.Le Saout, S. et al. Protected areas and effective biodiversity conservation. Science 342, 803–805 (2013).PubMed 

Google Scholar 
51.Sabu, M. M., Pasha, S. V., Reddy, C. S., Singh, R. & Jaishanker, R. The effectiveness of tiger conservation landscapes in decreasing deforestation in South Asia: a remote sensing-based study. Spat. Inf. Res. 1–13, https://doi.org/10.1007/s41324-021-00411-8 (2021).52.Joshi, A. R. et al. Tracking changes and preventing loss in critical tiger habitat. Sci. Adv. 2, e1501675 (2016).PubMed 
PubMed Central 

Google Scholar 
53.Ritter, C. D. et al. Environmental impact assessment in Brazilian Amazonia: challenges and prospects to assess biodiversity. Biol. Conserv. 206, 161–168 (2017).
Google Scholar 
54.Thompson, J. J. et al. Environmental and anthropogenic factors synergistically affect space use of jaguars. Curr. Biol. 31, 3457–3466 (2021).CAS 
PubMed 

Google Scholar 
55.Food and agriculture organization of the united nations. AQUASTAT – FAO’s global information system on water and agriculture. https://www.fao.org/aquastat/en/databases/dams (2016).56.Tortato, F. R. et al. Infanticide in a jaguar (Panthera onca) population—does the provision of livestock carcasses increase the risk? Acta Ethol. 20, 69–73 (2017).
Google Scholar 
57.Chanchani, P., Gerber, B. D. & Noon, B. R. Elevated potential for intraspecific competition in territorial carnivores occupying fragmented landscapes. Biol. Conserv. 227, 275–283 (2018).
Google Scholar  More

1.Report of the Open-Ended Working Group on the Post-2020 Global Biodiversity Framework on its Third Meeting (Part I) CBD/WG2020/3/5 (CBD, 2021).2.Maxwell, S. L. et al. Nature 586, 217–227 (2020).CAS 
Article 

Google Scholar 
3.Protected Planet Live Report 2021 (UNEP-WCMC, IUCN, NGS, 2021).4.Díaz, S. et al. Science 366, eaax3100 (2019).Article 

Google Scholar 
5.Visconti, P. et al. Science 364, 239–241 (2019).CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
6.Maron, M. et al. Conserv. Lett. 14, e12816 (2021).Article 

Google Scholar 
7.Pressey, R. L. et al. Trends Ecol. Evol. 36, 808–821 (2021).Article 
PubMed 
PubMed Central 

Google Scholar 
8.Global Assessment Report on Biodiversity and Ecosystem Services of the Intergovernmental Science-Policy Platform on Biodiversity and Ecosystem Service (IPBES Secretariat, 2019).9.Living Planet Report 2020 (WWF, 2020).10.Jetz, W. et al. Nat. Ecol. Evol. 3, 539–551 (2019).Article 
PubMed 

Google Scholar 
11.Powers, R. P. & Jetz, W. Nat. Clim. Change 9, 323–329 (2019).Article 

Google Scholar 
12.Wilson, E. O. Half-Earth: Our Planet’s Fight for Life (WW Norton & Company, 2016).13.Sala, E. et al. Nature 592, 397–402 (2021).CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
14.Rinnan, D. S., Sica, Y., Ranipeta, A., Wilshire, J. & Jetz, W. Preprint at bioRxiv https://doi.org/10.1101/2020.02.05.936047 (2020).15.Beger, M. et al. Nat. Commun. 6, 8208 (2015).CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
16.Armstrong, C. Conserv. Biol. 33, 554–560 (2019).Article 
PubMed 
PubMed Central 

Google Scholar 
17.Post-2020 Global Biodiversity Framework: Scientific and Technical Information to Support the Review of the Updated Goals and Targets, and Related Indicators and Baselines CBD/SBSTTA/24/3 (CBD, 2020).18.Moilanen, A., Wilson, K. A. & Possingham, H. Spatial Conservation Prioritization: Quantitative Methods and Computational Tools (Oxford Univ. Press, 2009).19.Jung, M. et al. Nat. Ecol. Evol. 5, 1499–1509 (2021).Article 
PubMed 
PubMed Central 

Google Scholar 
20.Navarro, L. M. et al. Curr. Opin. Environ. Sustain. 29, 158–169 (2017).Article 

Google Scholar 
21.Jantke, K., Kuempel, C. D., McGowan, J., Chauvenet, A. L. M. & Possingham, H. P. Divers. Distrib. 25, 170–175 (2019).Article 

Google Scholar 
22.Bhola, N. et al. Conserv. Biol. 35, 168–178 (2021).Article 
PubMed 

Google Scholar 
23.Hansen, A. J. et al. Conserv. Lett. 14, e12822 (2021).Article 

Google Scholar 
24.Measuring Ecosystem Integrity (Goal A) in the Post-2020 Global Biodiversity Framework: The Geo Bon Species Habitat Index CBD/WG2020/3/INF/6 (CBD Secretariat, 2021).25.Rondinini, C. & Visconti, P. Conserv. Biol. 29, 1028–1036 (2015).Article 

Google Scholar 
26.McGeoch, M. A. et al. Preprint at bioRxiv https://doi.org/10.1101/2021.08.26.457851 (2021).27.Hoskins, A. J. et al. Environ. Model. Softw. 132, 104806 (2020).Article 

Google Scholar 
28.Adams, V. M., Visconti, P., Graham, V. & Possingham, H. P. One Earth 4, 901–906 (2021).Article 

Google Scholar 
29.Heiner, M. et al. Conserv. Sci. Pract. 1, e110 (2019).
Google Scholar  More