Philippa Kaur

1.Vaganov, E. A. et al. Influence of snowfall and melt timing on tree growth in subarctic Eurasia. Nature 400(6740), 149–151. https://doi.org/10.1038/22087 (1999).ADS 
CAS 
Article 

Google Scholar 
2.Impacts of a Warming Arctic—Arctic Climate Impact Assessment (ACIA). 144 (Cambridge University Press, 2004).3.Apps, M. J., Shvidenko, A. Z. & Vaganov, E. A. Boreal forests and the environment: A mitigation and adaptation strategies for global change. BFE. 11(1), 1–4 (2006).
Google Scholar 
4.Fedotov, A. P. et al. A reconstruction of the thawing of the permafrost during the last 170 years on the Taimyr Peninsula (East Siberia, Russia). Glob. Planet. Change 98–99, 139–152 (2002).
Google Scholar 
5.Kharuk, V. I., Dvinskaya, M. L. & Ranson, J. Fire return intervals within the northern boundary of the larch forest in Central Siberia. Int. J. Wildland Fire 22(2), 207–211. https://doi.org/10.1071/WF11181 (2011).Article 

Google Scholar 
6.Knorre, A. A., Kirdyanov, A. V., Prokushkin, A. S., Krusic, P. J. & Büntgen, U. Tree ring-based reconstruction of the long-term influence of wildfires on permafrost active layer dynamics in Central Siberia. Sci. Total Environ. 652, 314–319 (2019).ADS 
Article 

Google Scholar 
7.Kim, J.-S., Kug, J.-S., Jeong, S.-J., Park, H. & Schaepman-Strub, G. Extensive fires in southeastern Siberian permafrost linked to preceding Arctic Oscillation. Sci. Adv. 6(2), eaax330. https://doi.org/10.1126/sciadv.aax3308 (2020).Article 

Google Scholar 
8.Kirdyanov, A. V. et al. Long-term ecological consequences of forest fires in the permafrost zone of Siberia. Environ. Res. Lett. 15, 034061. https://doi.org/10.1088/1748-9326/ab7469 (2020).ADS 
Article 

Google Scholar 
9.Fritts, H. C. Tree-Rings and Climate 567 (Academic Press, 1976).
Google Scholar 
10.Schweingruber, F. H. Tree Rings and Environment Dendroecology (Paul Haupt Publ, 1996).
Google Scholar 
11.Hughes, M. K., Vaganov, E. A., Shiyatov, S. G., Touchan, R. & Funkhouser, G. Twentieth-century summer warmth in northern Yakutia in a 600-year context. Holocene 9(5), 603–608 (1999).Article 

Google Scholar 
12.Briffa, K. R. Annual climate variability in the Holocene: Interpreting the message of ancient trees. Quat. Sci. Rev. 19, 87–105 (2000).ADS 
Article 

Google Scholar 
13.Naurzbaev, M., Vaganov, E. A., Sidorova, O. V. & Schweingruber, F. H. Summer temperatures in eastern Taimyr inferred from a 2427-year late-Holocene tree-ring chronology and earlier floating series. Holocene 12(6), 727–736 (2002).ADS 
Article 

Google Scholar 
14.Grudd, H. Torneträsk tree-ring width and density AD 500–2004: A test of climatic sensitivity and a new 1500-year reconstruction of north Fennoscandian summers. Clim. Dyn. 31, 843–857 (2008).Article 

Google Scholar 
15.Sidorova, O. V., Siegwolf, R., Saurer, M., Naurzbaev, M. M. & Vaganov, E. A. Isotopic composition (δ13C, δ18O) in Siberian tree-ring chronology. Geophys. Res. Biogeosci. 113, G02019. https://doi.org/10.1029/2007JG000473 (2008).CAS 
Article 

Google Scholar 
16.Sidorova, O. V. et al. Spatial patterns of climatic changes in the Eurasian north reflected in Siberian larch tree-ring parameters and stable isotopes. Glob. Change Biol. 16, 1003–1018. https://doi.org/10.1111/j.1365-2486.2009.02008.x (2010).ADS 
Article 

