Ecology

1.Neumann, V. H., Borrego, A. G., Cabrera, L. & Dino, R. Organic matter composition and distribution through the Aptian-Albian lacustrine sequences of the Araripe Basin, northeastern Brazil. Int. J. Coal. Geol. 54, 21–40. https://doi.org/10.1016/S0166-5162(03)00018-1 (2003).CAS 
Article 

Google Scholar 
2.Heimhofer, U. & Martill, D. M. Stratigraphy of the Crato Formation. In The Crato Fossil Beds of Brazil: Window into an Ancient World (eds Martill, D. M. et al.) 25–43 (Cambridge University Press, 2007).
Google Scholar 
3.Neumann, V. H. M. L. Estratigrafía, sedimentología, geoquímica y diagénesis de los sistemas lacustres Aptienses-Albienses de la Cuenca de Araripe (Noreste de Brasil) (Universidad de Barcelona, 1999).
Google Scholar 
4.Martill, D. M. The geology of the Crato Formation. In The Crato Fossil Beds of Brazil: Window into an Ancient World (eds Martill, D. M. et al.) 8–24 (Cambridge University Press, 2007).
Google Scholar 
5.Martill, D. M. & Wilby, P. R. Stratigraphy. In Fossils of the Santana and Crato Formations, Brazil (ed. Martill, D. M.) 20–50 (The Palaeontological Association Field Guides to Fossils, 1993).
Google Scholar 
6.Heimhofer, U. et al. Deciphering the depositional environment of the laminated Crato fossil beds (Early Cretaceous, Araripe Basin, North-eastern Brazil). Sedimentology 57(2), 677–694. https://doi.org/10.1111/j.1365-3091.2009.01114.x (2010).ADS 
CAS 
Article 

Google Scholar 
7.Martínez-Delclòs, X., Briggs, D. E. G. & Peñalver, E. Taphonomy of insects in carbonates and amber. Palaeogeogr. Palaeoclimatol. Palaeoecol. 203, 19–64. https://doi.org/10.1016/S0031-0182(03)00643-6 (2004).Article 

Google Scholar 
8.Menon, F. & Martill, D. M. Taphonomy and preservation of Crato Formation arthropods. In The Crato Fossil Beds of Brazil: Window into an Ancient World (eds Martill, D. M. et al.) 79–96 (Cambridge University Press, 2007).
Google Scholar 
9.Martins-Neto, R. G. New mayflies (Insecta, Ephemeroptera) from the Santana Formation (Lower Cretaceous), Araripe Basin, northeastern Brazil. Rev. Esp. Paleontol. 11(2), 177–192 (1996).
Google Scholar 
10.Brito, P. M. The Crato Formation fish fauna. In The Crato Fossil Beds of Brazil: Window into an ancient world (eds Martill, D. M. et al.) 429–443 (Cambridge University Press, 2007).
Google Scholar 
11.Sinitshenkova, N. D. The Mesozoic mayflies (Ephemeroptera) with special reference to their ecology. In 4th International Conference of Ephemeroptera (eds Landa, V. et al.) 61–66 (Czechoslovak Academy of Science, 1984).
Google Scholar 
12.Martill, D. M., Brito, P. M. & Washington-Evans, J. Mass mortality of fishes in the Santana Formation (Lower Cretaceous, Albian) of northeast Brazil. Cretac. Res. 29(4), 649–658. https://doi.org/10.1016/j.cretres.2008.01.012 (2008).Article 

Google Scholar 
13.Martins-Neto, R. G. Insetos fósseis como bioindicadores em depósitos sedimentares: um estudo de caso para o Cretáceo da Bacia do Araripe (Brasil). Rev. Bras. Zoociências. 8(2), 155–183 (2006).
Google Scholar 
14.Bechly, G. et al. A revision and phylogenetic study of Mesozoic Aeshnoptera, with description of several new families, genera and species (Insecta: Odonata: Anisoptera). Neue Paläontologische Abhandlungen. 4, 1–219 (2001).
Google Scholar 
15.Martins-Neto, R. G. & Gallego, O. F. Death behaviour”—Thanatoethology, new term and concept: A taphonomic analysis providing possible paleoethologic inferences. Special cases from arthropods of the santana formation (Lower Cretaceous, Northeast Brazil). Geociências. 25(2), 241–254 (2006).
Google Scholar 
16.Osés, G. L. et al. Deciphering the preservation of fossil insects: A case study from the Crato Member, Early Cretaceous of Brazil. PeerJ. 4, e2756. https://doi.org/10.7717/peerj.2756 (2016).CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
17.Saraiva, A. A. F., Hessel, M. H., Guerra, N. C. & Fara, E. Concreções Calcárias da Formação Santana, Bacia do Araripe: uma proposta de classificação. Estud. Geol. 17(1), 40–58 (2007).
Google Scholar 
18.Assine, M. L. Bacia do Araripe. Boletim de Geociências da Petrobras. 15(2), 371–389 (2007).
Google Scholar 
19.Neumann, V. H. & Cabrera, L. Una nueva propuesta estratigráfica para la tectonosecuencia post-rifte de la cuenca de Araripe, nordeste de Brasil. Boletim do 5° Simpósio sobre o Cretáceo do Brasil. 279–285 (1999).

Google Scholar 
20.Viana, M. S. & Neumann, V. H. L. Membro Crato da Formação Santana, Chapada do Araripe, CE-Riquíssimo registro de fauna e flora do Cretáceo. In Sítios Geológicos e Paleontológicos do Brasil (eds Schobbenhaus, C. et al.) 113–120 (Comissão Brasileira de Sítios Geológicos e Paleobiológicos, 2002).
Google Scholar 
21.Staniczek, A. H. Ephemeroptera: Mayflies. In The Crato Fossil Beds of Brazil: Window into an Ancient World (eds Martill, D. M. et al.) 163–184 (Cambridge University Press, 2007).
Google Scholar 
22.Datta, D., Mukherjee, D. & Ray, S. Taphonomic signatures of a new Upper Triassic phytosaur (Diapsida, Archosauria) bonebed from India: Aggregation of a juvenile-dominated paleocommunity. J. Vertebr. Paleontol. 39(6), e1726361 (2020).Article 

Google Scholar 
23.Barling, N. The Fidelity of Preservation of Insects from the Crato Formation (Lower Cretaceous) of Brazil (University of Portsmouth, 2018).
Google Scholar 
24.Boucot, A. J. Evolutionary Paleobiology of Behavior and Coevolution (Elsevier, 1990).
Google Scholar 
25.Grande, L. Palaeontology of the Green River Formation, with a Review of the Fish Fauna 2nd edn, Vol. 63, 1–333 (Geological Survey of Wyoming Bulletin, 1984).
Google Scholar 
26.McCafferty, W. P. Chapter 2. Ephemeroptera. Bull. Am. Mus. Nat. Hist. 195, 20–50 (1990).
Google Scholar 
27.Meshkova, N. P. On nymph Ephemeropsis trisetalis Eichwald (Insecta). Paleontol. Zh. 4, 164–168 (1961).
Google Scholar 
28.Polegatto, C. M. & Zamboni, J. C. Inferences regarding the feeding behavior and morphoecological patterns of fossil mayfly nymphs (Insecta Ephemeroptera) from the Lower Cretaceous Santana Formation of northeastern Brazil. Acta. Geol. Leopold. 24, 145–160 (2001).
Google Scholar 
29.Bouchard, R. W. Guide to Aquatic Macroinvertebrates of the Upper Midwest (University of Minnesota, 2004).
Google Scholar 
30.Tshernova, O. A. On the classification of Fossil and Recent Ephemeroptera. Entomol. Rev. 49, 71–81 (1970).
Google Scholar 
31.Braz, F. F. Registro angiospérmico Eocretáceo do Membro Crato, Formação Santana, Bacia do Araripe, NE do Brasil: Interpretações paleoambientais, paleoclimáticas e paleofitogeográficas (Universidade de São Paulo, 2012).
Google Scholar 
32.Archibald, S. B. & Makarkin, V. N. Tertiary giant lacewings (Neuroptera: Polystoechotidae): Revision and description of new taxa from western North America and Denmark. J. Syst. Palaeontol. 4, 1–37. https://doi.org/10.1017/S1477201906001817 (2005).Article 

Google Scholar 
33.Boyero, L., Cardinale, B. J., Bastian, M. & Pearson, R. G. Biotic vs abiotic control of decomposition: A comparison of the effects of simulated extinctions and changes in temperature. PLoS ONE 9(1), e87426. https://doi.org/10.1371/journal.pone.0087426 (2014).ADS 
CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
34.Gall, J. C. Les Voiles Microbiens. Leur Contribution à la Fossilisation des Organismes au Corps Mou. Lethaia 23, 21–28 (1990).Article 

Google Scholar 
35.Martill, D. M. Fish oblique to bedding in early diagenetic concretions from the Cretaceous Santana Formation of Brazil e implications for substrate consistency. Palaeontology 41, 1011–1026 (1997).
Google Scholar 
36.Iniesto, M. et al. Soft tissue histology of insect larvae decayed in laboratory experiments using microbial mats: Taphonomic comparison with Cretaceous fossil insects from the exceptionally preserved biota of Araripe, Brazil. Palaeogeogr. Palaeoclimatol. Palaeoecol. 564, 110156. https://doi.org/10.1016/j.palaeo.2020.110156 (2021).Article 

Google Scholar 
37.Kok, M. D., Schouten, S. & Damsté, J. S. S. Formation of insoluble, nonhydrolyzable, sulfur-rich macromolecules via incorporation of inorganic sulfur species into algal carbohydrates. Geochim. Cosmochim. Acta. 64, 2689–2699. https://doi.org/10.1016/S0016-7037(00)00382-3 (2000).ADS 
CAS 
Article 

Google Scholar 
38.Kluge, N. J. The Phylogenetic System of Ephemeroptera (Kluwer Academic, 2004). https://doi.org/10.1007/978-94-007-0872-3.
Google Scholar 
39.Camp, A. A., Funk, D. H. & Buchwalter, D. B. A stressful shortness of breath: Molting disrupts breathing in the mayfly Cloeon dipterum. Freshw. Sci. 33(3), 695–699. https://doi.org/10.1086/677899 (2014).Article 

Google Scholar 
40.Mohr, B. A. R., Bernardes-De-Oliveira, M. E. C. & Loveridge, R. F. The macrophyte flora of the Crato Formation. In The Crato Fossil Beds of Brazil: Window into an Ancient World (eds Martill, D. M. et al.) 537–565 (Cambridge University Press, 2007).
Google Scholar 
41.Kunzmann, L., Mohr, B. A. R. & Bernardes-De-Oliveira, M. E. C. Gymnosperms from the Early Cretaceous Crato Formation (Brazil). I. Araucariaceae and Lindleycladus (incertae sedis). Foss. Rec. 7, 155–174. https://doi.org/10.1002/mmng.20040070109 (2004).Article 

Google Scholar 
42.Mohr, B., Schultka, S., Süss, H. & Bernardes-De Oliveira, M. E. C. A new drought resistant gymnosperm taxon Duartenia araripensis gen. nov. et sp. nov. (Cheirolepidiaceae?) from the Early Cretaceous of Northern Gondwana. Palaeontogr. Abt. B. 289(1–3), 1–25. https://doi.org/10.1127/palb/289/2012/1 (2012).Article 

Google Scholar 
43.Bernardes-De-Oliveira, M. E. C. et al. Indicadores Paleoclimáticos na Paleoflora do Crato, final do Aptiano do Gondwana Norocidental. In Paleontologia: Cenários de Vida-Paleoclimas (eds Carvalho, I. S. et al.) 100–118 (Editora Interciência, 2013).
Google Scholar 
44.Kershaw, P. & Wagstaff, B. The Southern Conifer Family Araucariaceae: History, status, and value for paleoenvironmental reconstruction. Annu. Rev. Ecol. Syst. 32, 397–414. https://doi.org/10.1146/annurev.ecolsys.32.081501.114059 (2001).Article 

Google Scholar 
45.Lima, F. J. et al. Fire in the paradise: Evidence of repeated palaeo-wildfires from the Araripe Fossil Lagerstätte (Araripe Basin, Aptian-Albian), Northeast Brazil. Palaeobio. Palaeoenv. 99, 367–378. https://doi.org/10.1007/s12549-018-0359-7 (2019).Article 

Google Scholar 
46.Makarkin, V. N. & Menon, F. New species of the Mesochrysopidae (Insecta, Neuroptera) from the Crato Formation of Brazil (Lower Cretaceous), with taxonomic treatment of the family. Cretac. Res. 26, 801–812. https://doi.org/10.1016/j.cretres.2005.05.009 (2005).Article 

Google Scholar 
47.Martill, D. M., Loveridge, R. & Heimhofer, U. Halite pseudomorphs in the Crato Formation (Early Cretaceous, Late Aptian-Early Albian), Araripe Basin, northeast Brazil: Further evidence for hypersalinity. Cretac. Res. 28(4), 613–620. https://doi.org/10.1016/j.cretres.2006.10.003 (2007).Article 

