Ecology

1.Haddock, S. H. D., Moline, M. A. & Case, J. F. Bioluminescence in the sea. Annu. Rev. Mar. Sci. 2, 443–493. https://doi.org/10.1146/annurev-marine-120308-081028 (2010).ADS 
Article 

Google Scholar 
2.Bessho-Uehara, M. et al. Kleptoprotein bioluminescence: parapriacanthus fish obtain luciferase from ostracod prey. Sci. Adv. 6, eaax4942. https://doi.org/10.1126/sciadv.aax4942 (2020).ADS 
CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
3.Davis, M. P., Sparks, J. S. & Smith, W. L. Repeated and widespread evolution of bioluminescence in marine fishes. PLoS ONE 11, e0155154. https://doi.org/10.1371/journal.pone.0155154 (2016).CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
4.Claes, J. M. & Mallefet, J. Early development of bioluminescence suggests camouflage by counter-illumination in the velvet belly lantern shark Etmopterus spinax (Squaloidea: Etmopteridae). J. Fish Biol. 73, 1337–1350. https://doi.org/10.1111/j.1095-8649.2008.02006.x (2008).Article 

Google Scholar 
5.Harper, R. D. & Case, J. F. Disruptive counterillumination and its anti-predatory value in the plainfish midshipman Porichthys notatus. Mar. Biol. 134, 529–540. https://doi.org/10.1007/s002270050568 (1999).Article 

Google Scholar 
6.Herring, P. J. Sex with the lights on? A review of bioluminescent sexual dimorphism in the sea. J. Mar. Biol. Ass. 87, 829–842. https://doi.org/10.1017/S0025315407056433 (2007).CAS 
Article 

Google Scholar 
7.Widder, E. A. Bioluminescence in the ocean: origins of biological, chemical, and ecological diversity. Science (New York, NY) 328, 704–708. https://doi.org/10.1126/science.1174269 (2010).ADS 
CAS 
Article 

Google Scholar 
8.Hellinger, J. et al. The flashlight fish anomalops katoptron uses bioluminescent light to detect prey in the dark. PLoS ONE 12, e170489. https://doi.org/10.1371/journal.pone.0170489 (2017).CAS 
Article 

Google Scholar 
9.Golani, D., Fricke, R. & Appelbaum-Golani, B. Review of the genus Photoblepharon (Actinopterygii: Beryciformes: Anomalopidae). Acta Ichthyol. Piscat. 49, 33–41. https://doi.org/10.3750/AIEP/02530 (2019).Article 

Google Scholar 
10.Ho, H.-C. & Johnson, G. D. Protoblepharon mccoskeri, a new flashlight fish from eastern Taiwan (Teleostei: Anomalopidae). Zootaxa https://doi.org/10.11646/zootaxa.3479.1.5 (2012).Article 

Google Scholar 
11.Morin, J. G. et al. Light for all reasons: versatility in the behavioral repertoire of the flashlight fish. Science 190, 74–76. https://doi.org/10.1126/science.190.4209.74 (1975).ADS 
Article 

Google Scholar 
12.Hellinger, J. et al. Analysis of the territorial aggressive behavior of the bioluminescent flashlight fish photoblepharon steinitzi in the Red Sea. Front. Mar. Sci. 7, 431. https://doi.org/10.3389/fmars.2020.00078 (2020).ADS 
Article 

Google Scholar 
13.Gruber, D. F. et al. Bioluminescent flashes drive nighttime schooling behavior and synchronized swimming dynamics in flashlight fish. PLoS ONE 14, e0219852. https://doi.org/10.1371/journal.pone.0219852 (2019).CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
14.Hendry, T. A., de Wet, J. R. & Dunlap, P. V. Genomic signatures of obligate host dependence in the luminous bacterial symbiont of a vertebrate. Environ. Microbiol. 16, 2611–2622. https://doi.org/10.1111/1462-2920.12302 (2014).CAS 
Article 
PubMed 

Google Scholar 
15.Hendry, T. A., de Wet, J. R., Dougan, K. E. & Dunlap, P. V. Genome evolution in the obligate but environmentally active luminous symbionts of flashlight fish. Genome Biol. Evol. 8, 2203–2213. https://doi.org/10.1093/gbe/evw161 (2016).CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
16.Haneda, Y. & Tsuji, F. I. Light production in the luminous fishes Photoblepharon and Anomalops from the Banda Islands. Science (New York, NY) 173, 143–145. https://doi.org/10.1126/science.173.3992.143 (1971).ADS 
CAS 
Article 

Google Scholar 
17.Bassot, J.-M. in Bioluminescence in Progress, edited by F. H. Johnson & Y. Haneda (Princeton University Press1966), pp. 557–610.18.Watson, M., Thurston, E. L. & Nicol, J. A. C. Reflectors in the Light Organ of Anomalops (Anomalopidae, Teleostei). Proc. R. Soc. Lond. Ser. B Biol. Sci. 202, 339–351 (1978).ADS 
CAS 
Article 

Google Scholar 
19.Mark, M. D. et al. Visual tuning in the flashlight fish Anomalops katoptron to detect blue, bioluminescent light. PLoS ONE 13, e0198765. https://doi.org/10.1371/journal.pone.0198765 (2018).CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
20.Howland, H. C., Murphy, C. J. & McCosker, J. E. Detection of eyeshine by flashlight fishes of the family anomalopidae. Vis. Res. 32, 765–769. https://doi.org/10.1016/0042-6989(92)90191-K (1992).CAS 
Article 
PubMed 

Google Scholar 
21.McCosker, J. E. & Rosenblatt, R. H. Notes on the biology, taxonomy, and distribution of flashlight fishes (Beryciformes: Anomalopidae). Jpn. J. Ich. 34, 157–164. https://doi.org/10.1007/BF02912410 (1987).Article 

Google Scholar 
22.Parrish, J. K., Viscido, S. V. & Grünbaum, D. Self-organized fish schools: an examination of emergent properties. Biol. Bull. 202, 296–305. https://doi.org/10.2307/1543482 (2002).Article 
PubMed 

Google Scholar 
23.Pitcher, T. J. (ed.) Behaviour of Teleost Fishes (Chapman & Hall, 1993).
Google Scholar 
24.Helfman, G. S., Collette, B. B., Facey, D. E. & Bowen, B. W. The Diversity of Fishes. Biology, Evolution, and Ecology 2nd edn. (Wiley-Blackwell, Oxford, 2009).
Google Scholar 
25.McLean, S., Persson, A., Norin, T. & Killen, S. S. Metabolic costs of feeding predictively alter the spatial distribution of individuals in fish schools. Curr. Biol. 28, 1144–1149. https://doi.org/10.1016/j.cub.2018.02.043 (2018).CAS 
Article 
PubMed 

Google Scholar 
26.Pitcher, T. J., Magurran, A. E. & Winfield, I. J. Fish in larger shoals find food faster. Behav. Ecol. Sociobiol. 10, 149–151. https://doi.org/10.1007/BF00300175 (1982).Article 

Google Scholar 
27.Ioannou, C. C., Guttal, V. & Couzin, I. D. Predatory fish select for coordinated collective motion in virtual prey. Science (New York, NY) 337, 1212–1215. https://doi.org/10.1126/science.1218919 (2012).ADS 
CAS 
Article 

Google Scholar 
28.Turner, G. F. & Pitcher, T. J. Attack abatement: a model for group protection by combined avoidance and dilution. Am. Nat. 128, 228–240. https://doi.org/10.1086/284556 (1986).Article 

Google Scholar 
29.Landeau, L. & Terborgh, J. Oddity and the ‘confusion effect’ in predation. Anim. Behav. 34, 1372–1380. https://doi.org/10.1016/S0003-3472(86)80208-1 (1986).Article 

Google Scholar 
30.Kowalko, J. E. et al. Loss of schooling behavior in cavefish through sight-dependent and sight-independent mechanisms. Curr. Biol. 23, 1874–1883. https://doi.org/10.1016/j.cub.2013.07.056 (2013).CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
31.Partridge, B. L. & Pitcher, T. J. The sensory basis of fish schools: Relative roles of lateral line and vision. J. Comp. Physiol. 135, 315–325. https://doi.org/10.1007/BF00657647 (1980).Article 

Google Scholar 
32.Herbert-Read, J. E. et al. How predation shapes the social interaction rules of shoaling fish. Proc. Biol. Sci. https://doi.org/10.1098/rspb.2017.1126 (2017).Article 
PubMed 
PubMed Central 

Google Scholar 
33.Bierbach, D. et al. Using a robotic fish to investigate individual differences in social responsiveness in the guppy. R. Soc. Open Sci. 5, 181026. https://doi.org/10.1098/rsos.181026 (2018).Article 
PubMed 
PubMed Central 

Google Scholar 
34.Berdahl, A., Torney, C. J., Ioannou, C. C., Faria, J. J. & Couzin, I. D. Emergent sensing of complex environments by mobile animal groups. Science (New York, NY) 339, 574–576. https://doi.org/10.1126/science.1225883(2013) (2013).ADS 
CAS 
Article 

Google Scholar 
35.Sosna, M. M. G. et al. Individual and collective encoding of risk in animal groups. Proc. Natl. Acad. Sci. USA 116, 20556–20561. https://doi.org/10.1073/pnas.1905585116 (2019).CAS 
Article 
PubMed 

Google Scholar 
36.Kunz, H. & Hemelrijk, C. K. Artificial fish schools: collective effects of school size, body size, and body form. Artif. Life 9, 237–253. https://doi.org/10.1162/106454603322392451 (2003).Article 
PubMed 

Google Scholar 
37.Worm, M. et al. Evidence for mutual allocation of social attention through interactive signaling in a mormyrid weakly electric fish. Proc. Natl. Acad. Sci. USA 115, 6852–6857. https://doi.org/10.1073/pnas.1801283115 (2018).CAS 
Article 
PubMed 

Google Scholar 
38.Marras, S., Batty, R. S. & Domenici, P. Information transfer and antipredator maneuvers in schooling herring. Adapt. Behav. 20, 44–56. https://doi.org/10.1177/1059712311426799 (2012).Article 

Google Scholar 
39.Cohen, A. C. & Morin, J. G. It’s all about sex: bioluminescent courtship displays, morphological variation and sexual selection in two new genera of caribbean ostracodes. J. Crustacean Biol. 30, 56–67. https://doi.org/10.1651/09-3170.1 (2010).Article 

Google Scholar 
40.Rivers, T. J. & Morin, J. G. Complex sexual courtship displays by luminescent male marine ostracods. J. Exp. Biol. 211, 2252–2262. https://doi.org/10.1242/jeb.011130 (2008).Article 
PubMed 

Google Scholar 
41.Widder, E. A., Latz, M. I., Herring, P. J. & Case, J. F. Far red bioluminescence from two deep-sea fishes. Science (New York, NY) 225, 512–514. https://doi.org/10.1126/science.225.4661.512 (1984).ADS 
CAS 
Article 

Google Scholar 
42.Mensinger, A. F. & Case, J. F. Luminescent properties of deep sea fish. J. Exp. Mar. Biol. Ecol. 144, 1–15. https://doi.org/10.1016/0022-0981(90)90015-5 (1990).Article 

Google Scholar 
43.Sasaki, A. et al. Field evidence for bioluminescent signaling in the Pony Fish, Leiognathus elongatus. Environ. Biol. Fishes 66, 307–311. https://doi.org/10.1023/A:1023959123422 (2003).Article 

Google Scholar 
44.McFall-Ngai, M. J. & Dunlap, P. V. Three new modes of luminescence in the leiognathid fish Gazza minuta: discrete projected luminescence, ventral body flash, and buccal luminescence. Mar. Biol. 73, 227–237. https://doi.org/10.1007/BF00392247 (1983).Article 

Google Scholar 
45.Johnson, G. D. & Rosenblatt, R. H. Mechanisms of light organ occlusion in flashlight fishes, family Anomalopidae (Teleostei: Beryciformes), and the evolution of the group. Zool. J. Linnean Soc. 94, 65–96. https://doi.org/10.1111/j.1096-3642.1988.tb00882.x (1988).Article 

Google Scholar 
46.Herbert-Read, J. E. et al. Inferring the rules of interaction of shoaling fish. Proc. Natl. Acad. Sci. USA 108, 18726–18731. https://doi.org/10.1073/pnas.1109355108 (2011).ADS 
CAS 
Article 
PubMed 