Google Scholar 
17.Sidorova, O. V. et al. Is the 20th century warming unprecedented in the Siberian north?. Quat. Sci. Rev. 73, 93–102. https://doi.org/10.1016/j.quascirev.2013.05.015 (2013).ADS 
Article 

Google Scholar 
18.Kirdyanov, A. V., Treydte, K. S., Nikolaev, A., Helle, G. & Schleser, G. H. Climate signals in tree-ring width, density an δ13C from larches in Eastern Siberia (Russia). Chem. Geol. 252, 31–41. https://doi.org/10.1016/j.chemgeo.2008.01.023 (2008).ADS 
CAS 
Article 

Google Scholar 
19.Hilasvuori, E., Berninger, F., Sonninen, E., Tuomenvirta, H. & Jungner, H. Stability of climate signal in carbon and oxygen isotope records and ring width from Scots pine (Pinus sylvestris L.) in Finland. J. Quat. Sci. 24(5), 469–480 (2009).Article 

Google Scholar 
20.Loader, N. J., Young, G. H. F., Grudd, H. & McCarroll, D. Stable carbon isotopes from Torneträsk, norther Sweden provide a millennial length reconstruction of summer sunshine and its relationship to Arctic circulation. Quat. Sci. Rev. 62, 97–113 (2013).ADS 
Article 

Google Scholar 
21.Churakova (Sidorova), O. V. et al. Recent atmospheric drying in Siberia is not unprecedented over the last 1500 years. Sci. Rep. 10, 15024 (2020).CAS 
Article 

Google Scholar 
22.Young, G. H. F. et al. Changes in atmospheric circulation and the Arctic Oscillation preserved within a millennial length reconstruction of summer cloud cover from northern Fennoscandia. Clim. Dyn. 39, 495–507. https://doi.org/10.1007/s00382-011-1246-3 (2012).Article 

Google Scholar 
23.Saurer, M., Schweingruber, F., Vaganov, E. A., Shiyatov, S. G. & Siegwolf, R. Spatial and temporal oxygen isotope trends at the northern tree-line in Eurasia. Geophys. Res. Lett. https://doi.org/10.1029/2001GL013739 (2002).Article 

Google Scholar 
24.Saurer, M. et al. Influence of atmospheric circulation patterns on the oxygen isotope ratio of tree rings in the Alpine region. J. Geophys. Res. 117, D05118. https://doi.org/10.1029/2011JD016861 (2012).ADS 
CAS 
Article 

Google Scholar 
25.Ortega, P. et al. A model-tested North Atlantic Oscillation reconstruction for the past millennium. Nature 523, 71–74 (2015).ADS 
CAS 
Article 

Google Scholar 
26.Butzin, M. et al. Variations of oxygen-18 in West Siberian precipitation during the last 50 years. Atmos. Chem. Phys. 14, 5853–5869 (2014).ADS 
Article 

Google Scholar 
27.Gagen, M. et al. North Atlantic summer storm tracks over Europe dominated by internal variability over the past millennium. Nat. Geosci. 9(8), 630–635 (2016).ADS 
CAS 
Article 

Google Scholar 
28.Thompson, D. W. & Wallace, J. M. Annular modes in the extratropical circulation. Part I: Month-to-month variability. J. Clim. 13, 1000–1016 (2000).ADS 
Article 

Google Scholar 
29.Wang, J. et al. Impacts of the Siberian High and Arctic Oscillation on the East Asia winter monsoon: Driving down welling in the western Bering Sea Aquatic Ecosystem. Health Manag. 15(1), 20–30. https://doi.org/10.1080/14634988.2012.648860 (2012).Article 

Google Scholar 
30.Buermann, W. et al. Interannual covariability in Northern Hemisphere air temperatures and greenness associated with El-Nino-Southern Oscillation and the Arctic Oscillation. J. Geophys. Res. 108(D13), 4396. https://doi.org/10.1029/2002JD002630 (2003).Article 

Google Scholar 
31.Baltzer, H. et al. Impact of the Arctic Oscillation pattern on interannual forest fire variability in Central Siberia. Geophys. Res. Lett. https://doi.org/10.1029/2005GL022526 (2005).Article 