Google Scholar 
48.Williams, W. D. Salinisation: A major threat to water resources in the arid and semi-arid regions of the world. Lakes and Reservoirs. Res. Manag. 4, 85–91. https://doi.org/10.1046/j.1440-1770.1999.00089.x (1999).Article 

Google Scholar 
49.Clarke, R. T. & Hering, D. Errors and uncertainty in bioassessment methods, major results and conclusions from the STAR project and their application using STARBUGS. Hydrobiologia 566, 433–439. https://doi.org/10.1007/s10750-006-0079-2 (2006).Article 

Google Scholar 
50.Williams, W. D. Salinity tolerances of four species of fish from the Murray-Darling River system. Hydrobiologia 210, 145–160 (1991).Article 

Google Scholar 
51.Lancaster, J. & Scudder, G. G. E. Aquatic Coleoptera and Hemiptera in some Canadian saline lakes: Patterns in community structure. Can. J. Zool. 65(6), 1383–1390. https://doi.org/10.1139/z87-218 (1987).Article 

Google Scholar 
52.Metzeling, L. Benthic macroinvertebrate community structure in streams of different salinities. Mar. Freshw. Res. 44, 335–351. https://doi.org/10.1071/MF9930335 (1993).CAS 
Article 

Google Scholar 
53.Berezina, N. A. Tolerance of freshwater invertebrates to changes in water salinity. Russ. J. Ecol. 34(4), 261–266. https://doi.org/10.1023/A:1024597832095 (2003).Article 

Google Scholar 
54.Kefford, B. J., Dalton, A., Palmer, C. G. & Nugegoda, D. The salinity tolerance of eggs and hatchlings of selected aquatic macroinvertebrates in south-east Australia and South Africa. Hydrobiologia 517(1–3), 179–192. https://doi.org/10.1023/B:HYDR.0000027346.06304.bc (2004).Article 

Google Scholar 
55.Chadwick, M. A., Hunter, H., Feminella, J. W. & Henry, R. P. Salt and water balance in Hexagenia limbata (Ephemeroptera: Ephemeridae) when exposed to brackish water. Fla. Entomol. 85, 650–651. https://doi.org/10.1653/0015-4040(2002)085[0650:SAWBIH]2.0.CO;2 (2002).Article 

Google Scholar 
56.James, K. R., Cant, B. & Ryan, T. Responses of freshwater biota to rising salinity levels and implications for saline water management: A review. Aust. J. Bot. 51(6), 703. https://doi.org/10.1071/BT02110 (2003).CAS 
Article 

Google Scholar 
57.Nielsen, D. L., Brock, M. A., Rees, G. N. & Baldwin, D. S. Effects of increasing salinity on freshwater ecosystems in Australia. Aust. J. Bot. 51(6), 655–665. https://doi.org/10.1071/BT02115 (2003).Article 

Google Scholar 
58.Hart, B. T., Lake, P. S., Webb, J. A. & Grace, M. R. Ecological risk to aquatic systems from salinity increases. Aust. J. Bot. 51(6), 689. https://doi.org/10.1071/BT02111 (2003).CAS 
Article 

Google Scholar 
59.Bagarinao, T. Systematics, genetics and life history of milkfish, Chanos chanos. Environ. Biol. Fishes. 39, 23–41 (1994).Article 

Google Scholar 
60.Davis, S. P. & Martill, D. M. The Gonorynchiform fish Dastilbe from the Lower Cretaceous of Brazil. Palaeontology 42(4), 715–740 (2003).Article 

Google Scholar 
61.Jell, P. A. & Duncan, P. M. Invertebrates, mainly insects, from the freshwater, Lower Cretaceous, Koonwarra fossil bed (Korumburra group), South Gippsland, Victoria. In Plants and invertebrates from the Lower Cretaceous Koonwarra fossil bed, South Gippsland, Victoria (eds Jell, P. A. & Roberts, J.) 111–205 (Memoir of the Association of Australasian Palaeontologists, 1986).
Google Scholar 
62.Ponomarenko, A. G. Fossil insects from the Tithonian ‘Solnhofener Plattenkalke’ in the Museum of Natural History, Vienna. Ann. Naturhist. Mus. Wien. 87, 135–144 (1985).
Google Scholar 
63.Zhang, J. & Zhang, H. Insects and spiders. In The Jehol Biota (eds Chang, M. et al.) 59–68 (Shanghai Scientific and Technical Publishers, 2003).
Google Scholar 
64.Hellawell, J. & Orr, P. J. Deciphering taphonomic processes in the Eocene Green River Formation of Wyoming. Palaeobiodivers. Palaeoenviron. 93, 353–365. https://doi.org/10.1007/s12549-012-0092-6 (2012).Article 

Google Scholar 
65.McGrew, P. O. Taphonomy of Eocene fish from Fossil Basin, Wyoming. Fieldiana Geology. 33, 257–270 (1975).
Google Scholar 
66.Krzemiński, W., Soszyńska-Maj, A., Bashkuev, A. S. & Kopeć, K. Revision of the unique Early Cretaceous Mecoptera from Koonwarra (Australia) with description of a new genus and family. Cretac. Res. 52, 501–506. https://doi.org/10.1016/j.cretres.2014.04.004 (2015).Article 

Google Scholar 
67.Elder, R. L. & Smith, G. R. Fish taphonomy and environmental inference in Paleolimnology. Palaeogeogr. Palaeoclimatol. Palaeoecol. 62, 577–592 (1988).Article 

Google Scholar 
68.Huang, D. Tarwinia australis (Siponaptera: Tarwiniidae) from the Lower Cretaceous Koonwarra fossil bed: Morphological revision and analysis of its evolutionary relationship. Cretac. Res. 52, 507–515 (2015).Article 

Google Scholar 
69.Waldman, M. Fish from the freshwater Lower Cretaceous of Victoria, Australia with comments of the palaeo-environment. Spec. Pap. Palaeontol. 9, 1–124 (1971).
Google Scholar 
70.Brittain, J. E. & Sartori, M. Ephemeroptera. In Encyclopedia of Insects (eds Resh, V. H. & Cardé, R. T.) 328–334 (Academic Press, 2002).
Google Scholar 
71.Bartell, K. W., Swinburne, N. H. M. & Conway-Morris, S. Solnhofen: A Study in Mesozoic Palaeontology (Cambridge University Press, 1990).
Google Scholar 
72.Bechly, G. New fossil dragonflies from the Lower Cretaceous Crato Formation of north-east Brazil (Insecta: Odonata). Stuttgarter Beitrage zur Naturkunde. 264, 1–66 (1998).
Google Scholar 
73.Fielding, S., Martill, D. M. & Naish, D. Solnhofen-style soft-tissue preservation in a new species of turtle from the Crato Formation (Early Cretaceous, Aptian) of north-east Brazil. Palaeontology 48, 1301–1310. https://doi.org/10.1111/j.1475-4983.2005.00508.x (2005).Article 

Google Scholar 
74.Sartori, M. & Brittain, J. E. Order Ephemeroptera. In Ecology and General Biology: Thorp and Covich’s Freshwater Invertebrates (eds Thorp, J. & Rogers, D. C.) 873–891 (Academic Press, 2015).
Google Scholar 
75.Chang, M. M., Chen, P. J., Wang, Y. Q., Wang, Y. & Miao, D. S. The Jehol Fossils: TheEmergence of Feathered Dinosaurs, Beaked Birds and Flowering Plants (Academic Press, 2007).
Google Scholar 
76.Zhang, X. & Sha, J. Sedimentary laminations in the lacustrine Jianshangou Bed of the Yixian Formation at Sihetun, western Liaoning, China. Cretac. Res. 36, 96–105. https://doi.org/10.1016/j.cretres.2012.02.010 (2012).CAS 
Article 

Google Scholar 
77.Fürsich, F. T., Sha, J., Jiang, B. & Pan, Y. High resolution palaeoecological and taphonomic analysis of Early Cretaceous lake biota, western Liaoning (NE-China). Palaeogeogr. Palaeoclimatol. Palaeoecol. 253, 434–457. https://doi.org/10.1016/j.palaeo.2007.06.012 (2007).Article 

Google Scholar 
78.Pan, Y., Sha, J. & Fürsich, F. A model for organic fossilization of the Early Cretaceous Jehol Lagerstätte based on the taphonomy of “Ephemeropsis trisetalis”. Palaios 29(7/8), 363–377 (2014).ADS 
Article 

Google Scholar 
79.Upchurch, G. R. & Doyle, J. A. Paleoecology of the conifers Frenelopsis and Pseudofrenelopsis (Cheirolepidiaceae) from the Cretaceous Potomac Group of Maryland and Virginia. In Geobotany II (ed. Romans, R. C.) 167–202 (Plenum, 1981).
Google Scholar 
80.Maisey, J. G. A new Clupeomorph fish from the Santana Formation (Albian) of NE Brazil. Am. Mus. Novit. 3076, 1–15 (1993).
Google Scholar 
81.Valença, M. M., Neumann, V. H. & Mabesoone, J. M. An overview on Callovian-Cenomanian intracratonic basins of northeast Brazil: Onshore stratigraphic record of the opening of the southern Atlantic. Geol. Acta. 1, 261–275. https://doi.org/10.1344/105.000001614 (2003).Article 

Google Scholar 
82.Barling, N., Martill, D. M., Heads, S. W. & Gallien, F. High fidelity preservation of fossil insects from the Crato Formation (Lower Cretaceous) of Brazil. Cretac. Res. 52(B), 605–622. https://doi.org/10.1016/j.cretres.2014.05.007 (2015).Article 

Google Scholar 
83.Catto, B., Jahnert, R. J., Warren, L. V., Varejão, F. G. & Assine, M. L. The microbial nature of laminated limestones: lessons from the Upper Aptian, Araripe Basin, Brazil. Sediment. Geol. 341, 304–315. https://doi.org/10.1016/j.sedgeo.2016.05.007 (2016).ADS 
CAS 
Article 

Google Scholar 
84.Warren, L. V. et al. Stromatolites from the Aptian Crato Formation, a hypersaline lake system in the Araripe Basin, northeastern Brazil. Facies 63(3), 2016. https://doi.org/10.1007/s10347-016-0484-6 (2017).Article 

Google Scholar  More

1.Azam, F. et al. The ecological role of water-column microbes in the sea. Mar. Ecol. Prog. Ser. 10, 257–263 (1983).ADS 
Article 

Google Scholar 
2.Seymour, J. R., Amin, S. A., Raina, J.-B. & Stocker, R. Zooming in on the phycosphere: the ecological interface for phytoplankton–bacteria relationships. Nat. Microbiol. 2, 17065 (2017).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
3.Worden, A. Z. et al. Rethinking the marine carbon cycle: factoring in the multifarious lifestyles of microbes. Science 347, 1257594 (2015).PubMed 
Article 
CAS 
PubMed Central 

Google Scholar 
4.Andersson, A. F., Riemann, L. & Bertilsson, S. Pyrosequencing reveals contrasting seasonal dynamics of taxa within Baltic Sea bacterioplankton communities. ISME J. 4, 171–181 (2010).PubMed 
Article 
PubMed Central 

Google Scholar 
5.Chen, T., Liu, Y., Song, S. & Li, C. Characterization of the parasitic dinoflagellate Amoebophrya sp. infecting akashiwo sanguinea in coastal waters of China. J. Eukaryotic Microbiol. 65, 448–457 (2018).Article 

Google Scholar 
6.Azam, F. & Malfatti, F. Microbial structuring of marine ecosystems. Nat. Rev. Microbiol. 5, 782–791 (2007).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
7.Yang, C. et al. Bacterial community dynamics during a bloom caused by Akashiwo sanguinea in the Xiamen sea area, China. Harmful algae 20, 132–141 (2012).Article 

Google Scholar 
8.Yang, C. et al. A comprehensive insight into functional profiles of free-living microbial community responses to a toxic Akashiwo sanguinea bloom. Sci. Rep. 6, 34645 (2016).ADS 
CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
9.Kang, et al. Zooming on dynamics of marine microbial communities in the phycosphere of Akashiwo sanguinea (Dinophyta) blooms. Mol. Ecol. 30, 207–221 (2021).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
10.Kim, H. J. et al. Effects of temperature and nutrients on changes in genetic diversity of bacterioplankton communities in a semi-closed bay, South Korea. Mar. Pollut. Bull. 106, 139–148 (2016).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
11.Flaviani, F. et al. A pelagic microbiome (viruses to protists) from a small cup of seawater. Viruses 9, 47 (2017).PubMed Central 
Article 
CAS 

Google Scholar 
12.Jung, S. W. et al. Can the algicidal material Ca-aminoclay be harmful when applied to a natural ecosystem? An assessment using microcosms. J. Hazard. Mater. 298, 178–187 (2015).ADS 
CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
13.Jung, S. W., Noh, S. Y., Kang, D. & Lee, T.-K. Comparison of bacterioplankton communities between before and after inoculation with an algicidal material, Ca-aminoclay, to mitigate Cochlodinium polykrikoides blooms: assessment using microcosm experiments. J. Appl. Phycol. 29, 1343–1354 (2017).CAS 
Article 