Google Scholar 
47.Siebeck, U. E., Parker, A. N., Sprenger, D., Mäthger, L. M. & Wallis, G. A species of reef fish that uses ultraviolet patterns for covert face recognition. Curr. Biol. CB 20, 407–410. https://doi.org/10.1016/j.cub.2009.12.047 (2010).CAS 
Article 
PubMed 

Google Scholar 
48.Larsch, J. & Baier, H. Biological Motion as an Innate Perceptual Mechanism Driving Social Affiliation. Curr. Biol. 28, 3523-3532.e4. https://doi.org/10.1016/j.cub.2018.09.014 (2018).CAS 
Article 
PubMed 

Google Scholar 
49.Kasumyan, A. O. Acoustic signaling in fish. J. Ichthyol. 49, 963–1020. https://doi.org/10.1134/S0032945209110010 (2009).Article 

Google Scholar 
50.Santon, M. et al. Redirection of ambient light improves predator detection in a diurnal fish. Proc. Biol. Sci. 287, 20192292. https://doi.org/10.1098/rspb.2019.2292 (2020).Article 
PubMed 
PubMed Central 

Google Scholar 
51.de Busserolles, F., Fogg, L., Cortesi, F. & Marshall, J. The exceptional diversity of visual adaptations in deep-sea teleost fishes. Semin. Cell Dev. Biol. https://doi.org/10.1016/j.semcdb.2020.05.027 (2020).Article 
PubMed 

Google Scholar 
52.Bainbridge, R. The speed of swimming of fish as related to size and to the frequency and amplitude of the tail beat. J. Exp. Biol. 35, 109 (1958).
Google Scholar 
53.Videler, J. J. & Wardle, C. S. Fish swimming stride by stride: speed limits and endurance. Rev. Fish. Biol. Fish. 1, 23–40. https://doi.org/10.1007/BF00042660 (1991).Article 

Google Scholar 
54.Ware, D. M. Bioenergetics of pelagic fish: theoretical change in swimming speed and ration with body size. J. Fish. Res. Bd. Can. 35, 220–228. https://doi.org/10.1139/f78-036 (1978).ADS 
Article 

Google Scholar 
55.Meyer-Rochow, V. B. Loss of bioluminescence inAnomalops katoptron due to starvation. Experientia 32, 1175–1176. https://doi.org/10.1007/BF01927610 (1976).Article 

Google Scholar 
56.Barber, I., Downey, L. C. & Braithwaite, V. A. Parasitism, oddity and the mechanism of shoal choice. J. Fish Biol. 53, 1365–1368. https://doi.org/10.1111/j.1095-8649.1998.tb00256.x (1998).Article 

Google Scholar 
57.Ward, A. J. W., Duff, A. J., Krause, J. & Barber, I. Shoaling behaviour of sticklebacks infected with the microsporidian parasite, Glugea anomala. Environ. Biol. Fish. 72, 155–160. https://doi.org/10.1007/s10641-004-9078-1 (2005).Article 

Google Scholar 
58.Theodorakis, C. W. Size segregation and the effects of oddity on predation risk in minnow schools. Anim. Behav. 38, 496–502. https://doi.org/10.1016/S0003-3472(89)80042-9 (1989).Article 

Google Scholar 
59.Steche, O. Die Leuchtorgane von Anomalops katoptron und Photoblepharon palpebratus, zwei Oberflächenfischen aus dem malayischen Archipel: Ein Beitrag zur Anatomie und Physiologie der Leuchtorgane der Fische (Z Wiss Zool., 1909).60.Parrish, J. K. & Edelstein-Keshet, L. Complexity, pattern, and evolutionary trade-offs in animal aggregation. Science (New York, NY) 284, 99–101. https://doi.org/10.1126/science.284.5411.99 (1999).ADS 
CAS 
Article 

Google Scholar 
61.Woodland, D. J., Cabanban, A. S., Taylor, V. M. & Taylor, R. J. A synchronized rhythmic flashing light display by schooling Leiognathus splendens (Leiognathidae : Perciformes). Mar. Freshwater Res. 53, 159. https://doi.org/10.1071/MF01157 (2002).Article 

Google Scholar  More

1.Eisenberg, J. N. S., Desai, M. A., Levy, K., Bates, S. J., Liang, S., Naumoff, K. & Scott, J. C. Environmental determinants of infectious disease: A framework for tracking causal links and guiding public health research. Environ. Health Perspect. 115(8), 1216–1223. https://doi.org/10.1289/ehp.9806 (2007).Article 
PubMed 
PubMed Central 

Google Scholar 
2.Bertuzzo, E., Azaele, S., Maritan, A., Gatto, M., Rodriguez-Iturbe, I. & Rinaldo, A. On the space-time evolution of a cholera epidemic. Water Resour. Res. 44(1), 1–8. https://doi.org/10.1029/2007WR006211 (2008).Article 

Google Scholar 
3.Bomblies, A., Duchemin, J. B. & Eltahir, E. A. B. Hydrology of malaria: Model development and application to a Sahelian village. Water Resour. Res. 44(12), 1–26. https://doi.org/10.1029/2008WR006917 (2008).Article 

Google Scholar 
4.Perez-Saez, J., Mande, T., Ceperley, N., Bertuzzo, E., Mari, L., Gatto, M. & Rinaldo, A. Hydrology and density feedbacks control the ecology of intermediate hosts of schistosomiasis across habitats in seasonal climates. Proc. Natl. Acad. Sci. USA 113(23), 6427–6432. https://doi.org/10.1073/pnas.1602251113 (2016).ADS 
CAS 
Article 
PubMed 

Google Scholar 
5.van Dijk, J., Sargison, N. D., Kenyon, F. & Skuce, P. J. Climate change and infectious disease: Helminthological challenges to farmed ruminants in temperate regions. Animal 4(3), 377–392. https://doi.org/10.1017/s1751731109990991 (2010).Article 
PubMed 

Google Scholar 
6.Parham, P. E., Waldock, J., Christophides, G. K., Hemming, D., Agusto, F., Evans, K. J., … Michael, E. Climate, environmental and socio-economic change: weighing up the balance in vector- borne disease transmission. Philos. Trans. R. Soc. B 370, 1665. https://doi.org/10.1098/rstb.2013.0551 (2015).7.Cable, J., Barber, I., Boag, B., Ellison, A. R., Morgan, E. R., Murray, K., … Booth, M. Global change, parasite transmission and disease control: Lessons from ecology. Philos. Trans. R. Soc. B 372, 1719. https://doi.org/10.1098/rstb.2016.0088 (2017).8.McIntyre, K. M., Setzkorn, C., Hepworth, P. J., Morand, S., Morse, A. P. & Baylis, M. Systematic assessment of the climate sensitivity of important human and domestic animals pathogens in Europe. Sci. Rep. 7(1), 1–10. https://doi.org/10.1038/s41598-017-06948-9 (2017).CAS 
Article 

Google Scholar 
9.Garchitorena, A., Sokolow, S. H., Roche, B., Ngonghala, C. N., Jocque, M., Lund, A., … De Leo, G. A. Disease ecology, health and the environment: a framework to account for ecological and socio- economic drivers in the control of neglected tropical diseases. Phil. Trans. R. Soc. B, 372, 20160128. https://doi.org/10.1098/rstb.2016.0128 (2017).10.Webster, J. P., Molyneux, D. H., Hotez, P. J. & Fenwick, A. The contribution of mass drug administration to global health: Past, present and future. Philos. Trans. R. Soc. B 369(1645), 20130434. https://doi.org/10.1098/rstb.2013.0434 (2014).Article 

Google Scholar 
11.Beesley, N. J., Caminade, C., Charlier, J., Flynn, R. J., Hodgkinson, J. E., Martinez-Moreno, A., … Williams, D. J. L. Fasciola and fasciolosis in ruminants in Europe: Identifying research needs. Transbound. Emerg. Dis. 65, 199–216 https://doi.org/10.1111/tbed.12682 (2018).12.Kamaludeen, J., Graham-Brown, J., Stephens, N., Miller, J., Howell, A., Beesley, N. J., … Williams, D. Lack of efficacy of triclabendazole against Fasciola hepatica is present on sheep farms in three regions of England, and Wales. Vet. Rec. 184(16), 502–502. https://doi.org/10.1136/vr.105209 (2019).13.WHO. https://www.who.int/news-room/fact-sheets/detail/antimicrobial-resistance (2019).14.Mas-Coma, S., Valero, M. A. & Bargues, M. D. Climate change effects on trematodiases, with emphasis on zoonotic fascioliasis and schistosomiasis. Vet. Parasitol. 163(4), 264–280. https://doi.org/10.1016/j.vetpar.2009.03.024 (2009).Article 
PubMed 

Google Scholar 
15.Altizer, S., Ostfeld, R. S., Johnson, P. T. J., Kutz, S. & Harvell, C. D. Climate change and infectious diseases: From evidence to a predictive framework. Science 341(6145), 514–519. https://doi.org/10.1126/science.1239401 (2013).ADS 
CAS 
Article 
PubMed 

Google Scholar 
16.Siraj, A. S., Santos-Vega, M., Bouma, M. J., Yadeta, D., Ruiz Carrascal, D. & Pascual, M. Altitudinal changes in malaria incidence in highlands of Ethiopia and Colombia. Science 343(6175), 1154–1159. https://doi.org/10.1126/science.1244325 (2014).ADS 
CAS 
Article 
PubMed 

Google Scholar 
17.Sokolow, S. H., Jones, I. J., Jocque, M., La, D., Cords, O., Knight, A., … De Leo, G. A. Nearly 400 million people are at higher risk of schistosomiasis because dams block the migration of snail- eating river prawns. Phiosl. Trans. R. Soc. B 372(1722), 20160127. https://doi.org/10.1098/rstb.2016.0127 (2017).18.Morgan, E. R., Charlier, J., Hendrickx, G., Biggeri, A., Catalan, D., von Samson-Himmelstjerna, G., … Vercruysse, J. Global change and helminth infections in grazing ruminants in Europe: Impacts, trends and sustainable solutions. Agriculture 3(3), 484–502. https://doi.org/10.3390/agriculture3030484. (2013).19.Prüss-Ustün, A., Wolf, J., Corvalan, C., Bos, R., & Neira, M. Preventing Disease Through Healthy Environments: A Global Assessment of the Burden of Disease from Environmental Risks (World Health Organisation, 2016).20.Eisenberg, J. N. S., Brookhart, M. A., Rice, G., Brown, M. & Colford, J. M. Disease transmission models for public health decision making: Analysis of epidemic and endemic conditions caused by waterborne pathogens. Environ. Health Perspect. 110(8), 783–790. https://doi.org/10.1289/ehp.02110783 (2002).Article 
PubMed 
PubMed Central 

Google Scholar 
21.Lloyd-Smith, J. O., George, D., Pepin, K. M., Pitzer, V. E., Pulliam, J. R. C., Dobson, A. P., … Grenfell, B. T.. Epidemic dynamics at the human–animal interface. Science 326(5958), 1362–1367. https://doi.org/10.1126/science.1177345 (2009).22.Mellor, J. E., Levy, K., Zimmerman, J., Elliott, M., Bartram, J., Carlton, E., … Nelson, K.. Planning for climate change: The need for mechanistic systems-based approaches to study climate change impacts on diarrheal diseases. Science of the Total Environment, 548–549, 82–90. https://doi.org/10.1016/j.scitotenv.2015.12.087 (2016).23.Wu, X., Lu, Y., Zhou, S., Chen, L. & Xu, B. Impact of climate change on human infectious diseases: Empirical evidence and human adaptation. Environ. Int. 86, 14–23. https://doi.org/10.1016/j.envint.2015.09.007 (2016).Article 
PubMed 

Google Scholar 
24.Beltrame, L., Dunne, T., Vineer, H. R., Walker, J. G., Morgan, E. R., Vickerman, P., … Wagener, T. A mechanistic hydro-epidemiological model of liver fluke risk. Journal of the Royal Society Interface, 15(145). https://doi.org/10.1098/rsif.2018.0072 (2018).25.Rinaldo, A., Gatto, M. & Rodriguez-Iturbe, I. River networks as ecological corridors: A coherent ecohydrological perspective. Adv. Water Resour. 112, 27–58. https://doi.org/10.1016/j.advwatres.2017.10.005 (2018).ADS 
Article 
PubMed 
PubMed Central 