Google Scholar 
32.Zhang, J. et al. Analysis of the positive Arctic Oscillation index event and its influence in the winter and spring of 2019/2020. Front. Earth Sci. https://doi.org/10.3389/feart.2020.580601 (2021).Article 

Google Scholar 
33.Zielinski, G. A. Use of paleo-records in determining variability within the volcanism- climate system. Quat. Sci. Rev. 19, 417–438 (2000).ADS 
Article 

Google Scholar 
34.Panuyshkina, I. P. & Arbatskaya, M. K. Dendrochronological approach to study flammability of forests in Evenkia (Siberia). Sib. Ecol. J. 2, 167–173 (1999).
Google Scholar 
35.Valendik, E. N., Kisilyakhov, E. K., Rizova, V. A., Ponamarev, E. I. & Danilova, I. V. Large fires in taiga landscape of Central Siberia. Geogr. Nat. Resour. 14(1), 52–59 (2014).
Google Scholar 
36.Naulier, M. et al. A millennial summer temperature reconstruction for northeastern Canada using oxygen isotopes in subfossil trees. Clim. Past. 11, 1153–1164. https://doi.org/10.5194/cp-11-1153-2015 (2015).Article 

Google Scholar 
37.Churakova (Sidorova), O. V. et al. Siberian tree-ring and stable isotope proxies as indicators of temperature and moisture changes after major stratospheric volcanic eruptions. Clim. Past. https://doi.org/10.5194/cp-2018-70.y (2019).Article 

Google Scholar 
38.Furyaev, V. V., Vaganov, E. A., Tchebakova, N. M. & Valendik, E. N. Effects of fire and climate on successions and structural changes in the Siberian boreal forest. Eurasian J. For. Res. 2, 1–15 (2001).
Google Scholar 
39.Keller, K. M. et al. 20th-century changes in carbon isotopes and water-use efficiency: Tree-ring based evaluation of the CLM4.5 and LPX-Bern models. Biogeosciences 14, 2641–2673 (2017).ADS 
CAS 
Article 

Google Scholar 
40.D’Arrigo, R. D., Cook, E. R., Mann, M. E. & Jacoby, G. C. Tree-ring reconstructions of temperature and sea-level pressure variability associated with the warm-season Arctic Oscillation since AD 1650. Geophys. Res. Lett. 30(11), 1549. https://doi.org/10.1029/2003GL017250 (2003).ADS 
Article 

Google Scholar 
41.Kress, A. et al. Swiss tree rings reveal warm and wet summers during medieval times. Geophys. Res. Lett. 41, 1732–1737. https://doi.org/10.1002/2013GL059081 (2014).ADS 
Article 

Google Scholar 
42.Büntgen, U. et al. Recent European drought extremes beyond Common Era background variability. Nat. Geosci. 14, 190–196. https://doi.org/10.1038/s41561-021-00698-0 (2021).ADS 
CAS 
Article 

Google Scholar 
43.Kodera, K. & Kuroda, Y. Regional and hemispheric circulation patterns in the northern hemisphere winter, or the NAO and AO. Geophys. Res. Lett. 30(18), 2003. https://doi.org/10.1029/2003GL017290 (1934).Article 

Google Scholar 
44.Abaimov, A. P., Bondarev, A. I., Ziryanova, O. A. & Shitova, S. A. Forest Krasnoyarsk Polar (Nauka, 1997).
Google Scholar 
45.Ary-Mas Natural Conditions, Flora and Vegetation. (eds. Norin, B.N.) (Nauka, Leningrad, 1978).46.Ogi, M., Yamazaki, K. & Tachibana, Y. The summertime annular mode in the Northern Hemisphere and its linkage to the winter mode. J. Geophys. Res. 109, D20114 (2004).ADS 
Article 

Google Scholar 
47.Gagen, M. H., McCarroll, D., Loader, N. J., Robertson, I. & Jalkanen, R. Exorcising the ‘segment length curse’ summer temperature reconstruction since AD 1640 using non de-trend stable carbon isotope ratios from line trees in northern Finland. Holocene 17, 433–444 (2007).ADS 
Article 