Google Scholar 
14.Jung, S. W., Kim, B. H., Katano, T., Kong, D. S. & Han, M. S. Pseudomonas fluorescens HYK0210-SK09 offers species-specific biological control of winter algal blooms caused by freshwater diatom Stephanodiscus hantzschii. J. Appl. Microbiol. 105, 186–195 (2008).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
15.Jung, S. W. et al. Testing addition of Pseudomonas fluorescens HYK0210-SK09 to mitigate blooms of the diatom Stephanodiscus hantzschii in small- and large-scale mesocosms. J. Appl. Phycol. 22, 409–419 (2010).Article 

Google Scholar 
16.Anderson, D. M. Turning back the harmful red tide. Nature 388, 513–514 (1997).ADS 
CAS 
Article 

Google Scholar 
17.Du, X., Peterson, W., McCulloch, A. & Liu, G. An unusual bloom of the dinoflagellate Akashiwo sanguinea off the central Oregon, USA, coast in autumn 2009. Harmful Algae 10, 784–793 (2011).Article 

Google Scholar 
18.Lee, C.-K., Lee, O.-H. & Lee, S.-G. Impacts of temperature, salinity and irradiance on the growth of ten harmful algal bloom-forming microalgae isolated in Korean coastal waters. The Sea (J Korean Soc Oceanogr) 10, 79–91 (2005).
Google Scholar 
19.Luo, Z. et al. Cryptic diversity within the harmful dinoflagellate Akashiwo sanguinea in coastal Chinese waters is related to differentiated ecological niches. Harmful Algae 66, 88–96 (2017).PubMed 
Article 
PubMed Central 

Google Scholar 
20.Chen, T. et al. The effects of major environmental factors and nutrient limitation on growth and encystment of planktonic dinoflagellate Akashiwo sanguinea. Harmful Algae 46, 62–70 (2015).Article 

Google Scholar 
21.Matsubara, T. et al. Effects of temperature, salinity, and irradiance on the growth of the dinoflagellate Akashiwo sanguinea. J. Exp. Mar. Biol. Ecol. 342, 226–230 (2007).Article 

Google Scholar 
22.Teeling, H. et al. Substrate-controlled succession of marine bacterioplankton populations induced by a phytoplankton bloom. Science 336, 608–611 (2012).ADS 
CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
23.Buchan, A., LeCleir, G. R., Gulvik, C. A. & González, J. M. Master recyclers: features and functions of bacteria associated with phytoplankton blooms. Nat. Rev. Microbiol. 12, 686–698 (2014).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
24.Riemann, L., Steward, G. F. & Azam, F. Dynamics of bacterial community composition and activity during a mesocosm diatom bloom. Appl. Environ. Microbiol. 66, 578–587 (2000).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
25.Jones, K. L., Mikulski, C. M., Barnhorst, A. & Doucette, G. J. Comparative analysis of bacterioplankton assemblages from Karenia brevis bloom and nonbloom water on the west Florida shelf (Gulf of Mexico, USA) using 16S rRNA gene clone libraries. FEMS Microbiol. Ecol. 73, 468–485 (2010).CAS 
PubMed 
PubMed Central 

Google Scholar 
26.Theroux, S., Huang, Y. & Amaral-Zettler, L. Comparative molecular microbial ecology of the spring haptophyte bloom in a Greenland arctic oligosaline lake. Front. Microbiol. 3, 415 (2012).PubMed 
PubMed Central 
Article 

Google Scholar 
27.Amin, S. et al. Interaction and signalling between a cosmopolitan phytoplankton and associated bacteria. Nature 522, 98–101 (2015).ADS 
CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
28.Mayali, X. & Azam, F. Algicidal bacteria in the sea and their impact on algal blooms. J. Eukaryot. Microbiol. 51, 139–144 (2004).PubMed 
Article 
PubMed Central 

Google Scholar 
29.Tan, S. et al. An association network analysis among microeukaryotes and bacterioplankton reveals algal bloom dynamics. J. Phycol. 51, 120–132 (2015).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
30.Cruz-López, R., Maske, H., Yarimizu, K. & Holland, N. A. The B-vitamin mutualism between the dinoflagellate Lingulodinium polyedrum and the bacterium Dinoroseobacter shibae. Front. Mar. Sci. 5, 274 (2018).Article 

Google Scholar 
31.Park, B. S., Joo, J.-H., Baek, K.-D. & Han, M.-S. A mutualistic interaction between the bacterium Pseudomonas asplenii and the harmful algal species Chattonella marina (Raphidophyceae). Harmful Algae 56, 29–36 (2016).PubMed 
Article 
PubMed Central 

Google Scholar 
32.Needham, D. M., Sachdeva, R. & Fuhrman, J. A. Ecological dynamics and co-occurrence among marine phytoplankton, bacteria and myoviruses shows microdiversity matters. ISME J. 11, 1614–1629 (2017).PubMed 
PubMed Central 
Article 

Google Scholar 
33.Bloem, J., Starink, M., Bär-Gilissen, M.-J.B. & Cappenberg, T. E. Protozoan grazing, bacterial activity, and mineralization in two-stage continuous cultures. Appl. Environ. Microbiol. 54, 3113–3121 (1988).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
34.Gao, X., Olapade, O. A. & Leff, L. G. Comparison of benthic bacterial community composition in nine streams. Aquat. Microb. Ecol. 40, 51–60 (2005).Article 

Google Scholar 
35.González, J. M., Kiene, R. P. & Moran, M. A. Transformation of sulfur compounds by an abundant lineage of marine bacteria in the α-subclass of the class Proteobacteria. Appl. Environ. Microbiol. 65, 3810–3819 (1999).PubMed 
PubMed Central 
Article 

Google Scholar 
36.Cui, Y. et al. The water depth-dependent co-occurrence patterns of marine bacteria in shallow and dynamic Southern Coast, Korea. Sci. Rep. 9, 9176 (2019).ADS 
PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
37.Huang, X. et al. Profiles of quorum sensing (QS)-related sequences in phycospheric microorganisms during a marine dinoflagellate bloom, as determined by a metagenomic approach. Microbiol. Res. 217, 1–13 (2018).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
38.Orsi, W. D. et al. Ecophysiology of uncultivated marine euryarchaea is linked to particulate organic matter. ISME J. 9, 1747–1763 (2015).PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
39.Salter, I. et al. Seasonal dynamics of active SAR11 ecotypes in the oligotrophic Northwest Mediterranean Sea. ISME J. 9, 347–360 (2015).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
40.Berdjeb, L., Parada, A., Needham, D. M. & Fuhrman, J. A. Short-term dynamics and interactions of marine protist communities during the spring–summer transition. ISME J. 12, 1907–1917 (2018).PubMed 
PubMed Central 
Article 

Google Scholar 
41.Chow, C.-E.T., Kim, D. Y., Sachdeva, R., Caron, D. A. & Fuhrman, J. A. Top-down controls on bacterial community structure: microbial network analysis of bacteria, T4-like viruses and protists. ISME J. 8, 816–829 (2014).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
42.Louca, S., Parfrey, L. W. & Doebeli, M. Decoupling function and taxonomy in the globalocean microbiome. Science 353, 1272–1277 (2016).ADS 
CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
43.Rivett, D. W. & Bell, T. Abundance determines the functional role of bacterial phylotypes in complex communities. Nat. Microbiol. 3, 767–772 (2018).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
44.Amblard, C., Rachiq, S. & Bourdier, G. Photolithotrophy, photoheterotrophy and chemoheterotrophy during spring phytoplankton development (Lake Pavin). Microb. Ecol. 24, 109–123 (1992).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
45.Chun, S.-J. et al. Characterization of distinct cyanoHABs-related modules in microbial recurrent association network. Front. Microbiol. 10, 1637 (2019).PubMed 
PubMed Central 
Article 

Google Scholar 
46.He, T., Xie, D., Ni, J., Li, Z. & Li, Z. Nitrous oxide produced directly from ammonium, nitrate and nitrite during nitrification and denitrification. J. Hazard. Mater. 388, 122114 (2020).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
47.Leahy, J. G. & Colwell, R. R. Microbial degradation of hydrocarbons in the environment. Microbiol. Rev. 54, 305–315 (1990).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
48.Porter, K. G. & Feig, Y. S. The use of DAPI for identifying and counting aquatic microflora 1. Limnol. Oceanogr. 25, 943–948 (1980).ADS 
Article 

Google Scholar 
49.Andrew, S. A quality control tool for high throughput sequence data. http://www.bioinformatics.babraham.ac.uk/projects/fastqc/. (2010)50.Magoč, T. & Salzberg, S. L. FLASH: fast length adjustment of short reads to improve genome assemblies. Bioinformatics 27, 2957–2963 (2011).PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
51.Altschul, S. F., Gish, W., Miller, W., Myers, E. W. & Lipman, D. J. Basic local alignment search tool. J. Mol. Biol. 215, 403–410 (1990).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
52.Li, R. W. et al. Characterization of the rumen microbiota of pre-ruminant calves using metagenomic tools. Environ. Microbiol. 14, 129–139 (2012).PubMed 
Article 
CAS 
PubMed Central 

Google Scholar 
53.Schloss, P. D. et al. Introducing mothur: open-source, platform-independent, community-supported software for describing and comparing microbial communities. Appl. Environ. Microbiol. 75, 7537–7541 (2009).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
54.Oksanen, J. et al. Package ‘vegan’. Community ecology package. https://github.com/vegandevs/vegan (2019).55.Paradis, E. et al. Package ‘ape’. Analyses of phylogenetics and evolution. http://ape-package.ird.fr/. (2019).56.Wickham, H. et al. ggplot2: Create elegant data visualisations using the grammar of graphics. https://github.com/tidyverse/ggplot2 (2020).57.Walker, I. R., Levesque, A. J., Cwynar, L. C. & Lotter, A. F. An expanded surface-water palaeotemperature inference model for use with fossil midges from eastern Canada. J. Paleolimnol. 18, 165–178 (1997).ADS 
Article 

Google Scholar 
58.Clarke, K. R. Non-parametric multivariate analyses of changes in community structure. Aust. J. Ecol. 18, 117–143 (1993).Article 

Google Scholar 
59.Ter Braak, C. J. Canonical correspondence analysis: a new eigenvector technique for multivariate direct gradient analysis. Ecology 67, 1167–1179 (1986).Article 

Google Scholar 
60.Ter Braak, C.J.F. & Šmilauer, P. CANOCO Reference Manual and CanoDraw for Windows User’s Guide: Software for Canonical Community Ordination, version 4.5. Ithaca, NY, USA: Microcomputer Power. (2002).61.Xia, L. C. et al. Extended local similarity analysis (eLSA) of microbial community and other time series data with replicates. BMC Syst. Biol. 5, S15 (2011).PubMed 
PubMed Central 
Article 

Google Scholar 
62.Xia, L. C., Ai, D., Cram, J., Fuhrman, J. A. & Sun, F. Efficient statistical significance approximation for local similarity analysis of high-throughput time series data. Bioinformatics 29, 230–237 (2013).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
63.Shannon, P. et al. Cytoscape: a software environment for integrated models of biomolecular interaction networks. Genome Res. 13, 2498–2504 (2003).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar  More

National breeding bird monitoring schemesFieldworkers record all, or a fixed, pre-defined set, of bird species heard or seen in the main breeding season in 28 European countries on an annual basis (Fig. 1). All observations are recorded following a scheme-specific standardized protocol based on established field methods for counting birds: point count transect, line transect, or territory or spot mapping10,33,34. Here, we provide a short description of the field methods used, as each scheme provides its fieldworkers with specific fieldwork instructions and training.

1.

Point counts: A fieldworker counts all detected birds at census points, often placed along a transect (typically >200 meters apart) during a fixed time period to sample birds in a defined study area. Each point is usually visited twice a year.

2.

Line transects: A fieldworker moves along a transect and records all detected birds along the predefined path to sample birds in a defined study area. Each transect is usually visited twice a year.

3.

Territory or spot mapping: A fieldworker records all birds showing territorial behaviour in a defined study area and marks their positions and their territorial behaviour on a map. The study area is visited multiple times a year (usually 5–12) to map breeding bird territories based on the individual species-specific behaviour recorded. The species counts reflect the number of present territories.