Google Scholar 
26.Mahmoud, M., Liu, Y., Hartmann, H., Stewart, S., Wagener, T., Semmens, D., … Winter, L. A formal framework for scenario development in support of environmental decision-making. Environ. Model. Softw. https://doi.org/10.1016/j.envsoft.2008.11.010 (2009).27.Wagener, T., Sivapalan, M., Troch, P. A., McGlynn, B. L., Harman, C. J., Gupta, H. V., … Wilson, J. S. The future of hydrology: An evolving science for a changing world. Water Resour. Research, 46, W05301. https://doi.org/10.1029/2009WR008906 (2010).28.Liang, S., Seto, E. Y. W., Remais, J. V, Zhong, B., Yang, C., Hubbard, A., … Spear, R. C. Environmental effects on parasitic disease transmission exemplified by schistosomiasis in western China. Proc. Natl. Acad. Sci. USA 104(17), 7110–7115. https://doi.org/10.1073/pnas.0701878104 (2007).29.Mari, L., Ciddio, M., Casagrandi, R., Perez-Saez, J., Bertuzzo, E., Rinaldo, A., … Gatto, M. Heterogeneity in schistosomiasis transmission dynamics. J. Theor. Biol. 432, 87–99. https://doi.org/10.1016/j.jtbi.2017.08.015 (2017).30.Knubben-Schweizer, G., Rüegg, S., Torgerson, P., Rapsch, C., Grimm, F., Hässig, M., … Braun, U. Control of bovine fasciolosis in dairy cattle in Switzerland with emphasis on pasture management. Vet. J. 186, 188–191. https://doi.org/10.1016/j.tvjl.2009.08.003 (2010).31.McCann, C. M., Baylis, M. & Williams, D. J. L. The development of linear regression models using environmental variables to explain the spatial distribution of Fasciola hepatica infection in dairy herds in England and Wales. Int. J. Parasitol. 40(9), 1021–1028. https://doi.org/10.1016/j.ijpara.2010.02.009 (2010).Article 
PubMed 

Google Scholar 
32.Selemetas, N., Phelan, P., O’Kiely, P. & de Waal, T. The effects of farm management practices on liver fluke prevalence and the current internal parasite control measures employed on Irish dairy farms. Vet. Parasitol. 207(3–4), 228–240. https://doi.org/10.1016/j.vetpar.2014.12.010 (2015).Article 
PubMed 

Google Scholar 
33.Ollerenshaw, C. B. The approach to forecasting the incidence of fascioliasis over England and Wales 1958–1962. Agric. Meteorol. 3(1–2), 35–53. https://doi.org/10.1016/0002-1571(66)90004-5 (1966).Article 

Google Scholar 
34.Fox, N. J., White, P. C. L., McClean, C. J., Marion, G., Evans, A. & Hutchings, M. R. Predicting impacts of climate change on fasciola hepatica risk. PLoS ONE 6(1), e16126. https://doi.org/10.1371/journal.pone.0016126 (2011).ADS 
CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
35.Caminade, C., van Dijk, J., Baylis, M. & Williams, D. Modelling recent and future climatic suitability for fasciolosis in Europe. Geospat. Health 9(2), 301–308. https://doi.org/10.4081/gh.2015.352 (2015).Article 
PubMed 

Google Scholar 
36.Lo Iacono, G., Armstrong, B., Fleming, L. E., Elson, R., Kovats, S., Vardoulakis, S. & Nichols, G. L. Challenges in developing methods for quantifying the effects of weather and climate on water-associated diseases: A systematic review. PLoS Negl. Trop. Dis. 11(6), e0005659. https://doi.org/10.1371/journal.pntd.0005659 (2017).Article 
PubMed 
PubMed Central 

Google Scholar 
37.Lo, N. C., Gurarie, D., Yoon, N., Coulibaly, J. T., Bendavid, E., Andrews, J. R. & King, C. H. Impact and cost-effectiveness of snail control to achieve disease control targets for schistosomiasis. Proc. Natl. Acad. Sci. USA 115(4), E584–E591. https://doi.org/10.1073/pnas.1708729114 (2018).CAS 
Article 
PubMed 

Google Scholar 
38.Williams, D. J. L., Howell, A., Graham-Brown, J., Kamaludeen, J., & Smith, D. Liver fluke—An overview for practitioners. Cattle Pract. 22 (2014).39.Pritchard, G. C., Forbes, A. B., Williams, D. J. L., Salimi-Bejestani, M. R. & Daniel, R. G. Emergence of fasciolosis in cattle in East Anglia. Vet. Rec. 157, 578–582. https://doi.org/10.1136/vr.157.19.578 (2005).CAS 
Article 
PubMed 

Google Scholar 
40.Kenyon, F., Sargison, N. D., Skuce, P. J. & Jackson, F. Sheep helminth parasitic disease in south eastern Scotland arising as a possible consequence of climate change. Vet. Parasitol. 163(4), 293–297. https://doi.org/10.1016/j.vetpar.2009.03.027 (2009).CAS 
Article 
PubMed 

Google Scholar 
41.McCann, C. M., Baylis, M. & Williams, D. J. L. Seroprevalence and spatial distribution of Fasciola hepatica-infected dairy herds in England and Wales. Vet. Rec. 166(20), 612–617. https://doi.org/10.1136/vr.b4836 (2010).CAS 
Article 
PubMed 

Google Scholar 
42.Ollerenshaw, C. B. & Rowlands, W. T. A method of forecasting the incidence of fascioliasis in Anglesey. Vet. Rec. 71(29), 591–598 (1959).
Google Scholar 
43.Fairweather, I. & Boray, J. C. Fasciolicides: Efficacy, actions, resistance and its management. Vet. J. 158, 81–112 (1999).CAS 
Article 

Google Scholar 
44.Morgan, E. R., Hosking, B. C., Burston, S., Carder, K. M., Hyslop, A. C., Pritchard, L. J., … Coles, G. C. A survey of helminth control practices on sheep farms in Great Britain and Ireland. Vet. J. 192(3), 390–397. https://doi.org/10.1016/j.tvjl.2011.08.004 (2012).45.Mitchell, G. Update on Fasciolosis in cattle and sheep. Practice 24(7), 378–385 (2002).Article 

Google Scholar 
46.Skuce, P. J. & Zadoks, R. N. Liver fluke A growing threat to UK livestock production. Cattle Pract. 21(2), 138–149 (2013).
Google Scholar 
47.Scotland’s RUral College (SRUC). Technical Note 677: Treatment and Control of Liver Fluke (2016).48.National Animal Disease Information System (NADIS). https://www.nadis.org.uk/parasite-forecast.aspx (2019).49.Markus, S. B., Bailey, D. W. & Jensen, D. Comparison of electric fence and a simulated fenceless control system on cattle movements. Livestock Sci. 170, 203–209. https://doi.org/10.1016/j.livsci.2014.10.011 (2014).50.Marini, D., Llewellyn, R., Belson, S. & Lee, C. Controlling Within-Field Sheep Movement Using Virtual Fencing. Animals 8(3), 31. https://doi.org/10.3390/ani8030031 (2018).51.Marshall, E. J. P., Wade, P. M. & Clare, P. Land drainage channels in England and Wales. Geogr. J. 144(2), 254–263 (1978).Article 

Google Scholar 
52.Robinson, M. & Armstrong, A. C. The extent of agricultural field drainage in England and Wales, 1971–80. Trans. Inst. Brit. Geogr. 13(1), 19–28 (1988).Article 

Google Scholar 
53.Robinson, E. L., Blyth, E. M., Clark, D. B., Finch, J. & Rudd, A. C. Trends in atmospheric evaporative demand in Great Britain using high-resolution meteorological data. Hydrol. Earth Syst. Sci. 21(2), 1189–1224. https://doi.org/10.5194/hess-21-1189-2017 (2017).ADS 
Article 

Google Scholar 
54.National River Flow Archive (NRFA). NERC CEH. https://nrfa.ceh.ac.uk/ (2019).55.Intermap Technologies. NEXTMap British Digital Terrain 50m resolution (DTM10) Model Data by Intermap, NERC Earth Observation Data Centre. http://catalogue.ceda.ac.uk/uuid/f5d41db1170f41819497d15dd8052ad2 (2009).56.Coxon, G., Freer, J., Lane, R., Dunne, T., Howden, N. J. K., Quinn, N., … Woods, R. DECIPHeR v1: Dynamic fluxEs and ConnectIvity for Predictions of HydRology. Geosci. Model Dev. 12, 2285–2306. https://doi.org/10.5194/gmd-2018-205 (2019).57.Beven, K., Lamb, R., Quinn, P., Romanowicz, R. & Freer, J. TOPMODEL. In Computer Models of Watershed Hydrology (ed. Sing, V. P.) 627–668 (Water Resource Publications, 1995).
Google Scholar 
58.Vetter, T., Huang, S., Aich, V., Yang, T., Wang, X., Krysanova, V. & Hattermann, F. Multi-model climate impact assessment and intercomparison for three large-scale river basins on three continents. Earth Syst. Dyn. 6(1), 17–43. https://doi.org/10.5194/esd-6-17-2015 (2015).ADS 
Article 

Google Scholar 
59.Shen, C., Niu, J. & Phanikumar, M. S. Evaluating controls on coupled hydrologic and vegetation dynamics in a humid continental climate watershed using a subsurface-land surface processes model. Water Resour. Res. 49(5), 2552–2572. https://doi.org/10.1002/wrcr.20189 (2013).ADS 
Article 

Google Scholar 
60.MetOffice. https://www.metoffice.gov.uk/research/climate/maps-and-data (2017). More

1.Grassi, G. et al. The key role of forests in meeting climate targets requires science for credible mitigation. Nat. Clim. Chang. 7, 220–226 (2017).ADS 
Article 

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

Google Scholar 
3.Avitabile, V. et al. An integrated pan-tropical biomass map using multiple reference datasets. Glob. Chang. Biol. 22, 1406–1420 (2016).ADS 
PubMed 
Article 
PubMed Central 

Google Scholar 
4.Hubau, W. et al. Asynchronous carbon sink saturation in African and Amazonian tropical forests. Nature 579, 80–87 (2020).ADS 
CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
5.Song, X. P., Huang, C., Saatchi, S. S., Hansen, M. C. & Townshend, J. R. Annual carbon emissions from deforestation in the Amazon basin between 2000 and 2010. PLoS ONE 10, 1–21 (2015).
Google Scholar 
6.Pan, Y. et al. A large and persistent carbon sink in the world’s forests. Science 333, 988–993 (2011).ADS 
CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
7.Ministério do Meio Ambiente (MMA). REDD+ and Brazil’s Nationally Determined Contribution. http://redd.mma.gov.br/en/redd-and-brazil-s-ndc (2016).8.Bongers, F., Chazdon, R. L., Poorter, L. & Peña-Claros, M. The potential of secondary forests. Science 348, 642–643 (2015).ADS 
CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
9.Almeida, C. Ade et al. High spatial resolution land use and land cover mapping of the Brazilian Legal Amazon in 2008 using Landsat-5/TM and MODIS data. Acta Amaz 46, 291–302 (2016).Article 

Google Scholar 
10.Nunes, S. Jr., Oliveira, L., Siqueira, J., Morton, D. C. & Souza, C. M. Unmasking secondary vegetation dynamics in the Brazilian Amazon. Environ. Res. Lett. 15, 034057 (2020).11.Poorter, L. et al. Biomass resilience of Neotropical secondary forests. Nature 530, 211–214 (2016).ADS 
CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
12.Requena Suarez, D. et al. Estimating aboveground net biomass change for tropical and subtropical forests: refinement of IPCC default rates using forest plot data. Glob. Chang. Biol. 25, 3609–3624 (2019).ADS 
PubMed 
PubMed Central 
Article 

Google Scholar 
13.Mercado, L. M. et al. Impact of changes in diffuse radiation on the global land carbon sink. Nature 458, 1014–1017 (2009).ADS 
CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
14.Chazdon, R. L. et al. Carbon sequestration potential of second-growth forest regeneration in the Latin American tropics. Sci. Adv. 2, e1501639 (2016).15.Aragão, L. E. O. C. et al. 21st Century drought-related fires counteract the decline of Amazon deforestation carbon emissions. Nat. Commun. 9, 536 (2018).16.Zarin, D. J. et al. Legacy of fire slows carbon accumulation in Amazonian forest regrowth. Front. Ecol. Environ. 3, 365–369 (2005).Article 

Google Scholar 
17.Anderegg, W. et al. Climate-driven risks to the climate mitigation potential of forests. Science 368, eaaz7005 (2020).18.Silva Junior, C. H. L. et al. Benchmark maps of 33 years of secondary forest age for Brazil. Sci. Data 7, 269 (2020).PubMed 
PubMed Central 
Article 