Google Scholar 
48.Boettger, T. et al. Wood cellulose preparation methods and mass spectrometric analyses of δ13C, δ18O, and nonexchangeable δ2H values in cellulose, sugar, and starch: An inter-laboratory comparison. Anal. Chem. 15, 4603–4612 (2007).Article 

Google Scholar 
49.Weigt, R. B. et al. Comparison of δ18O and δ13C values between tree-ring whole wood and cellulose in five species growing under two different site conditions. Rapid Commun. Mass Spectrom. 29(29), 2233–2244. https://doi.org/10.1002/rcm.7388 (2015).ADS 
CAS 
Article 
PubMed 

Google Scholar 
50.Francey, R. J. et al. A 1000-year high precision record of δ13C in atmospheric CO2. Tellus B51, 170–193 (1999).ADS 
Article 

Google Scholar  More

1.Pugh, P. The vertical distribution of the siphonophores collected during the SOND cruise, 1965. J. Mar. Biol. Assoc. U.K. 54, 25–90 (1974).Article 

Google Scholar 
2.Grossmann, M. M., Collins, A. G. & Lindsay, D. J. Description of the eudoxid stages of Lensia havock and Lensia leloupi (Cnidaria: Siphonophora: Calycophorae), with a review of all known Lensia eudoxid bracts. Syst. Biodivers. 12, 163–180 (2014).Article 

Google Scholar 
3.Dunn, C. W. & Wagner, G. P. The evolution of colony-level development in the Siphonophora (Cnidaria: Hydrozoa). Dev. Genes. Evol. 216, 743–754 (2006).PubMed 
Article 
PubMed Central 

Google Scholar 
4.Karas, M. & Hillenkamp, F. Laser desorption ionization of proteins with molecular masses exceeding 10,000 daltons. Anal. Chem. 60, 2299–2301 (1988).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
5.Clark, A. E., Kaleta, E. J., Arora, A. & Wolk, D. M. Matrix-assisted laser desorption ionization–time of flight mass spectrometry: a fundamental shift in the routine practice of clinical microbiology. Clin. Microbiol. Rev. 26, 547–603 (2013).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
6.Dvorak, V. et al. Identification of phlebotomine sand flies (Diptera: Psychodidae) by matrix-assisted laser desorption/ionization time of flight mass spectrometry. Parasit. Vectors 7, 1–7 (2014).Article 

Google Scholar 
7.Rossel, S. & Arbizu, P. M. Revealing higher than expected diversity of Harpacticoida (Crustacea: Copepoda) in the North Sea using MALDI-TOF MS and molecular barcoding. Sci. Rep. 9, 1–14 (2019).CAS 
Article 

Google Scholar 
8.Feltens, R., Görner, R., Kalkhof, S., Gröger-Arndt, H. & von Bergen, M. Discrimination of different species from the genus Drosophila by intact protein profiling using matrix-assisted laser desorption ionization mass spectrometry. BMC Evol. Biol. 10, 1–15 (2010).Article 
CAS 

Google Scholar 
9.Mazzeo, M. F. et al. Fish authentication by MALDI-TOF mass spectrometry. J. Agric. Food Chem. 56, 11071–11076 (2008).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
10.Kaufmann, C., Schaffner, F., Ziegler, D., Pflueger, V. & Mathis, A. Identification of field-caught Culicoides biting midges using matrix-assisted laser desorption/ionization time of flight mass spectrometry. Parasitology 139, 248–258 (2012).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
11.Laakmann, S. et al. Comparison of molecular species identification for North Sea calanoid copepods (Crustacea) using proteome fingerprints and DNA sequences. Mol. Ecol. Resour. 13, 862–876 (2013).CAS 
PubMed 
Article 

Google Scholar 
12.Lassen, S., Wiebring, A., Helmholz, H., Ruhnau, C. & Prange, A. Isolation of a Nav channel blocking polypeptide from Cyanea capillata medusae–a neurotoxin contained in fishing tentacle isorhizas. Toxicon 59, 610–616 (2012).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
13.Lazcano-Pérez, F., Arellano, R. O., Garay, E., Arreguín-Espinosa, R. & Sánchez-Rodríguez, J. Electrophysiological activity of a neurotoxic fraction from the venom of box jellyfish Carybdea marsupialis. Comp. Biochem. Physiol. C: Toxicol. Pharmacol. 191, 177–182 (2017).
Google Scholar 
14.Helmholz, H., Naatz, S., Lassen, S. & Prange, A. Isolation of a cytotoxic glycoprotein from the Scyphozoa Cyanea lamarckii by lectin-affinity chromatography and characterization of molecule interactions by surface plasmon resonance. J. Chromatogr. B 871, 60–66 (2008).CAS 
Article 