National scheme coordinators provide all fieldworkers with instructions with the prescribed number and timing of survey visits, and information on how to record observations in terms of sampling effort, time of day, seasonality and weather conditions. This ensures the temporal and spatial consistency of data quality within individual national schemes35. The standardization of conditions during counting then enables unbiased comparison of results between years and individual study sites within each country.For the selection of sampling plots, national monitoring schemes use either random, stratified random, systematic selection, or allow a free choice by fieldworkers8,34. Sampling plots are selected randomly within the study boundaries using a random selection method or randomly within the stratum under the stratified random method. Under these methods, study plot selection is conducted by random generators (by computer programs) and stratum is predefined as a region with similar attributes; these might be proportions of habitat types, altitude bands, bird abundance, accessibility of survey sites, or fieldworker density, depending on the local circumstances. Systematic selection predefines a spatial grid for sampling plot selection while free choice enables fieldworkers to select their study areas without restrictions34. The use of a free choice, or stratified random selection of sampling plots may result in a biased sampling of specific habitat types (typically species-rich habitats) and regions (remote areas poorly covered), but post-hoc stratification and weighting procedures are generally used to correct for unequal sampling and reduce sampling bias as long as the number of plots per stratum is sufficient36. Moreover, national coordinators provide fieldworkers with recommendations or oversee the study plot selection to prevent oversampling of specific habitat types and regions. Detailed information on scheme-specific counting protocols, study plot selection and breeding period specification can be found for each national monitoring scheme8.National species indicesA species annual index reflects population size change relative to the population size in the reference year. On an annual basis, coordinators of the national monitoring schemes produce species indices for recorded species using a tailor-made implementation of loglinear regression models (TRIM models – Trends and Indices for Monitoring data) from time series of recorded species counts at the study plots37,38. Species counts from a study plot reflect mean (or maximum) of individuals recorded during visits at the study plot when using point counts or line transects. For some species, only the number of individuals recorded on the second visit is used because the period of the first visit coincides with the migratory period and consequently the mean number of recorded individuals might not reflect the number of breeding individuals. The method to estimate the species counts in a plot is constant within a national scheme.Missing data occur in the species counts at specific sites in individual years for various reasons, such as severe weather conditions during the counting period, abandonment of the study site, restricted access, or where counts are repeated in multi-year intervals. The TRIM model imputes missing data using species counts either from surveyed sites with similar environmental characteristics (stratified imputing) or all other sites with available data37,39. This process is based on the assumption that changes in populations at non-counted sites are similar to those at counted sites within the same stratum. To derive expected between-year changes in species population sizes, the program fits a log-linear regression model assuming Poisson distribution to time series from counted plots. Finally, we use this model to calculate missing species-specific counts for individual years37,39. The resulting time series of species counts with imputed missing values cover the whole period of counts in the national monitoring scheme. These imputed data are then used to estimate annual population sizes from all study plots and to derive population size indices for species11.European species indices and trendsThe individual national indices for a given species are combined to create the European species indices. Subsequently, long-term population size changes (trends) are calculated as the multiplicative linear slopes from species indices and represent an average between-year relative population size change over a predefined period.The European combination process is very similar to the production of national scheme species indices, but with three differences40. Firstly, the indices are calculated using national TRIM output data, consisting of imputed species counts, standard errors per year and covariance matrices. Secondly, species counts are weighted by the most recent species population size estimates (updated every three years) in a given country derived from national bird atlases, official data reports and national experts (http://datazone.birdlife.org/) to account for the country-specific population sizes and thus the unequal contribution of national indices on the European index. Thirdly, missing national time totals due to different start years of the schemes8 are imputed using species counts from a set of countries from the same geographical region6,11. For this purpose, we divided all national schemes into seven geographic regions – Central & East Europe, East Mediterranean, North Europe, South Europe, Southeast Europe, West Balkan and West Europe8. We then use a set of national indices from a given region to impute missing national indices. Therefore, the earliest periods of population size changes are based on data from a reduced number of study plots and schemes.The species trends are then imputed from species indices for four periods: 1980 onwards, 1990 onwards, 2000 onwards and using only the last ten years of data if the data are available. Despite higher uncertainty of the earliest estimates, we do provide the population index estimates for this period as no alternative and continuous measures of bird population size changes exist for this period.The uncertainty estimates of indices and trends are presented by the standard error11,37 allowing a calculation of 95% confidence limits (±1.96 × standard error). The magnitude of the trend estimates together with their 95% confidence intervals are then used for trend classification into six classes facilitating communication and interpretation of the outputs37 (Table 1).Table 1 Classification of the European bird species trends based on the magnitude and uncertainty of the estimates (using 95% confidence intervals).Full size tableFinally, European species indices and trends are presented only for a group of common and widespread bird species (hereafter ‘common bird species’) meeting two criteria:

1.

The estimated breeding population (http://datazone.birdlife.org/) is at least 50 000 pairs in PECBMS Europe (EU countries, Norway, Switzerland and the United Kingdom; Fig. 1). Additionally, Red-billed Chough (Pyrrhocorax pyrrhocorax) and Spotted Redshank (Tringa erythropus) with population sizes below 50 000 pairs are included, as large parts of their breeding populations are covered in the PECBMS Europe.

2.

The estimated breeding population in PECBMS countries providing data for a given species8 covers at least 50% of the whole PECBMS Europe breeding population (http://datazone.birdlife.org/).

The resulting datasets of European population size indices and trends consist of relative population changes for 170 common bird species.UpdatesWe aim to maintain the PECBMS database with annual updates. The annual updates will be available through the PECBMS database deposited at the Zenodo repository8 to ensure long-term public availability of the data. More

1.Kroeze, C., Dumont, E. & Seitzinger, S. P. New estimates of global emissions of N2O from rivers and estuaries. Environ. Sci. 2(2–3), 159–165. https://doi.org/10.1080/15693430500384671 (2005).Article 

Google Scholar 
2.Ciais, P. et al. Carbon and other biogeochemical cycles. In Climate Change 2013: The Physical Science Basis. Contribution of Working Group I to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change 465–570 (Cambridge University Press, 2014).3.Forster, P. et al. Changes in atmospheric constituents and in radiative forcing. Chapter 2. In Climate Change 2007. The Physical Science Basis (2007).4.Butterbach-Bahl, K., Baggs, E. M., Dannenmann, M., Kiese, R. & Zechmeister-Boltenstern, S. Nitrous oxide emissions from soils: How well do we understand the processes and their controls?. Philos. Trans. R. Soc. B. https://doi.org/10.1098/rstb.2013.0122 (2013).Article 

Google Scholar 
5.Reis, C. R. G., Nardoto, G. B. & Oliveira, R. S. Global overview on nitrogen dynamics in mangroves and consequences of increasing nitrogen availability for these systems. Plant Soil. 410(1–2), 1–19. https://doi.org/10.1007/s11104-016-3123-7#citeas (2017).CAS 
Article 

Google Scholar 
6.Rao, K., Priya, N. & Ramanathan, A. L. Impacts of anthropogenic perturbations on reactive nitrogen dynamics in mangrove ecosystem: Climate change perspective. J. Clim. Change 5(2), 9–21 (2019).Article 

Google Scholar 
7.Centre for Coastal Zone Management and Coastal Shelter Belt, Ministry of Environment, Forests and Climate change, Govt. of India http://iomenvis.nic.in/index2.aspx?slid=758&sublinkid=119&langid=1&mid=1 (2017).8.FSI. India State of Forest Report. 2019. Forest Survey of India, Ministry of Environment and Forests, Dehradun (2019).9.Borges, A. V. et al. Effects of agricultural land use on fluvial carbon dioxide, methane and nitrous oxide concentrations in a large European river, the Meuse (Belgium). Sci. Total Environ. 610, 342–355. https://doi.org/10.1016/j.scitotenv.2017.08.047 (2018).ADS 
CAS 
Article 
PubMed 

Google Scholar 
10.Lin, H. et al. Spatiotemporal variability of nitrous oxide in a large eutrophic estuarine system: The Pearl River Estuary, China. Mar. Chem. 182, 14–24. https://doi.org/10.1016/j.marchem.2016.03.005 (2016).CAS 
Article 

Google Scholar 
11.Reading, M. J. et al. Land use drives nitrous oxide dynamics in estuaries on regional and global scales. Limnol. 65(8), 1903–1920. https://doi.org/10.1002/lno.11426 (2020).CAS 
Article 

Google Scholar 
12.Chauhan, R., Ramanathan, A. L. & Adhya, T. K. Assessment of methane and nitrous oxide flux from mangrove along Eastern coast of India. Geofluids 8, 321332. https://doi.org/10.1111/j.1468-8123.2008.00227.x (2008).CAS 
Article 

Google Scholar 
13.Krithika, K., Purvaja, R. & Ramesh, R. Fluxes of methane and nitrous oxide from an Indian mangrove. Curr. Sci. 94, 218224, https://www.jstor.org/stable/24101861 (2008).14.Fernandes, S. O., LokaBharathi, P. A., Bonin, P. C. & Michotey, V. D. Denitrification: An important pathway for nitrous oxide production in tropical mangrove sediments (Goa, India). J. Environ. Qual. 39, 1507–1516. https://doi.org/10.2134/jeq2009.0477 (2010).CAS 
Article 
PubMed 

Google Scholar 
15.Wanninkhof, R. Relationship between wind speed and gas exchange over the ocean. J. Geophys Res. 97, 7373–7382. https://doi.org/10.4319/lom.2014.12.351 (1992).ADS 
Article 

Google Scholar 
16.Wanninkhof, R. & McGillis, W. M. A cubic relationship between gas transfer and wind speed. Geophys. Res. Lett. 26, 1889–1893. https://doi.org/10.1029/1999GL900363 (1999).ADS 
CAS 
Article 

Google Scholar 
17.Raymond, P. A. & Cole, J. J. Gas exchange in rivers and estuaries: Choosing a gas transfer velocity. Estuaries 24, 312–317. https://doi.org/10.2307/1352954 (2001).CAS 
Article 

Google Scholar 
18.Hershey, R. N., Nandan, S. B. & Vasu, N. K. Trophic status and nutrient regime of Cochin estuarine system, India. Indian J. Mar. Sci. 49(08), 2582–6727 http://nopr.niscair.res.in/handle/123456789/55309 (2020).19.Hershey, R. N. et al. Nitrous oxide flux from a Tropical estuarine system (Cochin estuary, India). Reg. Stud. Mar. Sci. 30, 100725. https://doi.org/10.1016/j.rsma.2019.100725 (2019).Article 

Google Scholar 
20.Maher, D. T., Sippo, J. Z., Tait, D. R., Holloway, C. & Santos, I. R. Pristine mangrove creek waters are a sink of nitrous oxide. Sci. Rep. 6, 25701. https://doi.org/10.1038/srep25701 (2016).ADS 
CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
21.Tait, D. R. et al. Greenhouse gas dynamics in a salt-wedge estuary revealed by high resolution cavity ring-down spectroscopy observations. Environ. Sci. Technol. 51(23), 13771–13778. https://doi.org/10.1021/acs.est.7b04627 (2017).ADS 
CAS 
Article 
PubMed 

Google Scholar 
22.Wells, N. S. et al. Estuaries as sources and sinks of N2O across a land use gradient in subtropical Australia. Glob. Biogeochem. Cycles. 32, 877–894. https://doi.org/10.1029/2017GB005826 (2018).ADS 
CAS 
Article 

Google Scholar 
23.Upstill-Goddard, R. C. Air–sea gas exchange in the coastal zone. Estuar Coast Shelf Sci. 70, 388–404. https://doi.org/10.1016/j.ecss.2006.05.043 (2006).ADS 
CAS 
Article 

Google Scholar 
24.Zappa, C. J., Raymond, P. A., Terray, E. A. & Mcgillis, W. R. Variation in surface turbulence and gas transfer velocity over a tidal cycle in a macro-tidal estuary. Estuaries 26, 1401–1415. https://doi.org/10.1007/BF02803649/citeas (2003).CAS 
Article 

Google Scholar 
25.Borges, A. V. et al. Gas transfer velocities of CO2 in three European estuaries (Randers Fjord, Scheldt, and Thames). Limnol. Oceanogr. 49, 1630–1641. https://doi.org/10.4319/lo.2004.49.5.1630 (2004).ADS 
CAS 
Article 

Google Scholar 
26.Munoz-Hincapie, M., Morell, J. M. & Corredor, J. E. Increase of nitrous oxide flux to the atmosphere upon nitrogen addition to red mangroves sediments. Mar. Pollut. Bull. 44, 992–996. https://doi.org/10.1016/S0025-326X(02)00132-7 (2002).CAS 
Article 
PubMed 

Google Scholar 
27.Srinivas, K., Revichandran, P., Maheswaran, P. A., Mohammed Ashraf, T. T. & Nuncio, M. Propagation of tides in the Cochin estuarine system, southwest coast of India. Indian J. Geomar. Sci. 32(1), 14–24 (2003).
Google Scholar 
28.Srinivas, K., Revichandran, C. & Dinesh Kumar, P. K. Statistical forecasting of met-ocean parameters in the Cochin estuarine system, southwest coast of India. Indian J. Geomar. Sci. 32(4), 285–293 (2003).
Google Scholar 
29.Balachandran, K. K., Joseph, T., Nair, K. K. C., Nair, M. & Joseph, P. S. The complex estuarine formation of six rivers (Cochin backwaters system on westcoast of India)—Sources and distribution of trace metals and nutrients. In:APN/SASCOM/LOICZ Regional Workshop on Assessment of Material Fluxes To the Coastal Zone in South Asia and their Impacts. Sri Lanka National Committee of IGBP, Colombo, Sri Lanka, 359, http://drs.nio.org/drs/handle/2264/1340 (2002).30.Martin, G. D. et al. Freshwater influence on nutrient stoichiometry in a tropical estuary, southwest coast of India. Appl. Ecol. Environ. Res. 6, 57–64 (2008).Article 

Google Scholar 
31.Liu, D. et al. N2O fluxes and rates of nitrification and denitrification at the sediment-water interface in Taihu Lake, China. Water 10, 911. https://doi.org/10.3390/w10070911 (2018).CAS 
Article 