Google Scholar 
19.Yang, Y., Saatchi, S., Xu, L., Keller, M. & Corsini, C. R. Interannual variability of carbon uptake of secondary forests in the Brazilian Amazon (2004–2014). Glob. Biogeochem. Cycles https://doi.org/10.1029/2019GB006396 (2020).20.Vieira, I. C. G., Gardner, T., Ferreira, J., Lees, A. C. & Barlow, J. Challenges of governing second-growth forests: A case study from the Brazilian Amazonian state of Pará. Forests 5, 1737–1752 (2014).Article 

Google Scholar 
21.Wang, Y. et al. Upturn in secondary forest clearing buffers primary forest loss in the Brazilian Amazon. Nat. Sustain. https://doi.org/10.1038/s41893-019-0470-4 (2020).22.Cook-Patton, S. C. et al. Mapping carbon accumulation potential from global natural forest regrowth. Nature 585, 545–550 (2020).ADS 
CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
23.Santoro, M. & Cartus, O. ESA Biomass Climate Change Initiative (Biomass_cci): Global datasets of forest above-ground biomass for the year 2017, v1. Centre for Environmental Data Analysis. https://catalogue.ceda.ac.uk/uuid/bedc59f37c9545c981a839eb552e4084 (2019).24.IPCC. Chapter 4 Forest Land. In IPCC Guidelines for National Greenhouse Gas Inventories (eds. Eggleston, H. S., Buendia, L., Miwa, K., Ngara, T. & Tanabe, K.) vol. 4, 1–29 (IGES, 2006).25.Mapbiomas Brasil. Project MapBiomas—Collection 3.1 of Brazilian Land Cover and Use Map Series. https://mapbiomas.org/ (2018).26.Abatzoglou, J. T., Dobrowski, S. Z., Parks, S. A. & Hegewisch, K. C. TerraClimate, a high-resolution global dataset of monthly climate and climatic water balance from 1958-2015. Sci. Data 5, 1–12 (2018).Article 

Google Scholar 
27.Funk, C. et al. The climate hazards infrared precipitation with stations—a new environmental record for monitoring extremes. Sci. Data 2, 1–21 (2015).
Google Scholar 
28.Anderson, L. O. et al. Vulnerability of Amazonian forests to repeated droughts. Philos. Trans. R. Soc. B Biol. Sci. 373, 20170411 (2018).29.Phillips, O. L. et al. Drought sensitivity of the Amazon rainforest. Science 323, 1344–1347 (2009).30.Zuquim, G. et al. Making the most of scarce data: mapping soil gradients in data-poor areas using species occurrence records. Methods Ecol. Evol. 10, 788–801 (2019).Article 

Google Scholar 
31.Didan, K. MOD13Q1 MODIS/Terra Vegetation Indices 16-Day L3 Global 250m SIN Grid V006. NASA EOSDIS Land Processes DAAC. https://doi.org/10.5067/MODIS/MOD13Q1.006. (2015).32.Johnson, C. M., Vieira, I. C. G., Zarin, D. J., Frizano, J. & Johnson, A. H. Carbon and nutrient storage in primary and secondary forests in eastern Amazônia. Forest Ecol. Manag. 147, 245–252 (2001).Article 

Google Scholar 
33.Moran, E. F. Effects of soil fertility and land-use on forest succesion in Amazonia. Forest Ecol. Manag. 139, 93–108 (2000).ADS 
Article 

Google Scholar 
34.Poorter, L. et al. Wet and dry tropical forests show opposite successional pathways in wood density but converge over time. Nat. Ecol. Evol. 3, 928–934 (2019).PubMed 
Article 
PubMed Central 

Google Scholar 
35.Aragão, L. E. O. C. et al. Environmental change and the carbon balance of Amazonian forests. Biol. Rev. 89, 913–931 (2014).PubMed 
Article 
PubMed Central 

Google Scholar 
36.Alves, D. S. et al. Biomass of primary and secondary vegetation in Rondônia, Western Brazilian Amazon. Glob. Chang. Biol. 3, 451–461 (1997).ADS 
Article 

Google Scholar 
37.MCT. Third National Communication of Brazil to the United Nations Framework Convention on Climate Change. (2016). https://unfccc.int/documents/66129.38.Roderick, M. L., Farquhar, G. D., Berry, S. L. & Noble, I. R. On the direct effect of clouds and atmospheric particles on the productivity and structure of vegetation. Oecologia 129, 21–30 (2001).ADS 
PubMed 
Article 
PubMed Central 

Google Scholar 
39.Lange, O. L., Lösch, R., Schulze, E. D. & Kappen, L. Responses of stomata to changes in humidity. Planta 100, 76–86 (1971).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
40.Morton, D. C. et al. Mapping canopy damage from understory fires in Amazon forests using annual time series of Landsat and MODIS data. Remote Sens. Environ. 115, 1706–1720 (2011).ADS 
Article 

Google Scholar 
41.Baker, T. R. et al. Variation in wood density determines spatial patterns in Amazonian forest biomass. Glob. Chang. Biol. 10, 545–562 (2004).ADS 
Article 

Google Scholar 
42.Malhi, Y. et al. The regional variation of aboveground live biomass in old-growth Amazonian forests. Glob. Chang. Biol. 12, 1107–1138 (2006).ADS 
Article 

Google Scholar 
43.Saatchi, S., Houghton, R. A., Dos Santos Alvalá, R. C., Soares, J. V. & Yu, Y. Distribution of aboveground live biomass in the Amazon basin. Glob. Chang. Biol. 13, 816–837 (2007).ADS 
Article 

Google Scholar 
44.Wandelli, E. V. & Fearnside, P. M. Secondary vegetation in central Amazonia: land-use history effects on aboveground biomass. Forest Ecol. Manag. 347, 140–148 (2015).Article 

Google Scholar 
45.Uhl, C., Buschbacher, R. & Serrão, E. A. Abandoned pastures in Eastern Amazonia. I. Patterns of plant succession. J. Ecol. 76, 663–681 (1988).Article 

Google Scholar 
46.Kalamandeen, M. et al. Pervasive rise of small-scale deforestation in Amazonia. Sci. Rep. 8, 1–10 (2018).CAS 
Article 

Google Scholar 
47.Jakovac, C. C., Peña-Claros, M., Kuyper, T. W. & Bongers, F. Loss of secondary-forest resilience by land-use intensification in the Amazon. J. Ecol. 103, 67–77 (2015).Article 

Google Scholar 
48.Hirota, M., Holmgren, M., van Nes, E. H. & Scheffer, M. Global resilience of tropical forest. Science 334, 232–235 (2011).ADS 
CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
49.Scheffer, M. et al. Anticipating critical transitions. Science 338, 344–348 (2012).50.Elias, F. et al. Assessing the growth and climate sensitivity of secondary forests in highly deforested Amazonian landscapes. Ecology 101, e02954 (2020).51.Hawes, J. E. et al. A large-scale assessment of plant dispersal mode and seed traits across human-modified Amazonian forests. J. Ecol. 108, 1373–1385 (2020).Article 

Google Scholar 
52.Bullock, E. L., Woodcock, C. E., Souza, C. & Olofsson, P. Satellite-based estimates reveal widespread forest degradation in the Amazon. Glob. Chang. Biol. 26, 2956–2969 (2020).ADS 
PubMed 
Article 
PubMed Central 

Google Scholar 
53.Smith, C. C. et al. Secondary forests offset less than 10% of deforestation-mediated carbon emissions in the Brazilian Amazon. Glob. Chang. Biol. https://doi.org/10.1111/gcb.15352 (2020).54.Toledo, R. M. et al. Restoring tropical forest composition is more difficult, but recovering tree-cover is faster, when neighbouring forests are young. Landsc. Ecol. 35, 1403–1416 (2020).Article 

Google Scholar 
55.Armenteras, D., González, T. M. & Retana, J. Forest fragmentation and edge influence on fire occurrence and intensity under different management types in Amazon forests. Biol. Conserv. 159, 73–79 (2013).Article 

Google Scholar 
56.Uriarte, M. et al. Impacts of climate variability on tree demography in second growth tropical forests: the importance of regional context for predicting successional trajectories. Biotropica 48, 780–797 (2016).Article 

Google Scholar 
57.Alencar, A. A. C., Solórzano, L. A. & Nepstad, D. C. Modeling forest understory fires in an eastern amazonian landscape. Ecol. Appl. 14, 139–149 (2004).Article 

Google Scholar 
58.Esquivel-Muelbert, A. et al. Compositional response of Amazon forests to climate change. Glob. Chang. Biol. 25, 39–56 (2019).ADS 
PubMed 
Article 
PubMed Central 

Google Scholar 
59.Levine, N. M. et al. Ecosystem heterogeneity determines the ecological resilience of the Amazon to climate change. Proc. Natl Acad. Sci. USA 113, 793–797 (2016).ADS 
CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
60.Esquivel-Muelbert, A. et al. Tree mode of death and mortality risk factors across Amazon forests. Nat. Commun. 11, 5515 (2020).61.PRODES. TerraBrasilis—Taxas anuais de sesmatamento na Amazônia Legal Brasiliera. http://terrabrasilis.dpi.inpe.br/app/dashboard/deforestation/biomes/legal_amazon/rates (2020).62.Lennox, G. D. et al. Second rate or a second chance? Assessing biomass and biodiversity recovery in regenerating Amazonian forests. Glob. Chang. Biol. 24, 5680–5694 (2018).PubMed 
Article 
PubMed Central 

Google Scholar 
63.Fearnside, P. M. & Guimarães, W. M. Carbon uptake By secondary forests in Brazilian Amazonia. Forest Ecology and Management 80, 35–46 (1996).64.Crouzeilles, R. et al. Achieving cost-effective landscape-scale forest restoration through targeted natural regeneration. Conserv. Lett. 13, 1–9 (2020).Article 

Google Scholar 
65.Aragão, L. E. O. C. et al. Spatial patterns and fire response of recent Amazonian droughts. Geophys. Res. Lett. 34, 1–5 (2007).Article 

Google Scholar 
66.Campanharo, W. & Silva Junior, C. H. L. Maximun Cumulative Water Deficit—MCWD: a R language script. https://doi.org/10.5281/zenodo.2652629 (2019).67.Richards, F. J. A flexible growth function for empirical use. J. Exp. Bot. 10, 290–301 (1959).Article 

Google Scholar 
68.Kuhn, M. et al. Caret: 6.0-71., Classification and Regression Training. R package version. (2016). https://rdrr.io/cran/caret/.69.R Development Core Team. R: A Language and Environment for Statistical Computing. (2020). https://www.r-project.org/.70.Strobl, C., Boulesteix, A. L., Zeileis, A. & Hothorn, T. Bias in random forest variable importance measures: Illustrations, sources and a solution. BMC Bioinformatics 8, 25 (2007).71.Strobl, C., Boulesteix, A. L., Kneib, T., Augustin, T. & Zeileis, A. Conditional variable importance for random forests. BMC Bioinformatics 9, 1–11 (2008).Article 
CAS 

Google Scholar 
72.Strobl, C., Hothorn, T. & Zeileis, A. Party on! A new, conditional variable importance measure available in the party package. R J. 1, 14–17 (2009).73.Behnamian, A. et al. A systematic approach for variable selection with random forests: achieving stable variable importance values. IEEE Geosci. Remote Sens. Lett. 14, 1988–1992 (2017).ADS 
Article 

Google Scholar 
74.Congalton Russell, G. & Green, K. Assessing the Accuracy of Remotely Sensed Data: Principles and Practices. vol. 25 (CRC Press, 2009).75.Heinrich, V. et al. Data from paper: Large carbon sink potential of Secondary Forests in Brazilian Amazon to mitigate climate change. Zenodo https://zenodo.org/record/4479234#.YBVdBHNxdPY (2021).76.Heinrich, V. et al. Code from paper: Large carbon sink potential of Secondary Forests in the Brazilian Amazon to mitigate climate change. GitHub https://github.com/heinrichTrees/secondary-forest-regrowth-amazon-public (2021). More

Thank you for visiting nature.com. You are using a browser version with limited support for CSS. To obtain
the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in
Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles
and JavaScript. More