Google Scholar 
15.Suzuki, R. & Shimodaira, H. Pvclust: an R package for assessing the uncertainty in hierarchical clustering. Bioinformatics 22, 1540–1542 (2006).CAS 
PubMed 
Article 
PubMed Central 

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

Google Scholar 
17.Park, N. & Lee, W. Four new records of family Diphyidae (Hydrozoa: Siphonophorae) in Korean waters. J. Spec. Res. 9, 131–146 (2020).
Google Scholar 
18.Karger, A., Bettin, B., Gethmann, J. M. & Klaus, C. Whole animal matrix-assisted laser desorption/ionization time-of-flight (MALDI-TOF) mass spectrometry of ticks–are spectra of Ixodes ricinus nymphs influenced by environmental, spatial, and temporal factors?. PLoS ONE 14, e0210590 (2019).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
19.Tayri-Wilk, T. et al. Mass spectrometry reveals the chemistry of formaldehyde cross-linking in structured proteins. Nat. Commun. 11, 1–9 (2020).Article 
CAS 

Google Scholar 
20.Rossel, S. & Martínez Arbizu, P. Effects of sample fixation on specimen identification in biodiversity assemblies based on proteomic data (MALDI-TOF). Fron. Mar. Sci. 5, 1–13 (2018).Article 

Google Scholar 
21.Zheng, L., He, J., Lin, Y., Cao, W. & Zhang, W. 16S rRNA is a better choice than COI for DNA barcoding hydrozoans in the coastal waters of China. Acta Oceanol. Sin. 33, 55–76 (2014).Article 
CAS 

Google Scholar 
22.Yeom, J., Park, N., Jeong, R. & Lee, W. Integrative description of cryptic Tigriopus species from Korea using MALDI-TOF MS and DNA barcoding. Front. Mar. Sci. 8, 495 (2021).Article 

Google Scholar 
23.Peter, S. Molecular characterization and phylogenetic analysis of Diphyes dispar (Siphonophora: Diphyidae) from the Laccadive Sea, off the south-west coast of Arabian Sea, Indian Ocean. Int. J. Fish Aquat. Stud. 4, 30–35 (2016).
Google Scholar 
24.Dunn, C. W., Pugh, P. R. & Haddock, S. H. Molecular phylogenetics of the Siphonophora (Cnidaria), with implications for the evolution of functional specialization. Syst. Biol. 54, 916–935 (2005).PubMed 
Article 
PubMed Central 

Google Scholar 
25.Munro, C. et al. Improved phylogenetic resolution within Siphonophora (Cnidaria) with implications for trait evolution. Mol. Phylogenet. Evol. 127, 823–833 (2018).PubMed 
PubMed Central 
Article 

Google Scholar 
26.Totton, A. K. Siphonophora of the Indian Ocean together with systematic and biological notes on related specimens from other oceans. Disc Rep 27, 1–162 (1954).
Google Scholar 
27.Totton, A. K., Bargmann, H. E. & British Museum (Natural History). A synopsis of the Siphonophora. (British Museum (Natural History), 1965).28.Mapstone, G. M. Siphonophora (Cnidaria, Hydrozoa) of Canadian Pacific waters. (NRC Research Press, 2009).29.Nishiyama, E. Y., Araujo, E. M. & Oliveira, O. M. Species of Lensia (Cnidaria: Hydrozoa: Siphonophorae) from southeastern Brazilian waters. Zoologia (Curitiba) 33, 2337 (2016).Article 