Google Scholar 
32.Luijn, F. V., Boers, P. C. M. & Lijklema, L. Comparison of denitrification rates in lake sediments obtained by the N2 flux method, the 15N isotope pairing technique and the mass balance approach. Water Res. 30, 893–900. https://doi.org/10.1016/0043-1354(95)00250-2 (1996).Article 

Google Scholar 
33.Pfenning, K. S. & McMahon, P. B. Effect of nitrate, organic carbon, and temperature on potential denitrification rates in nitrate-rich riverbed sediments. J. Hydrol. 187, 283–295. https://doi.org/10.1016/S0022-1694(96)03052-1 (1997).ADS 
CAS 
Article 

Google Scholar 
34.Borges, A. V. et al. Globally significant greenhouse-gas emissions from African inland waters. Nat. Geosci. 8(8), 637–642. https://doi.org/10.1038/ngeo2486 (2015).ADS 
CAS 
Article 

Google Scholar 
35.Marzadri, A., Dee, M. M., Tonina, D., Bellin, A. & Tank, J. L. Role of surface and subsurface processes in scaling N2O emissions along riverine networks. Proc. Natl. Acad. Sci. U. S. A. 114(17), 4330–4335. https://doi.org/10.1073/pnas.1617454114 (2017).ADS 
CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
36.Soued, C., del Giorgio, P. A. & Maranger, R. Nitrous oxide sinks and emissions in boreal aquatic networks in Quebec. Nat. Geosci. 9(2), 116–120, https://www.x-mol.com/paperRedirect/68353 (2016).37.Hu, M. P., Chen, D. J. & Dahlgren, R. A. Modeling nitrous oxide emission from rivers: A global assessment. Glob. Change Biol. 22(11), 3566–3582. https://doi.org/10.1111/gcb.13351 (2016).ADS 
Article 

Google Scholar 
38.Murray, R., Erler, D. V., Rosentreter, J., Wells, N. S. & Eyre, B. D. Seasonal and spatial controls on N2O concentrations and emissions in low-nitrogen estuaries: Evidence from three tropical systems. Mar. Chem. https://doi.org/10.1016/j.marchem.2020.103779 (2020).Article 

Google Scholar 
39.Ji, Q. X., Babbin, A. R., Peng, X. F., Bowen, J. L. & Ward, B. B. Nitrogen substrate dependent nitrous oxide cycling in salt marsh sediments. J. Mar. Res. 73(3–4), 71–92. https://doi.org/10.1016/j.marchem.2020.103779 (2015).CAS 
Article 

Google Scholar 
40.Punshon, S. & Moore, R. M. Nitrous oxide production and consumption in a eutrophic coastal embayment. Mar. Chem. 91(1–4), 37–51. https://doi.org/10.1016/j.marchem.2004.04.003 (2004).CAS 
Article 

Google Scholar 
41.Corredor, J. E., Morell, J. M. & Bauza, J. Atmospheric nitrous oxide fluxes from mangrove sediments. Mar. Pollut. Bull. 38, 473–478. https://doi.org/10.1016/S0025-326X(98)00172-6 (1999).CAS 
Article 

Google Scholar 
42.Raymond, P. A. et al. Scaling the gas transfer velocity and hydraulic geometry in streams and small rivers. Limnol. Oceanogr. Fluids Environ. 2, 41–53. https://doi.org/10.1215/21573689-1597669 (2012).Article 

Google Scholar 
43.Alongi, D. M. Impact of global change on nutrient dynamics in mangrove forests. Forests. 9(10), 596. https://doi.org/10.3390/f9100596 (2018).Article 

Google Scholar 
44.Reef, R., Feller, I. C. & Lovelock, C. E. Nutrition of mangroves. Tree Physiol. 30, 1148–1160. https://doi.org/10.1093/treephys/tpq048 (2010).CAS 
Article 
PubMed 

Google Scholar 
45.Muller, D. et al. Nitrous oxide and methane in two tropical estuaries in a peat-dominated region of northwestern Borneo. Biogeosciences 13(8), 2415–2428. https://doi.org/10.5194/bg-13-2415-2016 (2016).ADS 
CAS 
Article 

Google Scholar 
46.Hasegawa, T. & Okino, T. Seasonal variation of denitrification rate in Lake Suwa sediment. Limnology 5(1), 33–39. https://doi.org/10.1007/PL00021725/citeas (2004).CAS 
Article 

Google Scholar 
47.Myrstener, M., Jonsson, A. & Bergström, A. K. The effects of temperature and resource availability on denitrification and relative N2O production in boreal lake sediments. J. Environ. Sci. (China).48.Strickland, J. D. H. & Parsons, T. R. A Practical Handbook of Seawater Analysis. 2nd edn. 310 (Fisheries Research Board of Canada, 1972).49.Grasshoff, K., Ehrhardt, M. & Kremling, K. Methods of seawater analysis. 2nd edn. 419 (Verlag Chemie, 1983).50.Garcia, H. & Gordon, L. Oxygen solubility in seawater: Better fitting equations. Limnol. Oceanogr. 37, 1307–1312. https://doi.org/10.4319/lo.1992.37.6.1307 (1992).ADS 
CAS 
Article 

Google Scholar 
51.Grasshoff, K., Ehrhardt, M. & Kremling, K. Methods of Seawater Analysis 3rd edn. (VCH, 1999).
Google Scholar 
52.David, A. R. Analysis of Total organic carbon. UMass Environmental Engineering Program (2012).53.Polunin, N. V. et al. Feeding relationships in Mediterranean bathyal assemblages elucidated by stable nitrogen and carbon isotope data. Mar. Ecol. Prog. Ser. 220, 13–23. https://doi.org/10.3354/meps220013 (2001).ADS 
CAS 
Article 

Google Scholar 
54.McAuliffe, C. GC determination of solutes by multiple phase equilibrations. Chem. Tech. 1, 46–50 (1971).
Google Scholar 
55.Liss, P. S. & Merlivat, L. Air-sea exchange rates: Introduction and synthesis, in the role of air-sea exchange in geochemical cycling. In (ed. Buat-Menard, P.) 113–127 (D Reidel, 1986) https://doi.org/10.1007/978-94-009-4738-2_5.56.Weiss, R. F. & Price, B. A. Nitrous oxide solubility in water and seawater. Mar. Chem. 8, 347–359. https://doi.org/10.1016/0304-4203(80)90024-9 (1980).CAS 
Article 

Google Scholar 
57.Rao, G. D., Rao, V. D. & Sarma, V. V. S. S. Distribution and air–sea exchange of Nitrous oxide in the Coastal Bay of Bengal during peak discharge period(southwest monsoon). Mar. Chem. 155, 1–9. https://doi.org/10.1016/j.marchem.2013.04.014 (2013).CAS 
Article 

Google Scholar  More

1.Van Hees, P. A. W., Jones, D. L., Finlay, R., Godbold, D. L. & Lundström, U. S. The carbon we do not see – The impact of low molecular weight compounds on carbon dynamics and respiration in forest soils: a review. Soil Biol. Biochem. 37, 1–13 (2005).Article 
CAS 

Google Scholar 
2.Shindo, H., Ohta, S. & Kuwatsuka, S. Behavior of phenolic substances in the decaying process of plants: IX. Distribution of phenolic acids in soils of paddy fields and forests. Soil Sci. Plant Nutr. 24, 233–243 (1978).CAS 
Article 

Google Scholar 
3.Katase, T. Distribution of different forms of p-hydroxybenzoic, vanillic, p-coumaric and ferulic acids in forest soil. Soil Sci. Plant Nutr. 27, 365–371 (1981).CAS 
Article 

Google Scholar 
4.Muscolo, A. & Sidari, M. Seasonal fluctuations in soil phenolics of a coniferous forest: effects on seed germination of different coniferous species. Plant Soil. 284, 305–318 (2006).CAS 
Article 

Google Scholar 
5.Whitehead, D. C., Dibb, H. & Hartley, R. D. Bound phenolic compounds in water extracts of soils, plant roots and leaf litter. Soil Biol. Biochem. 15, 133–136 (1983).CAS 
Article 

Google Scholar 
6.Kuiters, A. T. & Sarink, H. M. Leaching of phenolic compounds from leaf and needle litter of several deciduous and coniferous trees. Soil Biol. Biochem. 18, 475–480 (1986).CAS 
Article 

Google Scholar 
7.Gallet, C. & Pellissier, F. Phenolic compounds in natural solutions of a coniferous forest. J. Chem. Ecol. 23, 2401–2412 (1997).CAS 
Article 

Google Scholar 
8.Schofield, J. A., Hagerman, A. E. & Harold, A. Loss of tannins and other phenolics from willow leaf litter. J. Chem. Ecol. 24, 1409–1421 (1998).CAS 
Article 

Google Scholar 
9.Kaiser, K., Guggenberger, G., Haumaier, L. & Zech, W. Seasonal variations in the chemical composition of dissolved organic matter in organic forest floor layer leachates of old-growth Scots pine (Pinus sylvestris L.) and European beech (Fagus sylvatica L.) stands in northeastern Bavaria, German. Biogeochemistry. 55, 103–143 (2001).CAS 
Article 

Google Scholar 
10.Li H. et al. Forest gaps alter the total phenol dynamics in decomposing litter in an alpine fir forest. PLoS ONE. 11, e0148426 (2016).PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
11.Blagodatskaya, E. & Kuzyakov, Y. Mechanisms of real and apparent priming effects and their dependence on soil microbial biomass and community structure: critical review. Biol. Fertil. Soils. 45, 115–131 (2008).Article 

Google Scholar 
12.Nottingham, A. T., Turner, B. L., Chamberlain, P. M., Stott, A. W. & Tanner, E. V. J. Priming and microbial nutrient limitation in lowland tropical forest soils of contrasting fertility. Biogeochemistry. 111, 219–237 (2012).CAS 
Article 

Google Scholar 
13.Stewart, C. E., Moturi, P., Follett, R. F. & Halvorson, A. D. Lignin biochemistry and soil N determine crop residue decomposition and soil priming. Biogeochemistry. 124, 335–351 (2015).CAS 
Article 

Google Scholar 
14.Lonardo, D. P. Di et al. Priming of soil organic matter: chemical structure of added compounds is more important than the energy content. Soil Biol. Biochem. 108, 41–54 (2017).Article 
CAS 

Google Scholar 
15.Zwetsloot, M. J. et al. Prevalent root-derived phenolics drive shifts in microbial community composition and prime decomposition in forest soil. Soil Biol. Biochem. 145, 530–541 (2020).Article 
CAS 

Google Scholar 
16.Tao, X. et al. Winter warming in Alaska accelerates lignin decomposition contributed by Proteobacteria. Microbiome 8, 84 (2020).PubMed 
PubMed Central 
Article 

Google Scholar 
17.Wutzler, T. & Reichstein, M. Priming and substrate quality interactions in soil organic matter models. Biogeosciences. 10, 2089–2103 (2013).Article 

Google Scholar 
18.Guenet, B. et al. Impact of priming on global soil carbon stocks. Glob Chang Biol. 24, 1873–1883 (2018).PubMed 
Article 
PubMed Central 

Google Scholar 
19.Schöning, I. & Kögel-Knabner, I. Chemical composition of young and old carbon pools throughout Cambisol and Luvisol profiles under forests. Soil Biol. Biochem. 38, 2411–2424 (2006).Article 
CAS 

Google Scholar 
20.Kleber, M. et al. Old and stable soil organic matter is not necessarily chemically recalcitrant: implications for modeling concepts and temperature sensitivity. Glob Chang Biol. 17, 1097–1107 (2011).Article 

Google Scholar 
21.Northup, R. R., Dahlgren, R. A. & Yu, Z. Intraspecific variation of conifer phenolic concentration on a marine terrace soil acidity gradient; a new interpretation. Plant Soil. 171, 255–262 (1995).CAS 
Article 

Google Scholar 
22.Sanger, L. J., Cox, P., Splatt, P., Whelan, M. J. & Anderson, J. M. Variability in the quality of Pinus sylvestris needles and litter from sites with different soil characteristics: Lignin and phenylpropanoid signature. Soil Biol. Biochem. 28, 829–835 (1996).CAS 
Article 

Google Scholar 
23.Thevenot, M., Dignac, M. F. & Rumpel, C. Fate of lignins in soils: a review. Soil Biol. Biochem. 42, 1200–1211 (2010).CAS 
Article 

Google Scholar 
24.Zwetsloot, M. J. & Bauerle, T. L. Phenolic root exudate and tissue compounds vary widely among temperate forest tree species and have contrasting effects on soil microbial respiration. New Phytol. 218, 530–541 (2018).CAS 
PubMed 
Article 

Google Scholar 
25.Burges, N., Hurst, H. & Walkden, B. The phenolic constituents of humic acid and their relation to the lignin of the plant cover. Geochim. Cosmochim. Acta. 28, 1547–1554 (1964).CAS 
Article 

Google Scholar 
26.Kuiters, A. T. & Denneman, C. A. J. Water-soluble phenolic substances in soils under several coniferous and deciduous tree species. Soil Biol. Biochem. 19, 765–769 (1987).CAS 
Article 

Google Scholar 
27.Jalal, M. A. F. & Read, D. J. The organic acid composition of Calluna heathland soil with special reference to phyto- and fungitoxicity. Plant Soil. 70, 273–286 (1983).CAS 
Article 