The COVID-19 pandemic prompted calls for cessation of wild meat trade and consumption to protect public health and biodiversity. High-quality data on wild meat consumption at a global scale is limited, but in many regions wild meat forms an important component of human nutrition — complete cessation of wild meat consumption could create unforeseen shocks to wider food systems through reduced protein intake and land use change for livestock production. Therefore, Hollie Booth, from the University of Oxford, and colleagues identified wild meat-consuming regions across 83 countries and estimated the potential magnitude of halting wild meat consumption on food systems.Using available global datasets on nutrient supply and land demand, Booth and colleagues identified 15 countries where wild meat accounted for more than 5% of total animal protein — and all ranked in the bottom 50% of the global food security index. Of these 15 countries, Madagascar, Republic of Congo, Guinea, Rwanda, Central African Republic, Zimbabwe, Botswana and Côte d’Ivoire would fall below WHO protein intake recommendations — the latter two countries derive 61% and 73% (respectively) of animal protein from wild meat. Estimates indicated that globally 123,980 km2 of additional agricultural land, predominantly in South and Central America, and sub-Saharan Africa, would be needed to replace wild meat protein with livestock-derived protein — placing up to 267 species at risk of extinction. Madagascar, rural Gabon, the East Region of Cameroon, Malawi, and the Brazilian Amazon would likely struggle with food system adaptation due to a lack of viable protein alternatives, food security trade-offs and social factors such as limited ban enforcement capacities and illicit trading.Booth and colleagues note that the ability of countries’ food systems to absorb these shocks are unequally distributed, with protein shortfalls in some of the world’s most food-insecure countries and potential loss of livelihoods, rights and social values. Forest regions with high mammalian biodiversity may also suffer under increased risk of emerging infectious diseases — with epidemic or pandemic potential. Thus, the authors call for risk-based regulation preventing the use and trade of slowly reproducing, endangered species, or those with high zoonotic potential while permitting use and trade of more sustainable species. Despite the limited data available, Booth and colleagues demonstrate that political and social debates around wild meat urgently require further research — and holistic policy responses guided by food systems thinking. More

1.Tilman, D. & Clark, M. Global diets link environmental sustainability and human health. Nature 515, 518–522 (2014).ADS 
CAS 
Article 

Google Scholar 
2.Hicks, C. C. et al. Harnessing global fisheries to tackle micronutrient deficiencies. Nature 574, 95–98 (2019).ADS 
CAS 
Article 

Google Scholar 
3.SOFIA 2020—State of Fisheries and Aquaculture in the World 2020 (FAO, 2020).4.Godfray, H. C. J. et al. Food security: the challenge of feeding 9 billion people. Science 327, 812–818 (2010).ADS 
CAS 
Article 

Google Scholar 
5.Kawarazuka, N. & Béné, C. The potential role of small fish species in improving micronutrient deficiencies in developing countries: building evidence. Public Health Nutr. 14, 1927–1938 (2011).Article 

Google Scholar 
6.Belton, B. & Thilsted, S. H. Fisheries in transition: food and nutrition security implications for the global South. Glob. Food Sec. 3, 59–66 (2014).Article 

Google Scholar 
7.Hilborn, R., Banobi, J., Hall, S. J., Pucylowski, T. & Walsworth, T. E. The environmental cost of animal source foods. Front. Ecol. Environ. 16, 329–335 (2018).Article 

Google Scholar 
8.Froehlich, H. E., Runge, C. A., Gentry, R. R., Gaines, S. D. & Halpern, B. S. Comparative terrestrial feed and land use of an aquaculture-dominant world. Proc. Natl Acad. Sci. USA 115, 5295–5300 (2018).CAS 
Article 

Google Scholar 
9.Heilpern, S. Integrating Food Webs and Food Security to Understand the Impact of Biodiversity Loss on Ecosystem Functions and Services. PhD thesis, Columbia Univ. (2020).10.Ministerio de Desarrollo Agrario y Riego (Midagri); https://www.gob.pe/midagri11.Ministerio de la Producción (Produce); https://www.gob.pe/produce12.OECD-FAO Agricultural Outlook, 2019 edn (OECD/FAO, 2020).13.Peru—National Program for Innovation in Fisheries and Aquaculture Project (World Bank, 2017).14.DeFries, R. et al. Metrics for land-scarce agriculture. Science 349, 238–240 (2015).ADS 
CAS 
Article 

Google Scholar 
15.Loreto: Resultados Definitivos de la Población Economicamnte Activa 2017 (Instituto Nacional de Estadistica e Informática, 2018).16.McIntyre, P. B., Liermann, C. A. R. & Revenga, C. Linking freshwater fishery management to global food security and biodiversity conservation. Proc. Natl Acad. Sci. USA 113, 12880–12885 (2016).CAS 
Article 

Google Scholar 
17.Youn, S.-J. et al. Inland capture fishery contributions to global food security and threats to their future. Glob. Food Sec. 3, 142–148 (2014).Article 

Google Scholar 
18.Kawarazuka, N. & Béné, C. The potential role of small fish species in improving micronutrient deficiencies in developing countries: building evidence. Public Health Nutr. 14, 1927–1938 (2011).Article 

Google Scholar 
19.Bogard, J. R. et al. Nutrient composition of important fish species in Bangladesh and potential contribution to recommended nutrient intakes. J. Food Compos. Anal. 42, 120–133 (2015).CAS 
Article 

Google Scholar 
20.Vaitla, B. et al. Predicting nutrient content of ray-finned fishes using phylogenetic information. Nat. Commun. 9, 1–10 (2018).CAS 
Article 

Google Scholar 
21.Popkin, B. M. Nutrition, agriculture and the global food system in low and middle income countries. Food Policy 47, 91–96 (2014).Article 

Google Scholar 
22.Bogard, J. R. et al. Higher fish but lower micronutrient intakes: temporal changes in fish consumption from capture fisheries and aquaculture in Bangladesh. PLoS ONE 12, e0175098 (2017).Article 

Google Scholar 
23.Golden, C. D., Fernald, L. C. H., Brashares, J. S., Rasolofoniaina, B. J. R. & Kremen, C. Benefits of wildlife consumption to child nutrition in a biodiversity hotspot. Proc. Natl Acad. Sci. USA 108, 19653–19656 (2011).ADS 
CAS 
Article 

Google Scholar 
24.Davis, K. F. et al. Meeting future food demand with current agricultural resources. Global Environ. Change 39, 125–132 (2016).Article 

Google Scholar 
25.Parker, R. W. R. & Tyedmers, P. H. Fuel consumption of global fishing fleets: current understanding and knowledge gaps. Fish Fish. 16, 684–696 (2015).Article 

Google Scholar 
26.Parker, R. W. R. et al. Fuel use and greenhouse gas emissions of world fisheries. Nat. Clim. Change 8, 333–337 (2018).ADS 
CAS 
Article 

Google Scholar 
27.Avadí, A. et al. Comparative environmental performance of artisanal and commercial feed use in Peruvian freshwater aquaculture. Aquaculture 435, 52–66 (2015).Article 

Google Scholar 
28.Fry, J. P., Mailloux, N. A., Love, D. C., Milli, M. C. & Cao, L. Feed conversion efficiency in aquaculture: do we measure it correctly? Environ. Res. Lett. 13, 024017 (2018).ADS 
Article 

Google Scholar 
29.Prudêncio da Silva, V., van der Werf, H. M. G., Soares, S. R. & Corson, M. S. Environmental impacts of French and Brazilian broiler chicken production scenarios: an LCA approach. J. Environ. Manage. 133, 222–231 (2014).Article 

Google Scholar 
30.Seto, K. & Fiorella, K. J. From sea to plate: the role of fish in a sustainable diet. Front. Mar. Sci. 4, 74 (2017).Article 

Google Scholar 
31.Lynch, A. J. et al. Inland fish and fisheries integral to achieving the Sustainable Development Goals. Nature Sustain. 3, 579–587 (2020).32.Nardoto, G. B. et al. Frozen chicken for wild fish: nutritional transition in the Brazilian Amazon region determined by carbon and nitrogen stable isotope ratios in fingernails. Am. J. Hum. Biol. 23, 642–650 (2011).Article 

Google Scholar 
33.Khoury, C. K. et al. Increasing homogeneity in global food supplies and the implications for food security. Proc. Natl Acad. Sci. USA 111, 4001–4006 (2014).ADS 
CAS 
Article 

Google Scholar 
34.Kearney, J. Food consumption trends and drivers. Philos. Trans. R. Soc. Lond B Biol. Sci. 365, 2793–2807 (2010).Article 

Google Scholar 
35.Pinnegar, J. K., Hutton, T. P. & Placenti, V. What relative seafood prices can tell us about the status of stocks. Fish Fish. 7, 219–226 (2006).Article 

Google Scholar 
36.Wong, J. T. et al. Small-scale poultry and food security in resource-poor settings: a review. Global Food Sec. 15, 43–52 (2017).Article 

Google Scholar 
37.Tabela Brasileira de Composição de Alimentos—TACO (Núcleo de Estudos e Pesquisas em Alimentação—NEPA/UNICAMP, 2011).38.Cahu, C., Salen, P. & de Lorgeril, M. Farmed and wild fish in the prevention of cardiovascular diseases: assessing possible differences in lipid nutritional values. Nutr. Metab. Cardiovasc. Dis. 14, 34–41 (2004).CAS 
Article 

Google Scholar 
39.Vitamin and Mineral Requirements in Human Nutrition (WHO/FAO, 2004).40.Fats and Fatty Acids in Human Nutrition: Report of an Expert Consultation (FAO, 2010).41.Heilpern, S. A., Weeks, B. C. & Naeem, S. Predicting ecosystem vulnerability to biodiversity loss from community composition. Ecology 99, 1099–1107 (2018).Article 

Google Scholar 
42.R Core Team. R: A Language and Environment for Statistical Computing (R Foundation for Statistical Computing, 2020). More

1.Parente, V., Ferreira, D., Moutinho dos Santos, E. & Luczynski, E. Offshore decommissioning issues: deductibility and transferability. Energy Policy 34, 1992–2001 (2006).Article 

Google Scholar 
2.Macreadie, P. I., Fowler, A. M. & Booth, D. J. Rigs-to-reefs: will the deep sea benefit from artificial habitat?. Front. Ecol. Environ. 9, 455–461 (2011).Article 

Google Scholar 
3.Fowler, A. M., Macreadie, P. I., Jones, D. O. B. & Booth, D. J. A multi-criteria decision approach to decommissioning of offshore oil and gas infrastructure. Ocean Coast. Manag. 87, 20–29 (2014).Article 

Google Scholar 
4.Hamzah, B. A. International rules on decommissioning of offshore installations: some observations. Mar. Policy 27, 339–348 (2003).Article 

Google Scholar 
5.Chandler, J., White, D., Techera, E. J., Gourvenec, S. & Draper, S. Engineering and legal considerations for decommissioning of offshore oil and gas infrastructure in Australia. Ocean Eng. 131, 338–347 (2017).Article 

Google Scholar 
6.Claisse, J. T. et al. Oil platforms off California are among the most productive marine fish habitats globally. Proc. Natl. Acad. Sci. U. S. A. 111, 15462–15467 (2014).ADS 
CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
7.Fowler, A. M. & Booth, D. J. Evidence of sustained populations of a small reef fish on artificial structures. Does depth affect production on artificial reefs?. J. Fish Biol. 80, 613–629 (2012).CAS 
PubMed 
Article 

Google Scholar 
8.Gallaway, B. J., Szedlmayer, S. T. & Gazey, W. J. A life history review for red snapper in the Gulf of Mexico with an evaluation of the importance of offshore petroleum platforms and other artificial reefs. Rev. Fish. Sci. 17, 48–67 (2009).Article 

Google Scholar 
9.Love, M. S. et al. Potential use of offshore marine structures in rebuilding an overfished rockfish species, bocaccio (Sebastes paucispinis). Fish. Bull. 104, 383–390 (2006).
Google Scholar 
10.Friedlander, A. M., Ballesteros, E., Fay, M. & Sala, E. Marine communities on oil platforms in Gabon, West Africa: high biodiversity oases in a low biodiversity environment. PLoS ONE 9, e103709 (2014).ADS 
PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
11.McLean, D. L., Taylor, M. D., Giraldo Ospina, A. & Partridge, J. C. An assessment of fish and marine growth associated with an oil and gas platform jacket using an augmented remotely operated vehicle. Cont. Shelf Res. 179, 66–84 (2019).ADS 
Article 

Google Scholar 
12.Schramm, K. D. et al. A comparison of stereo-BRUVs and stereo-ROV techniques for sampling shallow water fish communities on and off pipelines. Mar. Environ. Res. 162, 105198 (2020).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
13.Schroeder, D. M. & Love, M. S. Ecological and political issues surrounding decommissioning of offshore oil facilities in the Southern California Bight. Ocean Coast. Manag. 47, 21–48 (2004).Article 