Google Scholar 
30.MALDIquantForeign: Import/Export routines for MALDIquant (2015).31.Gibb, S. & Strimmer, K. MALDIquant: a versatile R package for the analysis of mass spectrometry data. Bioinformatics 28, 2270–2271 (2012).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
32.Palarea-Albaladejo, J., Mclean, K., Wright, F. & Smith, D. G. MALDIrppa: quality control and robust analysis for mass spectrometry data. Bioinformatics 34, 522–523 (2018).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
33.Gibb, S. & Strimmer, K. Species Identification using MALDIquant manual. http://www.strimmerlab.org/software/maldiquant/. (2015).34.Ahdesmäki, M. & Strimmer, K. Feature selection in omics prediction problems using cat scores and false nondiscovery rate control. Ann. Appl. Stat. 4, 503–519 (2010).MathSciNet 
MATH 
Article 

Google Scholar 
35.vegan: Community Ecology Package. R package version 2.5-2. 2018 (2018).36.Geller, J., Meyer, C., Parker, M. & Hawk, H. Redesign of PCR primers for mitochondrial cytochrome c oxidase subunit I for marine invertebrates and application in all-taxa biotic surveys. Mol. Ecol. Resour. 13, 851–861 (2013).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
37.Schuchert, P. DNA barcoding of some Pandeidae species (Cnidaria, Hydrozoa, Anthoathecata). Rev. Suisse Zool. 125, 101–127 (2018).
Google Scholar 
38.Medlin, L., Elwood, H. J., Stickel, S. & Sogin, M. L. The characterization of enzymatically amplified eukaryotic 16S-like rRNA-coding regions. Gene 71, 491–499 (1988).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
39.Collins, A. G. Towards understanding the phylogenetic history of Hydrozoa: hypothesis testing with 18S gene sequence data. Scientia Marina (2000).40.Strychar, K. B., Hamilton, L. C., Kenchington, E. L. & Scott, D. B. Cold-water Corals and Ecosystems 679–690 (Springer, Berlin, 2005).Book 

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

Google Scholar 
42.Thompson, J. D., Higgins, D. G. & Gibson, T. J. CLUSTAL W: improving the sensitivity of progressive multiple sequence alignment through sequence weighting, position-specific gap penalties and weight matrix choice. Nucleic Acids Res. 22, 4673–4680 (1994).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
43.Kumar, S., Stecher, G. & Tamura, K. MEGA7: molecular evolutionary genetics analysis version 7.0 for bigger datasets. Mol. Biol. Evol. 33, 1870–1874 (2016).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
44.Kimura, M. A simple method for estimating evolutionary rates of base substitutions through comparative studies of nucleotide sequences. J. Mol. Evol. 16, 111–120 (1980).ADS 
CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
45.Darriba, D., Taboada, G. L., Doallo, R. & Posada, D. jModelTest 2: more models, new heuristics and parallel computing. Nat. Methods 9, 772–772 (2012).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
46.Guindon, S. & Gascuel, O. A simple, fast and accurate method to estimate large phylogenies by maximum-likelihood. Syst. Biol. 52, 696–704 (2003).PubMed 
Article 
PubMed Central 

Google Scholar 
47.Akaike, H. A new look at the statistical model identification. IEEE Trans. Autom. Control 19, 716–723 (1974).ADS 
MathSciNet 
MATH 
Article 

Google Scholar 
48.Yamaoka, K., Nakagawa, T. & Uno, T. Application of Akaike’s information criterion (AIC) in the evaluation of linear pharmacokinetic equations. J. Pharmacokinet. Biopharm. 6, 165–175 (1978).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
49.Hurvich, C. M. & Tsai, C.-L. Regression and time series model selection in small samples. Biometrika 76, 297–307 (1989).MathSciNet 
MATH 
Article 

Google Scholar 
50.Simmons, M. P. & Ochoterena, H. Gaps as characters in sequence-based phylogenetic analyses. Syst. Biol. 49, 369–381 (2000).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
51.Young, N. D. & Healy, J. GapCoder automates the use of indel characters in phylogenetic analysis. BMC Bioinform. 4, 6 (2003).Article 