Google Scholar 
28.Shindo, H., Ohta, S. & Kuwatsuka, S. Behavior of phenolic substances in the decaying process of plants: IX. Distribution of phenolic acids in soils of paddy fields and forests. Soil Sci. Plant Nutr. 24, 233–243 (1978).CAS 
Article 

Google Scholar 
29.Whitehead, D. C., Dibb, H. & Hartley, R. D. Extractant pH and the release of phenolic compounds from soils, plant roots and leaf litter. Soil Biol. Biochem. 13, 343–348 (1981).CAS 
Article 

Google Scholar 
30.Ed, V., Boyd, S. & Mokma, D. Extraction of phenolic compounds from a spodsol profile. Soil Sci. 140, 412–420 (1985).Article 

Google Scholar 
31.Wang, Y. et al. Environmental behaviors of phenolic acids dominated their rhizodeposition in boreal poplar plantation forest soils. J. Soils Sediments. 16, 1858–1870 (2016).CAS 
Article 

Google Scholar 
32.Phillips, R. P. et al. Tree species and mycorrhizal associations influence the magnitude of rhizosphere effects. Ecology. 87, 1302–1313 (2006).PubMed 
Article 

Google Scholar 
33.Blum, U. & Shafer, S. R. Microbial populations and phenolic acids in soil. Soil Biol. Biochem. 20, 793–800 (1988).CAS 
Article 

Google Scholar 
34.Shafer, S. R. & Blum, U. Influence of Phenolic acids on microbial populations in the rhizosphere of cucumber. J. Chem. Ecol. 17, 369–389 (1991).CAS 
PubMed 
Article 

Google Scholar 
35.Eilers, K. G., Lauber, C. L., Knight, R. & Fierer, N. Shifts in bacterial community structure associated with inputs of low molecular weight carbon compounds to soil. Soil Biol. Biochem. 42, 896–903 (2010).CAS 
Article 

Google Scholar 
36.Morrissey, E. M. et al. Phylogenetic organization of bacterial activity. ISME J. 10, 2336–2340 (2016).PubMed 
PubMed Central 
Article 

Google Scholar 
37.Huang P., Wang T., Wang M., Wu M., Hsu N. Retention of phenolic acids by noncrystalline hydroxy-aluminum and-iron compounds and clay minerals of soils. Soil Sci. 123, 213–219 (1977).CAS 
Article 

Google Scholar 
38.Cecchi, A. M., Koskinen, W. C., Cheng, H. H. & Haider, K. Sorption-desorption of phenolic acids as affected by soil properties. Biol. Fertil. Soils. 39, 235–242 (2004).CAS 
Article 

Google Scholar 
39.Shindo, H. & Kuwatsuka, S. Behavior of phenolic substances in the decaying process of plants: IV adsorption and movement of phenolic acids in soils. Soil Sci. Plant Nutr. 22, 23–33 (1976).CAS 
Article 

Google Scholar 
40.DeAngelis K. M. et al. Characterization of trapped lignin-degrading microbes in tropical forest soil. PLoS ONE. 6, e19306 (2011).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
41.Pold G., Melillo J. M., DeAngelis K. M. Two decades of warming increases diversity of a potentially lignolytic bacterial community. Front Microbiol. 6, 480 (2015).PubMed 
PubMed Central 
Article 

Google Scholar 
42.Wilhelm, R. C., Singh, R., Eltis, L. D. & Mohn, W. W. Bacterial contributions to delignification and lignocellulose degradation in forest soils with metagenomic and quantitative stable isotope probing. ISME J. 13, 413–429 (2019).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
43.Folman, L. B., Klein Gunnewiek, P. J. A., Boddy, L., De & Boer, W. Impact of white-rot fungi on numbers and community composition of bacteria colonizing beech wood from forest soil. FEMS Microbiol. Ecol. 63, 181–191 (2008).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
44.Valášková, V. et al. Phylogenetic composition and properties of bacteria coexisting with the fungus Hypholoma fasciculare in decaying wood. ISME J. 3, 1218–1221 (2009).PubMed 
Article 
CAS 
PubMed Central 

Google Scholar 
45.Mandal, S. M., Chakraborty, D. & Dey, S. Phenolic acids act as signaling molecules in plant-microbe symbioses. Plant Signal Behav. 5, 359–368 (2010).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
46.Munoz Aguilar, M. et al. Chemotaxis of rhizobium leguminosarum biovar phaseoli towards flavonoid inducers of the symbiotic nodulation genes. J. Gen. Microbiol. 134, 2741–2746 (1988).CAS 

Google Scholar 
47.Morrissey, E. M. et al. Bacterial carbon use plasticity, phylogenetic diversity and the priming of soil organic matter. ISME J. 11, 1890–1899 (2017).PubMed 
PubMed Central 
Article 

Google Scholar 
48.Fontaine, S., Mariotti, A. & Abbadie, L. The priming effect of organic matter: a question of microbial competition? Soil Biol. Biochem. 35, 837–843 (2003).CAS 
Article 

Google Scholar 
49.Liu, X. J. A. et al. The soil priming effect: consistent across ecosystems, elusive mechanisms. Soil Biol Biochem. 140, 107617 (2020).CAS 
Article 

Google Scholar 
50.Fanin, N., Alavoine, G. & Bertrand, I. Temporal dynamics of litter quality, soil properties and microbial strategies as main drivers of the priming effect. Geoderma [Internet]. 377, 114576 (2020).CAS 
Article 

Google Scholar 
51.Fuchs, G., Boll, M. & Heider, J. Microbial degradation of aromatic compounds – from one strategy to four. Nat. Rev. Microbiol. 9, 803–816 (2011).CAS 
PubMed 
Article 

Google Scholar 
52.Yang, Z. H. & Ji, G. D. Quantitative response relationships between degradation rates and functional genes during the degradation of beta-cypermethrin in soil. J. Hazard Mater. 299, 719–724 (2015).CAS 
PubMed 
Article 

Google Scholar 
53.Nishiyama, E., Ohtsubo, Y., Nagata, Y. & Tsuda, M. Identification of Burkholderia multivorans ATCC 17616 genes induced in soil environment by in vivo expression technology. Environ. Microbiol. 12, 2539–2558 (2010).CAS 
PubMed 
Article 

Google Scholar 
54.Wilhelm et al. Paraburkholderia madseniana sp. nov., a phenolic acid-degrading bacterium isolated from acidic forest soil. Int. J. Syst. Evol. Microbiol. 70, (2020). https://doi.org/10.1099/ijsem.0.004029.55.Pallant, E. & Riha, S. J. Surface soil acidification under red pine and Norway spruce. Soil Sci. Soc. Am. J. 54, 1124–1130 (1990).CAS 
Article 

Google Scholar 
56.Fahey T. J. et al. Earthworm effects on the incorporation of litter C and N into soil organic matter in a sugar maple forest. Ecol. Appl. 23, 1185–1201 (2013).PubMed 
Article 

Google Scholar 
57.Melvin, A. M. & Goodale, C. L. Tree species and earthworm effects on soil nutrient distribution and turnover in a northeastern United States common garden. Can. J. For. Res. 43, 180–187 (2013).CAS 
Article 

Google Scholar 
58.Suarez E. Invasion of Northern Hardwood Forests by Exotic Earthworm Communities in South-Central New York. Cornell; 2004.59.Greweling T., Peech M. Chemical soil tests. Ithaca; 1960.60.Griffiths, R. I., Whiteley, A. S., O’Donnell, A. G. & Bailey, M. J. Rapid method for coextraction of DNA and RNA from natural environments for analysis of ribosomal DNA- and rRNA-based microbial community composition. Appl. Environ. Microbiol. 66, 5488–5491 (2000).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
61.Neufeld, J. D. et al. DNA stable-isotope probing. Nat. Protoc. 2, 860–866 (2007).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
62.Wilhelm, R., Szeitz, A., Klassen, T. L. & Mohn, W. W. Sensitive, efficient quantitation of 13C-enriched nucleic acids via ultrahigh-performance liquid chromatography-tandem mass spectrometry for applications in stable isotope probing. Appl. Environ. Microbiol. 80, 7206–7211 (2014).PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
63.Kozich, J. J., Westcott, S. L., Baxter, N. T., Highlander, S. K. & Schloss, P. D. Development of a dual-index sequencing strategy and curation pipeline for analyzing amplicon sequence data on the miseq illumina sequencing platform. Appl. Environ. Microbiol. 79, 5112–5120 (2013).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
64.Love, M. I., Huber, W. & Anders, S. Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2. Genome Biol. 15, 1–21 (2014).Article 
CAS 

Google Scholar 
65.De Caceres, M. & Legendre, P. Associations between species and groups of sites: indices and statistical inference. Ecology, Ecology. 90, 3566–3574 (2009). http://sites.google.com/site/miqueldecaceres/.PubMed 
Article 

Google Scholar 
66.Wilhelm, R. C., Niederberger, T. D., Greer, C. & Whyte, L. G. Microbial diversity of active layer and permafrost in an acidic wetland from the Canadian High Arctic. Can. J. Microbiol. 57, 303–315 (2011).CAS 
PubMed 
Article 

Google Scholar 
67.Ye, J. et al. Primer-BLAST: a tool to design target-specific primers for polymerase chain reaction. BMC Bioinformatics. 13, 134 (2012).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
68.López-Gutiérrez, J. C. et al. Quantification of a novel group of nitrate-reducing bacteria in the environment by real-time PCR. J. Microbiol. Methods. 57, 399–407 (2004).PubMed 
Article 
CAS 

Google Scholar 
69.Markowitz, V. M. et al. IMG/M: A data management and analysis system for metagenomes. Nucleic Acids Res. 36(Suppl. 1), 534–538 (2008).
Google Scholar 
70.Martiny, A. C., Treseder, K. & Pusch, G. Phylogenetic conservatism of functional traits in microorganisms. ISME J. 7, 830–838 (2013).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
71.Bolyen, E. et al. Reproducible, interactive, scalable and extensible microbiome data science using QIIME 2. Nat. Biotechnol. 37, 852–857 (2019).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
72.Callahan, B. J., McMurdie, P. J. & Holmes, S. P. Exact sequence variants should replace operational taxonomic units in marker-gene data analysis. ISME J. 11, 2639–2643 (2017).PubMed 
PubMed Central 
Article 

Google Scholar 
73.Quast, C. et al. The SILVA ribosomal RNA gene database project: Improved data processing and web-based tools. Nucleic Acids Res. 41(D1), 590–596 (2013).Article 
CAS 

Google Scholar 
74.Wickham, H. Elegant graphics for data analysis. Media. 35, 211 (2009).
Google Scholar 
75.McMurdie P. J., Holmes S. Phyloseq: an R package for reproducible interactive analysis and graphics of microbiome census data. PLoS ONE. 8, e61217 (2013).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
76.Oksanen J. et al. Vegan: community ecology package. R Packag. 2015;77.Katoh, K. & Standley, D. M. MAFFT multiple sequence alignment software version 7: Improvements in performance and usability. Mol. Biol. Evol. 30, 772–780 (2013).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
78.Price M. N., Dehal P. S., Arkin A. P. FastTree 2 – Approximately maximum-likelihood trees for large alignments. PLoS ONE. 5, e9490 (2010).Article 
CAS 

Google Scholar 
79.Wilhelm R. C. et al. Paraburkholderia solitsugae sp. nov. and Paraburkholderia elongata sp. nov., phenolic acid-degrading bacteria isolated from forest soil and emended description of Paraburkholderia madseniana. Int. J. Syst. Evol. Microbiol. 70, 5093–5105 (2020).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
80.Chain, P. S. G. et al. Burkholderia xenovorans LB400 harbors a multi-replicon, 9.73-Mbp genome shaped for versatility. PNAS. 103, 15280–15287 (2006).PubMed 
Article 
PubMed Central 

Google Scholar 
81.Mason-Jones, K. & Kuzyakov, Y. “Non-metabolizable” glucose analogue shines new light on priming mechanisms: Triggering of microbial metabolism. Soil Biol. Biochem. 107, 68–76 (2017).CAS 
Article 

Google Scholar 
82.Yuan, Y. et al. Exudate components exert different influences on microbially mediated C losses in simulated rhizosphere soils of a spruce plantation. Plant Soil. 419, 127–140 (2017).CAS 
Article 

Google Scholar 
83.Liu, X. J. A. et al. Labile carbon input determines the direction and magnitude of the priming effect. Appl. Soil Ecol. 109, 7–13 (2017).Article 

Google Scholar 
84.Sugai, S. F. & Schimel, J. P. Decomposition and biomass incorporation of 14C-labeled glucose and phenolics in taiga forest floor: effect of substrate quality, successional state, and season. Soil Biol. Biochem. 25, 1379–1389 (1993).CAS 
Article 

Google Scholar 
85.Fontaine, S. et al. Fungi mediate long term sequestration of carbon and nitrogen in soil through their priming effect. Soil Biol. Biochem. 43, 86–96 (2011).CAS 
Article 

Google Scholar 
86.Zhu, Z. et al. Microbial stoichiometric flexibility regulates rice straw mineralization and its priming effect in paddy soil. Soil Biol. Biochem. 121, 67–76 (2018).CAS 
Article 