Google Scholar 
14.Bull, A. S. & Love, M. S. Worldwide oil and gas platform decommissioning: a review of practices and reefing options. Ocean Coast. Manag. 168, 274–306 (2019).Article 

Google Scholar 
15.Department of Industry, Science, Energy and Resources. Offshore Petroleum Decommissioning Guideline 4 (Department of Industry, Science, Energy and Resources, 2018).
Google Scholar 
16.Bell, N. & Smith, J. Coral growing on North Sea oil rigs. Nature 402, 601–601 (1999).ADS 
CAS 
Article 

Google Scholar 
17.APPEA. Scientific Literature Review. Environmental Impacts of Decommissioning Options (APPEA, 2017).
Google Scholar 
18.Stolk, P., Markwell, K. & Jenkins, J. M. Artificial reefs as recreational scuba diving resources: a critical review of research. J. Sustain. Tour. 15, 331–350 (2007).Article 

Google Scholar 
19.Scarborough-Bull, A., Love, M. S. & Schroeder, D. M. Artificial reefs as fishery conservation tools: contrasting the roles of offshore structures between the Gulf of Mexico and the Southern California Bight. Am. Fish. Soc. Symp. 49, 899–915 (2008).
Google Scholar 
20.Moore, C. H. et al. Improving spatial prioritisation for remote marine regions: optimising biodiversity conservation and sustainable development trade-offs. Sci. Rep. 6, 32029 (2016).ADS 
CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
21.Cripps, S. J. & Aabel, J. P. Environmental and socio-economic impact assessment of Ekoreef, a multiple platform rigs-to-reefs development. ICES J. Mar. Sci. 59, 300–308 (2002).Article 

Google Scholar 
22.Matthews, K. R. Species similarity and movement of fishes on natural and artificial reefs in Monterey bay, California. Bull. Mar. Sci. 37, 252–270 (1985).ADS 

Google Scholar 
23.Grossman, G. D., Jones, G. P. & Seaman, W. J. Jr. Do artificial reefs increase regional fish production? A review of existing data. Fisheries 22, 17–23 (1997).Article 

Google Scholar 
24.Bohnsack, J. A. Are high densities of fishes at artificial reefs the result of habitat limitation or behavioral preference?. Bull. Mar. Sci. 44, 631–645 (1989).
Google Scholar 
25.Page, H. M., Dugan, J. E., Culver, C. S. & Hoesterey, J. C. Exotic invertebrate species on offshore oil platforms. Mar. Ecol. Prog. Ser. 325, 101–107 (2006).ADS 
Article 

Google Scholar 
26.Pajuelo, J. G. et al. Introduction of non-native marine fish species to the Canary Islands waters through oil platforms as vectors. J. Mar. Syst. 163, 23–30 (2016).Article 

Google Scholar 
27.van Elden, S., Meeuwig, J. J., Hobbs, R. J. & Hemmi, J. M. Offshore oil and gas platforms as novel ecosystems: a global perspective. Front. Mar. Sci. 6, 548 (2019).Article 

Google Scholar 
28.Rouse, S., Hayes, P. & Wilding, T. A. Commercial fisheries losses arising from interactions with offshore pipelines and other oil and gas infrastructure and activities. ICES J. Mar. Sci. 77, 1148–1156 (2020).Article 

Google Scholar 
29.Bond, T. et al. Fish associated with a subsea pipeline and adjacent seafloor of the North West Shelf of Western Australia. Mar. Environ. Res. 141, 53–65 (2018).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
30.Bond, T., Partridge, J. C., Taylor, M. D., Cooper, T. F. & McLean, D. L. The influence of depth and a subsea pipeline on fish assemblages and commercially fished species. PLoS ONE 13, e0207703 (2018).PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
31.Bond, T. et al. Diel shifts and habitat associations of fish assemblages on a subsea pipeline. Fish. Res. 206, 220–234 (2018).Article 

Google Scholar 
32.McLean, D. L. et al. Using industry ROV videos to assess fish associations with subsea pipelines. Cont. Shelf Res. 141, 76–97 (2017).ADS 
Article 

Google Scholar 
33.McLean, D. L., Vaughan, B. I., Malseed, B. E. & Taylor, M. D. Fish-habitat associations on a subsea pipeline within an Australian Marine Park. Mar. Environ. Res. 153, 104813 (2020).CAS 
Article 

Google Scholar 
34.Love, M. S. & York, A. A comparison of the fish assemblages associated with an oil/gas pipeline and adjacent seafloor in the Santa Barbara Channel, Southern California Bight. Bull. Mar. Sci. 77, 101–118 (2005).ADS 

Google Scholar 
35.Arnould, J. P. Y. et al. Use of anthropogenic sea floor structures by Australian fur seals: potential positive ecological impacts of marine industrial development?. PLoS ONE 10, e0130581 (2015).PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
36.DMP. Maps and Geospatial Information (Government of Western Australia, Department of Mine, Industry Regulation and Safety, 2020).
Google Scholar 
37.McLean, D. L. et al. Distribution, abundance, diversity and habitat associations of fishes across a bioregion experiencing rapid coastal development. Estuar. Coast. Shelf Sci. 178, 36–47 (2016).ADS 
Article 

Google Scholar 
38.Travers, M. J., Clarke, K. R., Newman, S. J., Hall, N. G. & Potter, I. C. To what extents are species richness and abundance of reef fishes along a tropical coast related to latitude and other factors?. Cont. Shelf Res. 167, 99–110 (2018).ADS 
Article 

Google Scholar 
39.Travers, M. J., Newman, S. J. & Potter, I. C. Influence of latitude, water depth, day v. night and wet v. dry periods on the species composition of reef fish communities in tropical Western Australia. J. Fish Biol. 69, 987–1017 (2006).Article 

Google Scholar 
40.Travers, M. J., Potter, I. C., Clarke, K. R. & Newman, S. J. Relationships between latitude and environmental conditions and the species richness, abundance and composition of tropical fish assemblages over soft substrata. Mar. Ecol. Prog. Ser. 446, 221–241 (2012).ADS 
Article 

Google Scholar 
41.Chevron. Wheatstone Project: Dredging and Dredge Spoil Placement Environmental Monitoring and Management Plan 234 (Chevron Australia Pty Ltd., 2016).
Google Scholar 
42.Gaughan, D. J. et al. (eds) Status Reports of the Fisheries and Aquatic Resources of Western Australia 2017/18: The State of the Fisheries (Department of Primary Industries and Regional Development, 2019).
Google Scholar 
43.Ryan, K. L. et al. Statewide Survey of Boat-Based Recreational Fishing in Western Australia 2017/18. Fisheries Research Report No. 297, Department of Primary Industries and Regional Development (2019).44.Harvey, E. S., Goetze, J., McLaren, B., Langlois, T. & Shortis, M. R. Influence of range, angle of view, image resolution and image compression on underwater stereo-video measurements: high-definition and broadcast-resolution video cameras compared. Mar. Technol. Soc. J. 44, 75–85 (2010).Article 

Google Scholar 
45.Goetze, J. S. et al. A field and video analysis guide for diver operated stereo-video. Methods Ecol. Evol. 10, 1083–1090 (2019).Article 

Google Scholar 
46.Myers, E. M. V., Harvey, E. S., Saunders, B. J. & Travers, M. J. Fine-scale patterns in the day, night and crepuscular composition of a temperate reef fish assemblage. Mar. Ecol. 37, 668–678 (2016).ADS 
Article 

Google Scholar 
47.Sward, D., Monk, J. & Barrett, N. A systematic review of remotely operated vehicle surveys for visually assessing fish assemblages. Front. Mar. Sci. 6, 134 (2019).Article 

Google Scholar 
48.Gregoire, T. G. & Valentine, H. T. Sampling Strategies for Natural Resources and the Environment (CRC Press, 2007).
Google Scholar 
49.Harvey, E. S. & Shortis, M. R. Calibration stability of an underwater stereo-video system: implications for measurement accuracy and precision. Mar. Technol. Soc. J. 32, 3–17 (1998).
Google Scholar 
50.Shortis, M. R. & Harvey, E. S. Design and calibration of an underwater stereo-video system for the monitoring of marine fauna populations. Int. Arch. Photogramm. Remote Sens. 32, 792–799 (1998).
Google Scholar 
51.Shortis, M., Harvey, E. & Abdo, D. A review of underwater stereo-image measurement for marine biology and ecology applications. In Oceanography and Marine Biology Vol. 47 (eds Gibson, R. et al.) 257–292 (Taylor & Francis, 2009).
Google Scholar 
52.Boutros, N., Shortis, M. R. & Harvey, E. S. A comparison of calibration methods and system configurations of underwater stereo-video systems for applications in marine ecology. Limnol. Oceanogr. Methods 13, 224–236 (2015).Article 

Google Scholar 
53.Taylor, R. B. & Willis, T. J. Relationships amongst length, weight and growth of north-eastern New Zealand reef fishes. Mar. Freshw. Res. 49, 255–260 (1998).Article 

Google Scholar 
54.Froese, R. & Pauly, D. FishBase. www.fishbase.org, Accessed Sept 2019 (2019).55.Bach, L. L., Saunders, B. J., Newman, S. J., Holmes, T. H. & Harvey, E. S. Cross and long-shore variations in reef fish assemblage structure and implications for biodiversity management. Estuar. Coast. Shelf Sci. 218, 246–257 (2019).ADS 
Article 

Google Scholar 
56.Anderson, M., Gorley, R. & Clarke, K. P. PERMANOVA+ for PRIMER: Guide to Software and Statistical Methods 1st edn. (PRIMER-E, 2008).
Google Scholar 
57.Anderson, M. J. A new method for non-parametric multivariate analysis of variance. Austral. Ecol. 26, 32–46 (2001).
Google Scholar 
58.Anderson, M. J. Distance-based tests for homogeneity of multivariate dispersions. Biometrics 62, 245–253 (2006).MathSciNet 
PubMed 
MATH 
Article 
PubMed Central 

Google Scholar 
59.Harvey, E. S., Cappo, M., Butler, J. J., Hall, N. & Kendrick, G. A. Bait attraction affects the performance of remote underwater video stations in assessment of demersal fish community structure. Mar. Ecol. Prog. Ser. 350, 245–254 (2007).ADS 
Article 

Google Scholar 
60.Watson, D. L., Harvey, E. S., Anderson, M. J. & Kendrick, G. A. A comparison of temperate reef fish assemblages recorded by three underwater stereo-video techniques. Mar. Biol. 148, 415–425 (2005).Article 

Google Scholar 
61.Langlois, T. J. et al. Cost-efficient sampling of fish assemblages: comparison of baited video stations and diver video transects. Aquat. Biol. 9, 155–168 (2010).Article 

Google Scholar 
62.Simon, T., Pinheiro, H. T. & Joyeux, J.-C. Target fishes on artificial reefs: evidences of impacts over nearby natural environments. Sci. Total Environ. 409, 4579–4584 (2011).ADS 
CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
63.Simon, T., Joyeux, J.-C. & Pinheiro, H. T. Fish assemblages on shipwrecks and natural rocky reefs strongly differ in trophic structure. Mar. Environ. Res. 90, 55–65 (2013).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
64.Wulff, J. L. Ecological interactions of marine sponges. Can. J. Zool. 84, 146–166 (2006).Article 

Google Scholar 
65.Bohnsack, J. A. & Sutherland, D. L. Artificial reef research: a review with recommendations for future priorities. Bull. Mar. Sci. 37, 11–39 (1985).
Google Scholar 
66.Harvey, E. S., Butler, J. J., McLean, D. L. & Shand, J. Contrasting habitat use of diurnal and nocturnal fish assemblages in temperate Western Australia. J. Exp. Mar. Biol. Ecol. 426–427, 78–86 (2012).Article 

Google Scholar 
67.Newman, S. J. & Williams, D. M. Mesh size selection and diel variability in catch of fish traps on the central Great Barrier Reef, Australia: a preliminary investigation. Fish. Res. 23, 237–253 (1995).Article 

Google Scholar 
68.Nagelkerken, I., Dorenbosch, M., Verberk, W. & van der Cocheret de la Morinière Velde, E. G. Day-night shifts of fishes between shallow-water biotopes of a Caribbean bay, with emphasis on the nocturnal feeding of Haemulidae and Lutjanidae. Mar. Ecol. Prog. Ser. 194, 55–64 (2000).ADS 
Article 