Google Scholar 
87.Roller, B. R. K., Stoddard, S. F. & Schmidt, T. M. Exploiting rRNA operon copy number to investigate bacterial reproductive strategies. Nat. Microbiol. 1, 1–7 (2016).Article 
CAS 

Google Scholar 
88.Smirnova, G. V. & Oktyabrsky, O. N. Relationship between Escherichia coli growth rate and bacterial susceptibility to ciprofloxacin. FEMS Microbiol. Lett. 365, 1–6 (2018).Article 
CAS 

Google Scholar 
89.Klappenbach, J. A., Dunbar, J. M. & Schmidt, T. M. rRNA operon copy number reflects ecological strategies of bacteria. Appl. Environ. Microbiol. 66, 1328–1333 (2000).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
90.Méndez V., Agulló L., González M., Seeger M. The homogentisate and homoprotocatechuate central pathways are involved in 3- and 4-hydroxyphenylacetate degradation by Burkholderia xenovorans LB400. PLoS ONE. 6, e17583 (2011).PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
91.Johnson, G. R. & Olsen, R. H. Multiple pathways for toluene degradation in Burkholderia sp. strain JS150. Appl. Environ. Microbiol. 63, 4047–4052 (1997).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
92.Andreolli, M. et al. Endophytic Burkholderia fungorum DBT1 can improve phytoremediation efficiency of polycyclic aromatic hydrocarbons. Chemosphere. 92, 688–694 (2013).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
93.Somtrakoon, K. et al. Phenanthrene stimulates the degradation of pyrene and fluoranthene by Burkholderia sp. VUN10013. World J. Microbiol. Biotechnol. 24, 523–531 (2008).CAS 
Article 

Google Scholar 
94.Chain, P. S. G. et al. Burkholderia xenovorans LB400 harbors a multi-replicon, 9.73-Mbp genome shaped for versatility. Proc. Natl. Acad. Sci. 103, 15280–15287 (2006).PubMed 
Article 
PubMed Central 

Google Scholar 
95.Raj, A., Krishna Reddy, M. M. & Chandra, R. Identification of low molecular weight aromatic compounds by gas chromatography-mass spectrometry (GC-MS) from kraft lignin degradation by three Bacillus sp. Int. Biodeterior Biodegrad. 59, 292–296 (2007).CAS 
Article 

Google Scholar 
96.Shi, Y. et al. Biochemical investigation of kraft lignin degradation by Pandoraea sp. B-6 isolated from bamboo slips. Bioprocess Biosyst. Eng. 36, 1957–1965 (2013).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
97.Moraes, E. C. et al. Lignolytic-consortium omics analyses reveal novel genomes and pathways involved in lignin modification and valorization. Biotechnol. Biofuels. 11, 1–16 (2018).CAS 
Article 

Google Scholar 
98.Coenye, T. et al. Burkholderia fungorum sp. nov. and Burkholderia caledonica sp. nov., two new species isolated from the environment, animals and human clinical samples. Int. J. Syst. Evol. Microbiol. 51, 1099–1107 (2001).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
99.Lim, Y. W., Baik, K. S., Han, S. K., Kim, S. B. & Bae, K. S. Burkholderia sordidicola sp. nov., isolated from the white-rot fungus Phanerochaete sordida. Int. J. Syst. Evol. Microbiol. 53, 1631–1636 (2003).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
100.Herzog C. et al. Microbial succession on decomposing root litter in a drought-prone Scots pine forest. ISME J. 13, 2346–2362 (2019).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
101.Yeoh Y. K. et al. Evolutionary conservation of a core root microbiome across plant phyla along a tropical soil chronosequence. Nat. Commun. 8, 215 (2017).PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
102.Zhalnina, K. et al. Dynamic root exudate chemistry and microbial substrate preferences drive patterns in rhizosphere microbial community assembly. Nat. Microbiol. 3, 1–11 (2018).Article 
CAS 

Google Scholar 
103.Badri, D. V., Chaparro, J. M., Zhang, R., Shen, Q. & Vivanco, J. M. Application of natural blends of phytochemicals derived from the root exudates of arabidopsis to the soil reveal that phenolic-related compounds predominantly modulate the soil microbiome. J. Biol. Chem. 288, 4502–4512 (2013).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
104.Sinsabaugh, R. L. Phenol oxidase, peroxidase and organic matter dynamics of soil. Soil Biol. Biochem. 42, 391–404 (2010).CAS 
Article 

Google Scholar 
105.Henning, J. A. et al. Root bacterial endophytes alter plant phenotype, but not physiology. PeerJ. 2016, 1–20 (2016).
Google Scholar 
106.Caballero-Mellado, J., Martínez-Aguilar, L., Paredes-Valdez, G., & Estrada-de los Santos, P. Burkholderia unamae sp. nov., an N2-fixing rhizospheric and endophytic species. Int. J. Syst. Evol. Microbiol. 54, 1165–1172 (2004).CAS 
PubMed 
Article 

Google Scholar 
107.Martínez-Aguilar, L. et al. Burkholderia caballeronis sp. nov., a nitrogen fixing species isolated from tomato (Lycopersicon esculentum) with the ability to effectively nodulate Phaseolus vulgaris. Antonie van Leeuwenhoek. Int. J. Gen. Mol. Microbiol. 104, 1063–1071 (2013).
Google Scholar 
108.De Meyer, S. E. et al. Symbiotic and non-symbiotic Paraburkholderia isolated from South African Lebeckia ambigua root nodules and the description of Paraburkholderia fynbosensis sp. Nov. Int. J. Syst. Evol. Microbiol. 68, 2607–2614 (2018).PubMed 
Article 
CAS 

Google Scholar 
109.Peeters, C. et al. Phylogenomic study of Burkholderia glathei-like organisms, proposal of 13 novel Burkholderia species and emended descriptions of Burkholderia sordidicola, Burkholderia zhejiangensis, and Burkholderia grimmiae. Front Microbiol. 7, 1–19 (2016).
Google Scholar 
110.Vandamme, P. et al. Burkholderia humi sp. nov., Burkholderia choica sp. nov., Burkholderia telluris sp. nov., Burkholderia terrestris sp. nov. and Burkholderia udeis sp. nov.: Burkholderia glathei-like bacteria from soil and rhizosph. Int. J. Syst. Evol. Microbiol. 63(PART 12), 4707–4718 (2013).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
111.Shiraishi, A., Matsushita, N. & Hougetsu, T. Nodulation in black locust by the Gammaproteobacteria Pseudomonas sp. and the Betaproteobacteria Burkholderia sp. Syst. Appl. Microbiol. 33, 269–274 (2010).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
112.Thijs, S. et al. Exploring the rhizospheric and endophytic bacterial communities of Acer pseudoplatanus growing on a TNT-contaminated soil: towards the development of a rhizocompetent TNT-detoxifying plant growth promoting consortium. Plant Soil. 385, 15–36 (2014).CAS 
Article 

Google Scholar 
113.Mavengere, N. R., Ellis, A. G. & Le Roux, J. J. Burkholderia aspalathi sp. nov., isolated from root nodules of the South African legume Aspalathus abietina Thunb. Int. J. Syst. Evol. Microbiol. 64(PART 6), 1906–1912 (2014).PubMed 
Article 
PubMed Central 

Google Scholar 
114.Blair, P. M. et al. Exploration of the biosynthetic potential of the populus microbiome. mSystems. 3, 1–17 (2018).Article 

Google Scholar 
115.Peters, N. K. & Verma, D. P. S. Phenolic compounds as regulators of gene expression in plant-microbe interactions. Mol. Plant-Microbe Interact. 3, 4–8 (1990).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
116.Kuzyakov, Y. & Blagodatskaya, E. Microbial hotspots and hot moments in soil: concept & review. Soil Biol. Biochem. 83, 184–199 (2015).CAS 
Article 

Google Scholar  More

1.Higuchi, R. et al. DNA sequences from the quagga, an extinct member of the horse family. Nature 312, 282–284 (1984).CAS 
PubMed 
Article 
ADS 

Google Scholar 
2.Dabney, J. et al. Complete mitochondrial genome sequence of a Middle Pleistocene cave bear reconstructed from ultrashort DNA fragments. Proc. Natl. Acad. Sci. U. S. A. 110, 15758–15763 (2013).CAS 
PubMed 
PubMed Central 
Article 
ADS 

Google Scholar 
3.Hansen, H. B. et al. Comparing ancient DNA preservation in petrous bone and tooth cementum. PLoS ONE 12, e0170940 (2017).PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
4.Hagan, R. W. et al. Comparison of extraction methods for recovering ancient microbial DNA from paleofeces. Am. J. Phys. Anthropol. 171, 275–284 (2020).PubMed 
Article 

Google Scholar 
5.Epp, L. S., Zimmermann, H. H. & Stoof-Leichsenring, K. R. Sampling and extraction of ancient DNA from sediments. In Ancient DNA. Methods in Molecular Biology (eds Shapiro, B. et al.) 31–44 (Humana Press, 2019).
Google Scholar 
6.Modi, A. et al. Combined methodologies for gaining much information from ancient dental calculus: testing experimental strategies for simultaneously analysing DNA and food residues. Archaeol. Anthropol. Sci. 12, 10 (2020).Article 

Google Scholar 
7.Campos, P. F. & Gilbert, M. T. P. DNA extraction from keratin and chitin. In Ancient DNA. Methods in Molecular Biology (eds Shapiro, B. et al.) 57–63 (Humana Press, 2019).
Google Scholar 
8.Adler, C. J. et al. Sequencing ancient calcified dental plaque shows changes in oral microbiota with dietary shifts of the neolithic and industrial revolutions. Nat. Genet. 45, 450-455e1 (2013).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
9.Warinner, C. et al. Pathogens and host immunity in the ancient human oral cavity. Nat. Genet. 46, 336–344 (2014).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
10.Weyrich, L. S. et al. Neanderthal behaviour, diet, and disease inferred from ancient DNA in dental calculus. Nature 544, 357–361 (2017).CAS 
PubMed 
Article 
ADS 

Google Scholar 
11.Slon, V. et al. Neandertal and Denisovan DNA from Pleistocene sediments. Science 356, 605–608 (2017).CAS 
PubMed 
Article 
ADS 

Google Scholar 
12.Teasdale, M. D. et al. The York Gospels: a 1000-year biological palimpsest. R. Soc. Open. Sci. 4, 170988 (2017).PubMed 
PubMed Central 
Article 
ADS 
CAS 

Google Scholar 
13.Boast, A. et al. Coprolites reveal ecological interactions lost with the extinction of New Zealand birds. Proc. Natl. Acad. Sci. U. S. A. 115, 1546–1551 (2018).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
14.Zarrillo, S. et al. The use and domestication of Theobroma cacao during the mid-Holocene in the upper Amazon. Nat. Ecol. Evol. 2, 1879–1888 (2018).PubMed 
Article 

Google Scholar 
15.Cano, R. J. et al. Amplification and sequencing of DNA from a 120–135 million-year-old weevil. Nature 363, 536–538 (1993).CAS 
PubMed 
Article 
ADS 

Google Scholar 
16.DeSalle, R. et al. DNA sequences from a fossil termite in Oligo-Miocene amber and their phylogenetic implications. Science 257, 1933–1936 (1992).CAS 
PubMed 
Article 
ADS 

Google Scholar 
17.Sherratt, E. et al. Amber fossils demonstrate deep-time stability of Caribbean lizard communities. Proc. Natl. Acad. Sci. U. S. A. 112, 9961–9966 (2015).CAS 
PubMed 
PubMed Central 
Article 
ADS 

Google Scholar 
18.Sadowski, E. M. et al. Carnivorous leaves from Baltic amber. Proc. Natl. Acad. Sci. U. S. A. 112, 190–195 (2015).CAS 
PubMed 
Article 
ADS 

Google Scholar 
19.Xing, L. et al. A feathered dinosaur tail with primitive plumage trapped in mid-Cretaceous amber. Curr. Biol. 26, 3352–3360 (2016).CAS 
PubMed 
Article 

Google Scholar 
20.Rikkinen, J., Grimaldi, D. A. & Schmidt, A. R. Morphological stasis in the first myxomycete from the Mesozoic, and the likely role of cryptobiosis. Sci. Rep. 9, 19730 (2019).CAS 
PubMed 
PubMed Central 
Article 
ADS 

Google Scholar 
21.Peñalver, E. et al. Thrips pollination of Mesozoic gymnosperms. Proc. Natl. Acad. Sci. U. S. A. 109, 8623–8628 (2012).PubMed 
PubMed Central 
Article 
ADS 

Google Scholar 
22.Cai, C. et al. Beetle pollination of cycads in the mesozoic. Curr. Biol. 28, 2806-2812.e1 (2018).CAS 
PubMed 
Article 

Google Scholar 
23.Bao, T., Wang, B., Li, J. & Dilcher, D. Pollination of Cretaceous flowers. Proc. Natl. Acad. Sci. U. S. A. 116, 24707–24711 (2019).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
24.Labandeira, C. Amber. Paleont. Soc. Pap. 20, 163–215 (2014).Article 

Google Scholar 
25.Solórzano-Kraemer, M. M. et al. A revised definition for copal and its significance for palaeontological and Anthropocene biodiversity-loss studies. Sci. Rep. 10, 19904 (2020).PubMed 
PubMed Central 
Article 
ADS 
CAS 

Google Scholar 
26.Clifford, D. J. & Hatcher, P. G. Structural transformations of polylabdanoid resinites during maturation. Org. Geochem. 23, 407–418 (1995).CAS 
Article 

Google Scholar 
27.Lambert, J. B., Santiago-Blay, J. A., Wu, Y. & Levy, A. J. Examination of amber and 490 related materials by NMR spectroscopy. Magn. Reson. Chem. 53, 2–8 (2015).CAS 
PubMed 
Article 

Google Scholar 
28.Stankiewicz, B. A. et al. Chemical preservation of plants and insects in natural resins. Proc. Biol. Sci. 265, 641–647 (1998).CAS 
PubMed Central 
Article 

Google Scholar 
29.McCoy, V. E. et al. Ancient amino acids from fossil feathers in amber. Sci. Rep. 9, 6420 (2019).PubMed 
PubMed Central 
Article 
ADS 
CAS 

Google Scholar 
30.Bada, J. L. et al. Amino acid racemization in amber-entombed insects: implications for DNA preservation. Geochim. Cosmochim. Acta. 58, 3131–3135 (1994).CAS 
PubMed 
Article 
ADS 

Google Scholar 
31.Collins, M. J. et al. Is amino acid racemization a useful tool for screening for ancient DNA in bone?. Proc. R. Soc. B 276, 2971–2977 (2009).CAS 
PubMed 
Article 

Google Scholar 
32.Allentoft, M. E. et al. The half-life of DNA in bone: measuring decay kinetics in 158 dated fossils. Proc. R. Soc. B 279, 4724–4733 (2012).CAS 
PubMed 
Article 

Google Scholar 
33.Kistler, L. et al. A new model for ancient DNA decay based on paleogenomic meta-analysis. Nucleic Acids Res. 45, 6310–6320 (2017).CAS 
PubMed 
PubMed Central 
Article 
ADS 

Google Scholar 
34.DeSalle, R., Barcia, M. & Wray, C. PCR jumping in clones of 30-million-year-old DNA fragments from amber preserved termites (Mastotermes electrodominicus). Experientia 49, 906–909 (1993).CAS 
PubMed 
Article 

Google Scholar 
35.Poinar, G. O., Poinar, H. N. & Cano, R. J. DNA from amber inclusions. In Ancient DNA (eds Herrmann, B. & Hummel, S.) 92–103 (Springer, 1994).
Google Scholar 
36.Austin, J. J. et al. Problems of reproducibility: does geologically ancient DNA survive in amber-preserved insects?. Proc. Roy. Soc. Lond. Ser. B 264, 467–474 (1997).CAS 
Article 
ADS 

Google Scholar 
37.Penney, D. et al. Absence of ancient DNA in sub-fossil insect inclusions preserved in ‘Anthropocene’ Colombian copal. PLoS ONE 8, e73150 (2013).CAS 
PubMed 
PubMed Central 
Article 
ADS 

Google Scholar 
38.Peris, D. et al. DNA from resin-embedded organisms: past, present and future. PLoS ONE 15, e0239521 (2020).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
39.Reich, D. et al. Genetic history of an archaic hominin group from Denisova Cave in Siberia. Nature 468, 1053–1060 (2010).CAS 
PubMed 
PubMed Central 
Article 
ADS 

Google Scholar 
40.Meyer, M. et al. A high-coverage genome sequence from an archaic Denisovan individual. Science 338, 222–226 (2012).CAS 
PubMed 
PubMed Central 
Article 
ADS 

Google Scholar 
41.Prüfer, K. et al. The complete genome sequence of a Neandertal from the Altai Mountains. Nature 505, 43–49 (2014).PubMed 
Article 
ADS 
CAS 

Google Scholar 
42.Prüfer, K. et al. A high-coverage Neandertal genome from Vindija Cave in Croatia. Science 358, 655–658 (2017).PubMed 
PubMed Central 
Article 
ADS 
CAS 

Google Scholar 
43.Meyer, M. et al. Nuclear DNA sequences from the Middle Pleistocene Sima de los Huesos hominins. Nature 531, 504–507 (2016).CAS 
PubMed 
Article 
ADS 

Google Scholar 
44.Gilbert, M. T., Bandelt, H. J., Hofreiter, M. & Barnes, I. Assessing ancient DNA studies. Trends Ecol. Evol. 20, 541–544 (2005).PubMed 
Article 

Google Scholar 
45.Willerslev, E. & Cooper, A. Ancient DNA. Proc. Biol. Sci. 272, 3–16 (2005).CAS 
PubMed 

Google Scholar 
46.Penney, D., Wadsworth, C. & Green, D. I. Extraction of inclusions from (sub)fossil resins, with description of a new species of stingless bee (Hymenoptera: Apidae: Meliponini), in quaternary Colombian copal. Paleontol. Contrib. 2013, 7:1–6 (2013).
Google Scholar 
47.Meyer, M. & Kircher, M. Illumina sequencing library preparation for highly multiplexed target capture and sequencing. Cold Spring Harb. Protoc. 6, pdb.prot5448 (2010).Article 

Google Scholar 
48.Modi, A. et al. Complete mitochondrial sequences from Mesolithic Sardinia. Sci. Rep. 7, 42869 (2017).PubMed 
PubMed Central 
Article 
ADS 

Google Scholar 
49.Peltzer, A. et al. EAGER: efficient ancient genome reconstruction. Genome Biol. 17, 60 (2016).PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
50.Andrews, S. FastQC: a quality control tool for high throughput sequence data (2010). Available online at: http://www.bioinformatics.babraham.ac.uk/projects/fastqc51.Li, H. & Durbin, R. Fast and accurate short read alignment with Burrows–Wheeler transform. Bioinformatics 25, 1754–1760 (2009).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
52.Schubert, M. et al. Improving ancient DNA read mapping against modern reference genomes. BMC Genomics 13, 178 (2012).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
53.Li, H. et al. The Sequence alignment/map (SAM) format and SAMtools. Bioinformatics 25, 2078–2079 (2009).PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
54.Altschul, S. et al. Basic local alignment search tool. J. Mol. Biol. 215, 403–410 (1990).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
55.Jackman, S. D. et al. ABySS 2.0: resource-efficient assembly of large genomes using a Bloom filter. Genome Res. 27, 768–777 (2017).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
56.Huson, D. H. et al. MEGAN community edition: interactive exploration and analysis of large-scale microbiome sequencing data. PLoS Comput. Biol. 12, e1004957 (2016).PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
57.Hofreiter, M., Jaenicke, V., Serre, D., Haeseler, A. & Pääbo, S. DNA sequences from multiple amplifications reveal artifacts induced by cytosine deamination in ancient DNA. Nucleic Acids Res. 9, 4793–4799 (2011).
Google Scholar 
58.Briggs, A. W. et al. Patterns of damage in genomic DNA sequences from a Neandertal. Proc. Natl. Acad. Sci. U. S. A. 104, 14616–14621 (2007).CAS 
PubMed 
PubMed Central 
Article 
ADS 

Google Scholar 
59.Sawyer, S., Krause, J., Guschanski, K., Savolainen, V. & Pääbo, S. Temporal patterns of nucleotide misincorporations and DNA fragmentation in ancient DNA. PLoS ONE 7, e34131 (2012).CAS 
PubMed 
PubMed Central 
Article 
ADS 

Google Scholar 
60.Jonsson, H., Ginolhac, A., Schubert, M., Johnson, P. L. & Orlando, L. mapDamage2.0: fast approximate Bayesian estimates of ancient DNA damage parameters. Bioinformatics 29, 1682–1684 (2013).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
61.Edgar, R. C. MUSCLE: multiple sequence alignment with high accuracy and high throughput. Nucleic Acids Res. 32, 1792–1797 (2004).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
62.Kumar, S. et al. MEGA X: molecular evolutionary genetics analysis across computing platforms. Mol. Biol. Evol. 35, 1547–1549 (2018).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
63.Noonan, J. P. et al. Genomic sequencing of pleistocene cave bears. Science 309, 597–599 (2005).CAS 
PubMed 
Article 
ADS 

Google Scholar 
64.Green, R. E. et al. Analysis of one million base pairs of Neanderthal DNA. Nature 444, 330–336 (2006).CAS 
PubMed 
Article 
ADS 

Google Scholar 
65.Garcia-Garcera, M. et al. Fragmentation of contaminant and endogenous DNA in ancient samples determined by shotgun sequencing; prospects for human palaeogenomics. PLoS ONE 6, e24161 (2011).CAS 
PubMed 
PubMed Central 
Article 
ADS 

Google Scholar 
66.Llamas, B. et al. From the field to the laboratory: Controlling DNA contamination in human ancient DNA research in the high-throughput sequencing era. STAR 3, 1–14 (2017).Article 

Google Scholar 
67.Wintertona, S. L., Brian, M. W. & Evert, I. S. Phylogeny and Bayesian divergence time estimations of small-headed Xies (Diptera: Acroceridae) using multiple molecular markers. Mol. Phylogenet. Evol. 43, 808–832 (2007).Article 
CAS 

Google Scholar 
68.Gillung, J. P. & Wintertona, S. L. Evolution of fossil and living spider flies based onmorphological and molecular data (Diptera, Acroceridae). Syst. Entomol. 44, 820–841 (2019).Article 

Google Scholar 
69.Klasson, L. et al. The mosaic genome structure of the Wolbachia wRi strain infecting Drosophila simulans. Proc. Natl. Acad. Sci. U. S. A. 106, 5725–5730 (2009).CAS 
PubMed 
PubMed Central 
Article 
ADS 

Google Scholar 
70.Daley, T. & Smith, A. D. Predicting the molecular complexity of sequencing libraries. Nat. Methods 10, 325–327 (2013).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar  More

Spotted wing drosophila captures within the United StatesComparison between the raspberry field and wooded area, during pre-harvest and harvest periods to account for presence of developing and fully ripened fruit, SWD captures and selectivity per QB dry sticky trap is found in Fig. 1A,B. No difference was found in average capture per trap between either area during the pre-harvest period, nor was there a difference between these and the field during the harvest period. The wooded area during the harvest period captured the greatest amount of SWD/trap (F1,209 = 7.335, P = 0.007) (Fig. 1A). Dry sticky traps baited with QB had a significantly higher selectivity during the pre-harvest period in the raspberry field than in the wooded area but was not significantly different from the trap selectivity in the wooded area during the harvest period. The pre-harvest wooded area trap selectivity was not different from the harvest field trap selectivity. While the harvest field trap selectivity was lower than that of the wooded area trap selectivity during the same period (F1,203 = 23.6, P  More

1.Root, T. L. et al. Nature 421, 57–60 (2003).CAS 
Article 

Google Scholar 
2.Morelli, T. L. et al. Front. Ecol. Environ. 18, 228–234 (2020).Article 

Google Scholar 
3.Armstrong, J. B. et al. Nat. Clim. Change https://doi.org/10.1038/s41558-021-00994-y (2021).4.Northcote, T. G. N. Am. J. Fish. Manage. 17, 1029–1045 (1997).Article 

Google Scholar 
5.Xu, C., Letcher, B. H. & Nislow, K. H. Freshwater Biol. 55, 2253–2264 (2010).Article 

Google Scholar 
6.Schlosser, I. J. BioScience 41, 704–712 (1991).Article 

Google Scholar 
7.Al-Chokhachy, R., Alder, J., Hostetler, S., Gresswell, R. & Shepard, B. Glob. Change Biol. 19, 3069–3081 (2013).Article 

Google Scholar 
8.Fausch, K. D., Torgersen, C. E., Baxter, C. V. & Li, H. W. BioScience 52, 483–498 (2002).Article 

Google Scholar 
9.Muhlfeld, C. C. et al. Science 360, 866–867 (2018).CAS 

Google Scholar 
10.Kovach, R. P. et al. Rev. Fish Biol. Fisher. 26, 135–151 (2016).Article 

Google Scholar 
11.Isaak, D. J., Young, M. K., Nagel, D. E., Horan, D. L. & Groce, M. C. Glob. Change Biol. 21, 2540–2553 (2015).Article 

Google Scholar 
12.Isaak, D. J. et al. Water Resour. Res. 53, 9181–9205 (2017).Article 

Google Scholar 
13.Small-Lorenz, S. L., Culp, L. A., Ryder, T. B., Will, T. C. & Marra, P. P. Nat. Clim. Change 3, 91–93 (2013).Article 

Google Scholar 
14.Rieman, B. E. & Dunham, J. B. Ecol. Freshw. Fish 9, 51–64 (2000).Article 

Google Scholar 
15.Guzzo, M. M., Blanchfield, P. J. & Rennie, M. D. Proc. Natl Acad. Sci. USA 114, 9912–9917 (2017).CAS 
Article 

Google Scholar 
16.Brennan, S. R. et al. Science 364, 783–786 (2019).CAS 
Article 

Google Scholar 
17.Hauer, F. R. et al. Sci. Adv. 2, e1600026 (2016).Article 

Google Scholar  More