Google Scholar 
69.Currey, L. M., Heupel, M. R., Simpfendorfer, C. A. & Williams, A. J. Assessing fine-scale diel movement patterns of an exploited coral reef fish. Anim. Biotelem. 3, 41 (2015).Article 

Google Scholar 
70.Newman, S. J. & Williams, D. M. Spatial and temporal variation in assemblages of Lutjanidae, Lethrinidae and associated fish species among mid-continental shelf reefs in the central Great Barrier Reef. Mar. Freshw. Res. 52, 843–851 (2001).Article 

Google Scholar 
71.Layman, C. A., Allgeier, J. E., Yeager, L. A. & Stoner, E. W. Thresholds of ecosystem response to nutrient enrichment from fish aggregations. Ecology 94, 530–536 (2013).PubMed 
Article 

Google Scholar 
72.Shantz, A. A., Ladd, M. C., Schrack, E. & Burkepile, D. E. Fish-derived nutrient hotspots shape coral reef benthic communities. Ecol. Appl. 25, 2142–2152 (2015).PubMed 
Article 

Google Scholar 
73.Marnane, M. J. & Bellwood, D. R. Diet and nocturnal foraging in cardinalfishes (Apogonidae) at One Tree Reef, Great Barrier Reef, Australia. Mar. Ecol. Prog. Ser. 231, 261–268 (2002).ADS 
Article 

Google Scholar 
74.Wen, C. K. C., Pratchett, M. S., Almany, G. R. & Jones, G. P. Patterns of recruitment and microhabitat associations for three predatory coral reef fishes on the southern Great Barrier Reef, Australia. Coral Reefs 32, 389–398 (2013).ADS 
Article 

Google Scholar 
75.Friedlander, A. M. & Parrish, J. D. Habitat characteristics affecting fish assemblages on a Hawaiian coral reef. J. Exp. Mar. Biol. Ecol. 224, 1–30 (1998).Article 

Google Scholar 
76.Pradella, N., Fowler, A. M., Booth, D. J. & Macreadie, P. I. Fish assemblages associated with oil industry structures on the continental shelf of north-western Australia. J. Fish Biol. 84, 247–255 (2014).CAS 
PubMed 
Article 

Google Scholar 
77.McLean, D. L. et al. Fish and habitats on wellhead infrastructure on the north west shelf of Western Australia. Cont. Shelf Res. 164, 10–27 (2018).ADS 
Article 

Google Scholar 
78.Frisch, A. J. Are juvenile coral-trouts (Plectropomus) mimics of poisonous pufferfishes (Canthigaster) on coral reefs?. Mar. Ecol. 27, 247–252 (2006).ADS 
Article 

Google Scholar 
79.Wen, C. K. C., Pratchett, M. S., Almany, G. R. & Jones, G. P. Role of prey availability in microhabitat preferences of juvenile coral trout (Plectropomus: Serranidae). J. Exp. Mar. Biol. Ecol. 443, 39–45 (2013).Article 

Google Scholar 
80.Kerry, J. T. & Bellwood, D. R. The effect of coral morphology on shelter selection by coral reef fishes. Coral Reefs 31, 415–424 (2012).ADS 
Article 

Google Scholar 
81.Lindfield, S. J., Harvey, E. S., McIlwain, J. L. & Halford, A. R. Silent fish surveys: bubble-free diving highlights inaccuracies associated with SCUBA-based surveys in heavily fished areas. Methods Ecol. Evol. 5, 1061–1069 (2014).Article 

Google Scholar 
82.Kulbicki, M. How the acquired behaviour of commercial reef fishes may influence the results obtained from visual censuses. J. Exp. Mar. Biol. Ecol. 222, 11–30 (1998).Article 

Google Scholar 
83.Gray, A. E. et al. Comparison of reef fish survey data gathered by open and closed circuit SCUBA divers reveals differences in areas with higher fishing pressure. PLoS ONE 11, e0167724 (2016).PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
84.Guidetti, P., Vierucci, E. & Bussotti, S. Differences in escape response of fish in protected and fished Mediterranean rocky reefs. J. Mar. Biol. Assoc. U. K. 88, 625–627 (2008).Article 

Google Scholar 
85.Laidig, T. E., Krigsman, L. M. & Yoklavich, M. M. Reactions of fishes to two underwater survey tools, a manned submersible and a remotely operated vehicle. Fish. Bull. 111, 54–67 (2013).
Google Scholar 
86.Sutton, S. G. & Bushnell, S. L. Socio-economic aspects of artificial reefs: considerations for the Great Barrier Reef Marine Park. Ocean Coast. Manag. 50, 829–846 (2007).Article 

Google Scholar 
87.Florisson, J. H. et al. King Reef: an Australian first in repurposing oil and gas infrastructure to benefit regional communities. APPEA J. 60, 435–439 (2020).Article 

Google Scholar 
88.Rouse, S., Kafas, A., Catarino, R. & Peter, H. Commercial fisheries interactions with oil and gas pipelines in the North Sea: considerations for decommissioning. ICES J. Mar. Sci. 75, 279–286 (2018).Article 

Google Scholar 
89.Gratwicke, B. & Speight, M. R. The relationship between fish species richness, abundance and habitat complexity in a range of shallow tropical marine habitats. J. Fish Biol. 66, 650–667 (2005).Article 

Google Scholar 
90.Charbonnel, E., Serre, C., Ruitton, S., Harmelin, J.-G. & Jensen, A. Effects of increased habitat complexity on fish assemblages associated with large artificial reef units (French Mediterranean coast). ICES J. Mar. Sci. 59, 208–213 (2002).Article 

Google Scholar 
91.Perkol-Finkel, S., Shashar, N. & Benayahu, Y. Can artificial reefs mimic natural reef communities? The roles of structural features and age. Mar. Environ. Res. 61, 121–135 (2006).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
92.Burt, J., Bartholomew, A., Usseglio, P., Bauman, A. & Sale, P. F. Are artificial reefs surrogates of natural habitats for corals and fish in Dubai, United Arab Emirates?. Coral Reefs 28, 663–675 (2009).ADS 
Article 

Google Scholar 
93.Folpp, H., Lowry, M., Gregson, M. & Suthers, I. M. Fish assemblages on estuarine artificial reefs: natural rocky-reef mimics or discrete assemblages?. PLoS ONE 8, e63505 (2014).ADS 
PubMed 
Article 
CAS 
PubMed Central 

Google Scholar  More

1.Berthrong, S. T. et al. Nitrogen fertilization has a stronger effect on soil nitrogen-fixing bacterial communities than elevated atmospheric CO2. Appl. Environ. Microb. 80, 3103–3112. https://doi.org/10.1128/AEM.04034-13 (2014).CAS 
Article 

Google Scholar 
2.Millar, N., Robertson, G. P., Grace, P. R., Gehl, R. J. & Hoben, J. P. Nitrogen fertilizer management for nitrous oxide (N2O) mitigation in intensive corn (Maize) production: An emissions reduction protocol for US Midwest agriculture. Mitig. Adapt. Strat. Gl. 15, 185–204. https://doi.org/10.1007/s11027-010-9212-7 (2010).Article 

Google Scholar 
3.Zhou, J. et al. Influence of 34-years of fertilization on bacterial communities in an intensively cultivated black soil in northeast China. Soil Biol. Biochem. 90, 42–51. https://doi.org/10.1016/j.soilbio.2015.07.005 (2015).CAS 
Article 

Google Scholar 
4.Ding, J. et al. Influence of inorganic fertilizer and organic manure application on fungal communities in a long-term field experiment of Chinese Mollisols. Appl. Soil Ecol. 111, 114–122. https://doi.org/10.1016/j.apsoil.2016.12.003 (2017).ADS 
Article 

Google Scholar 
5.Zhou, J. et al. Thirty four years of nitrogen fertilization decreases fungal diversity and alters fungal community composition in black soil in northeast China. Soil Biol. Biochem. 95, 135–143. https://doi.org/10.1016/j.soilbio.2015.12.012 (2016).CAS 
Article 

Google Scholar 
6.Liu, J. et al. Diversity and distribution patterns of acidobacterial communities in the black soil zone of northeast China. Soil Biol. Biochem. 95, 212–222. https://doi.org/10.1016/j.soilbio.2015.12.021 (2016).CAS 
Article 

Google Scholar 
7.Pan, H. et al. Organic and inorganic fertilizers respectively drive bacterial and fungal community compositions in a fluvo-aquic soil in northern China. Soil Till. Res. 198, 104540. https://doi.org/10.1016/j.still.2019.104540 (2020).Article 

Google Scholar 
8.Ma, M. et al. Chronic fertilization of 37 years alters the phylogenetic structure of soil arbuscular mycorrhizal fungi in Chinese Mollisols. AMB Express 8, 57. https://doi.org/10.1186/s13568-018-0587-2 (2018).Article 
PubMed 
PubMed Central 

Google Scholar 
9.Hu, X. et al. Long-term manure addition reduces diversity and changes community structure of diazotrophs in a neutral black soil of northeast China. J. Soils Sediments 18, 2053–2062. https://doi.org/10.1007/s11368-018-1975-6 (2018).CAS 
Article 

Google Scholar 
10.Liu, J. et al. Ammonia-oxidizing archaea show more distinct biogeographic distribution patterns than ammonia-oxidizing bacteria across the black soil zone of northeast China. Front. Microbial. 9, 171. https://doi.org/10.3389/fmicb.2019.00023 (2018).Article 

Google Scholar 
11.Fan, K., Delgado-Baquerizo, M., Guo, X., Wang, D. & Chu, H. Suppressed N fixation and diazotrophs after four decades of fertilization. Microbiome 7, 143. https://doi.org/10.1186/s40168-019-0757-8 (2019).Article 
PubMed 
PubMed Central 

Google Scholar 
12.Kumar, U. et al. Long-term aromatic rice cultivation effect on frequency and diversity of diazotrophs in its rhizosphere. Ecol. Eng. 101, 227–236. https://doi.org/10.1016/j.ecoleng.2017.02.010 (2017).Article 

Google Scholar 
13.Gaby, J. C., Rishishwar, L., Valderrama-Aguirre, L. C., Green, S. J. & Kostka, J. E. Diazotroph community characterization via a high-throughput nifH amplicon sequencing and analysis pipeline. Appl. Environ. Microbiol. 84, 01512–01517. https://doi.org/10.1128/AEM.01512-17 (2018).Article 

Google Scholar 
14.Wang, J. et al. Temporal variation of diazotrophic community abundance and structure in surface and subsoil under four fertilization regimes during a wheat growing season. Agric. Ecosyst. Environ. 216, 116–124. https://doi.org/10.1016/j.agee.2015.09.039 (2016).CAS 
Article 

Google Scholar 
15.Van Kessel, C. & Hartley, C. Agricultural management of grain legumes: Has it led to an increase in nitrogen fixation?. Field Crops Res. 65, 165–181. https://doi.org/10.1016/S0378-4290(99)00085-4 (2000).Article 

Google Scholar 
16.Wang, C. et al. Impact of 25 years of inorganic fertilization on diazotrophic abundance and community structure in an acidic soil in southern China. Soil Biol. Biochem. 113, 240–249. https://doi.org/10.1016/j.soilbio.2017.06.019 (2017).CAS 
Article 

Google Scholar 
17.Feng, M. et al. Long-term fertilization influences community assembly processes of soil diazotrophs. Soil Biol. Biochem. 126, 151–158. https://doi.org/10.1016/j.soilbio.2018.08.021 (2018).CAS 
Article 

Google Scholar 
18.Fan, L. Response of diazotrophic microbial community to nitrogen input and glyphosate application in soils cropped to soybean. (2013).19.Cheng, F. et al. Isolation and application of effective nitrogen fixation rhizobial strains on low-phosphorus acid soils in South China. Chin. Sci. Bull. 54, 412–420. https://doi.org/10.1007/s11434-008-0521-0 (2009).CAS 
Article 

Google Scholar 
20.Qiao, Y. et al. The effect of fertilizer practices on N balance and global warming potential of maize–soybean–wheat rotations in Northeastern China. Field Crops Res. 161, 98–106. https://doi.org/10.1016/j.fcr.2014.03.005 (2014).Article 

Google Scholar 
21.Hsu, S. F. & Buckley, D. H. Evidence for the functional significance of diazotroph community structure in soil. ISME J. 3, 124–136. https://doi.org/10.1038/ismej.2008.82 (2009).CAS 
Article 
PubMed 

Google Scholar 
22.Chen, J., Shen, W., Xu, H., Li, Y. & Luo, T. The composition of nitrogen-fixing microorganisms correlates with soil nitrogen content during reforestation: A comparison between legume and non-legume plantations. Front. Microbiol. 10, 508. https://doi.org/10.3389/fmicb.2019.00508 (2019).Article 
PubMed 
PubMed Central 

Google Scholar 
23.Saleem, M., Law, A. D., Sahib, M. R., Pervaiz, Z. H. & Zhang, Q. Impact of root system architecture on rhizosphere and root microbiome. Rhizosphere 6, 47–51. https://doi.org/10.1016/j.rhisph.2018.02.003 (2018).Article 

Google Scholar 
24.Zhang, X. et al. Response of the abundance of key soil microbial nitrogen-cycling genes to multi-factorial global changes. PLoS ONE 8, e76500. https://doi.org/10.1371/journal.pone.0076500 (2013).ADS 
CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
25.Coelho, M. et al. Molecular detection and quantification of nifH gene sequences in the rhizosphere of sorghum (Sorghum bicolor) sown with two levels of nitrogen fertilizer. Appl. Soil Ecol. 42, 48–53. https://doi.org/10.1016/j.apsoil.2009.01.010 (2009).Article 

Google Scholar 
26.Wakelin, S. A. et al. The effects of stubble retention and nitrogen application on soil microbial community structure and functional gene abundance under irrigated maize. Fems Microbiol. Ecol. 59, 661–670. https://doi.org/10.1111/j.1574-6941.2006.00235.x (2006).CAS 
Article 
PubMed 

Google Scholar 
27.Shirani, H., Hajabbasi, M. A., Afyuni, M. & Hemmat, A. Effects of farmyard manure and tillage systems on soil physical properties and corn yield in central Iran. Soil Till. Res. 68, 101–108. https://doi.org/10.1016/S0167-1987(02)00110-1 (2002).Article 

Google Scholar 
28.Sheffer, E., Batterman, S. A., Levin, S. A. & Hedin, L. O. Biome-scale nitrogen fixation strategies selected by climatic constraints on nitrogen cycle. Nat. Plants 1, 15182. https://doi.org/10.1038/nplants.2015.182 (2015).CAS 
Article 
PubMed 

Google Scholar 
29.Guo, J. H. et al. Significant acidification in major Chinese croplands. Science 327, 1008–1010. https://doi.org/10.1126/science.1182570 (2010).ADS 
CAS 
Article 
PubMed 

Google Scholar 
30.Ding, J. et al. Effect of 35 years inorganic fertilizer and manure amendment on structure of bacterial and archaeal communities in black soil of northeast China. Appl. Soil Ecol. 105, 187–195. https://doi.org/10.1016/j.apsoil.2016.04.010 (2016).Article 

Google Scholar 
31.Soman, C., Keymer, D. P. & Kent, A. D. Edaphic correlates of feedstock-associated diazotroph communities. GCB Bioenergy 10, 343–352. https://doi.org/10.1111/gcbb.12502 (2018).CAS 
Article 

Google Scholar 
32.He, D. et al. Evolvement of structure and abundance of soil nitrogen-fixing bacterial community in Phyllostachys edulis plantations with age of time. Acta Pedol. Sin. 52, 934–942. https://doi.org/10.11766/trxb201408070397 (2015).Article 

Google Scholar 
33.Ning, Q. et al. Effects of nitrogen deposition rates and frequencies on the abundance of soil nitrogen-related functional genes in temperate grassland of northern China. J. Soils Sediments 15, 694–704. https://doi.org/10.1007/s11368-015-1061-2 (2015).CAS 
Article 

Google Scholar 
34.Huang, J. et al. Responses of soil nitrogen fixation to Spartina alterniflora invasion and nitrogen addition in a Chinese salt marsh. Sci. Rep. 6, 20384. https://doi.org/10.1038/srep20384 (2016).ADS 
CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
35.Zhu, C. et al. N-fertilizer-driven association between the arbuscular mycorrhizal fungal community and diazotrophic community impacts wheat yield. Agric. Ecosyst. Environ. 254, 191–201. https://doi.org/10.1016/j.agee.2017.11.029 (2018).Article 

Google Scholar 
36.Coelho, M. et al. Diversity of nifH gene pools in the rhizosphere of two cultivars of sorghum (Sorghum bicolor) treated with contrasting levels of nitrogen fertilizer. FEMS Microbiol. Lett. 111, 114–122. https://doi.org/10.1111/j.1574-6968.2007.00975.x (2007).CAS 
Article 

Google Scholar 
37.Velagaleti, R. R. & Marsh, S. Influence of host cultivars and Bradyrhizobium strains on the growth and symbiotic performance of soybean under salt stress. Plant Soil 119, 133–138. https://doi.org/10.1007/BF02370277 (1989).Article 

Google Scholar 
38.Appunu, C. & Dhar, B. Symbiotic effectiveness of acid-tolerant Bradyrhizobium strains with soybean in low pH soil. Afr. J. Biotechnol. https://doi.org/10.5897/AJB06.131 (2006).Article 

Google Scholar 
39.Kunert, K. J. et al. Drought stress responses in soybean roots and nodules. Front. Plant Sci. 7, 1015. https://doi.org/10.3389/fpls.2016.01015 (2016).Article 
PubMed 
PubMed Central 

Google Scholar 
40.Ahemad, M. & Khan, M. S. Insecticide-tolerant and plant growth promoting Bradyrhizobium sp. (vigna) improves the growth and yield of greengram [Vigna radiata (L.) Wilczek] in insecticide-stressed soils. Symbiosis 54, 17–27. https://doi.org/10.1007/s13199-011-0122-6 (2011).CAS 
Article 

Google Scholar 
41.Chen, J., Zhou, Z. & Gu, J. Occurrence and diversity of nitrite-dependent anaerobic methane oxidation bacteria in the sediments of the South China Sea revealed by amplification of both 16S rRNA and pmoAgenes. Appl. Microbiol. Biotechnol. 98, 5685–5696. https://doi.org/10.1007/s00253-014-5733-4 (2014).CAS 
Article 
PubMed 

Google Scholar 
42.Santoscaton, I. R., Caton, T. M. & Schneegurt, M. A. Nitrogen-fixation activity and the abundance and taxonomy of nifH genes in agricultural, pristine, and urban prairie stream sediments chronically exposed to different levels of nitrogen loading. Arch. Microbiol. https://doi.org/10.1007/s00203-018-1475-5 (2018).Article 

Google Scholar 
43.Zhou, J. et al. Effects of long term application of urea on ammonia oxidizing archaea community in black soil in Northeast China. Sci. Agric. Sin. 49, 294–304. https://doi.org/10.3864/j.issn.0578-1752.2016.02.010 (2016).CAS 
Article 

Google Scholar 
44.Zhou, J. et al. Consistent effects of nitrogen fertilization on soil bacterial communities in black soils for two crop seasons in China. Sci. Rep. 7, 3267. https://doi.org/10.1038/s41598-017-03539-6 (2017).ADS 
CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
45.Diedrick, K. A. Field Investigations of Nitrogen Fertility on Corn and Soybeans and Foliar Manganese-Glyphosate Interactions on Glyphosate-Tolerant Soybeans in Ohio (The Ohio State University, 2010).
Google Scholar 
46.Salamone, I., Bereiner, J., Urquiaga, S. & Boddey, R. Biological nitrogen fixation in Azospirillumstrain-maize genotype associations as evaluated by the 15N isotope dilution technique. Biol. Fertil. Soils 23, 249–256. https://doi.org/10.1007/BF00335952 (1996).Article 

Google Scholar 
47.Carelli, M. et al. Genetic diversity and dynamics of sinorhizobium meliloti populations nodulating different alfalfa cultivars in Italiansoils. Appl. Environ. Microbiol. 66, 4785–4789. https://doi.org/10.1128/AEM.66.11.4785-4789.2000 (2000).CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
48.Coelho, M. R. et al. Diversity of Paenibacillus spp. in the rhizosphere of four sorghum (Sorghum bicolor) cultivars sown with two contrasting levels of nitrogen fertilizer assessed by rpoB-based PCR-DGGE and sequencing analysis. J. Microbiol. Biotechnol. 17, 753–760. https://doi.org/10.1007/s10295-007-0209-5 (2007).CAS 
Article 
PubMed 

Google Scholar 
49.Cao, Y., Wang, E., Zhao, L., Chen, W. & Wei, G. Diversity and distribution of rhizobia nodulated with Phaseolus vulgaris in two ecoregions of China. Soil Biol. Biochem. 78, 128–137. https://doi.org/10.1016/j.soilbio.2014.07.026 (2014).CAS 
Article 

Google Scholar 
50.Ahmed, I. H., Francina, L. B., Isabella, H. R. & Galaletsang, S. Nodulation efficacy of Bradyrhizobium japonicum inoculant strain WB74 on soybean (Glycine max L. Merrill) is affected by several limiting factors. Afr. J. Microbiol. Res. 8, 2069–2076. https://doi.org/10.5897/ajmr2014.6709 (2014).Article 

Google Scholar 
51.Yan, J. et al. Effects of long-term fertilization strategies on soil productivity and rhizobial diversity in Chinese mollisol. Pedosphere 29, 784–793. https://doi.org/10.1016/S1002-0160(17)60470-3 (2019).Article 

Google Scholar 
52.Riffkin, P. A., Quigley, P. E., Kearney, G. A., Cameron, F. J. & Thies, J. E. Factors associated with biological nitrogen fixation in dairy pastures in south-western Victoria. Aust. J. Agric. Res. 50, 261–272. https://doi.org/10.1071/a98035 (1999).Article 

Google Scholar 
53.Yang, L. et al. Diazotroph abundance and community structure are reshaped by straw return and mineral fertilizer in rice-rice-green manure rotation. Appl. Soil Ecol. 136, 11–20. https://doi.org/10.1016/j.apsoil.2018.12.015 (2019).Article 

Google Scholar 
54.Zou, Y. et al. Effects of different land use patterns on nifH genetic diversity of soil nitrogen-fixing microbial communities in Leymus Chinensis steppe. Acta Ecol. Sin. 31, 150–156 (2011).Article 

Google Scholar 
55.Zahran, H. H. Rhizobium-Legume symbiosis and nitrogen fixation under severe conditions and in an arid climate. Microbiol. Mol. Biol. R 63, 968–989. https://doi.org/10.1016/j.chnaes.2011.03.004 (1999).CAS 
Article 

Google Scholar 
56.Tang, Y. et al. Impact of fertilization regimes on diazotroph community compositions and N2-fixation activity in paddy soil. Agriculture, Ecosystems & Environment: An International Journal for Scientific Research on the Relationship of Agriculture and Food Production to the Biosphere (2017).57.Gao, P., Li, Y., Tan, L., Guo, F. & Ma, T. Composition of bacterial and archaeal communities in an alkali-surfactant-polyacrylamide-flooded oil reservoir and the responses of microcosms to nutrients. Front. Microbiol. 10, 2197. https://doi.org/10.3389/fmicb.2019.02197 (2019).Article 
PubMed 
PubMed Central 

Google Scholar 
58.Rösch, C., Mergel, A. & Bothe, H. Biodiversity of denitrifying and dinitrogen-fixing bacteria in an acid forest soil. Appl. Enviro. Microbiol. 68, 3818–3829. https://doi.org/10.1128/AEM.68.8.3818-3829.2002 (2002).CAS 
Article 

Google Scholar 
59.Wei, G. et al. Similar drivers but different effects lead to distinct ecological patterns of soil bacterial and archaeal communities. Soil Biol. Biochem. 144, 107759. https://doi.org/10.1016/j.soilbio.2020.107759 (2020).CAS 
Article 

Google Scholar 
60.Sun, R., Guo, X., Wang, D. & Chu, H. Effects of long-term application of chemical and organic fertilizers on the abundance of microbial communities involved in the nitrogen cycle. Appl. Soil Ecol. 95, 171–178. https://doi.org/10.1016/j.apsoil.2015.06.010 (2015).Article 

Google Scholar 
61.Asnicar, F., Weingart, G., Tickle, T. L., Huttenhower, C. & Segata, N. Compact graphical representation of phylogenetic data and metadata with GraPhlAn. PeerJ 3, 1029. https://doi.org/10.7717/peerj.1029 (2015).Article 

Google Scholar 
62.Gao, P. et al. Spatial isolation and environmental factors drive distinct bacterial and archaeal communities in different types of petroleum reservoirs in China. Sci. Rep. 6, 20174. https://doi.org/10.1038/srep20174 (2016).ADS 
CAS 
Article 
PubMed 
PubMed Central 

Google Scholar  More