Google Scholar 
52.Swofford, D. L. & Sullivan, J. Phylogeny inference based on parsimony and other methods using PAUP*. Phylogenet. Handb. Pract. Approach DNA and Protein Phylogeny 7, 160–206 (2003).
Google Scholar 
53.Trifinopoulos, J., Nguyen, L.-T., von Haeseler, A. & Minh, B. Q. W-IQ-TREE: a fast online phylogenetic tool for maximum likelihood analysis. Nucleic Acids Res. 44, W232–W235 (2016).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
54.Felsenstein, J. Confidence limits on phylogenies: an approach using the bootstrap. Evolution 39, 783–791 (1985).PubMed 
PubMed Central 
Article 

Google Scholar 
55.Huelsenbeck, J. P. & Ronquist, F. MRBAYES: Bayesian inference of phylogenetic trees. Bioinformatics 17, 754–755 (2001).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
56.Ronquist, F. & Huelsenbeck, J. P. MrBayes 3: Bayesian phylogenetic inference under mixed models. Bioinformatics 19, 1572–1574 (2003).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
57.Ronquist, F. et al. MrBayes 3.2: efficient Bayesian phylogenetic inference and model choice across a large model space. Syst. Biol. 61, 539–542 (2012).PubMed 
PubMed Central 
Article 

Google Scholar  More

1.Pocock, M. J., Tweddle, J. C., Savage, J., Robinson, L. D. & Roy, H. E. The diversity and evolution of ecological and environmental citizen science. PLoS ONE 12, e0172579 (2017).PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
2.Chandler, M. et al. Contribution of citizen science towards international biodiversity monitoring. Biol. Cons. 213, 280–294 (2017).Article 

Google Scholar 
3.Chandler, M. et al. Involving citizen scientists in biodiversity observation. In The GEO Handbook on Biodiversity Observation Networks 211–237 (Springer, 2017).4.McKinley, D. C. et al. Citizen science can improve conservation science, natural resource management, and environmental protection. Biol. Cons. 208, 15–28 (2017).Article 

Google Scholar 
5.Pereira, H. M. et al. Monitoring essential biodiversity variables at the species level. In The GEO Handbook on Biodiversity Observation Networks 79–105 (Springer, 2017).6.Wiggins, A. & Crowston, K. From conservation to crowdsourcing: A typology of citizen science. in 2011 44th Hawaii International Conference on System Sciences 1–10 (IEEE, 2011).7.Haklay, M. Citizen science and volunteered geographic information: Overview and typology of participation. In Crowdsourcing Geographic Knowledge 105–122 (Springer, 2013).8.Kelling, S. et al. Using semistructured surveys to improve citizen science data for monitoring biodiversity. Bioscience 69, 170–179 (2019).PubMed 
PubMed Central 
Article 

Google Scholar 
9.Welvaert, M. & Caley, P. Citizen surveillance for environmental monitoring: Combining the efforts of citizen science and crowdsourcing in a quantitative data framework. Springerplus 5, 1890 (2016).PubMed 
PubMed Central 
Article 

Google Scholar 
10.Callaghan, C. T., Rowley, J. J., Cornwell, W. K., Poore, A. G. & Major, R. E. Improving big citizen science data: Moving beyond haphazard sampling. PLoS Biol. 17, e3000357 (2019).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
11.Bonter, D. N. & Cooper, C. B. Data validation in citizen science: A case study from project FeederWatch. Front. Ecol. Environ. 10, 305–307 (2012).Article 

Google Scholar 
12.Kosmala, M., Wiggins, A., Swanson, A. & Simmons, B. Assessing data quality in citizen science. Front. Ecol. Environ. 14, 551–560 (2016).Article 

Google Scholar 
13.Burgess, H. K. et al. The science of citizen science: Exploring barriers to use as a primary research tool. Biol. Cons. 208, 113–120 (2017).Article 

Google Scholar 
14.Courter, J. R., Johnson, R. J., Stuyck, C. M., Lang, B. A. & Kaiser, E. W. Weekend bias in citizen science data reporting: Implications for phenology studies. Int. J. Biometeorol. 57, 715–720 (2013).ADS 
PubMed 
Article 
PubMed Central 

Google Scholar 
15.Sullivan, B. L. et al. The eBird enterprise: An integrated approach to development and application of citizen science. Biol. Cons. 169, 31–40 (2014).Article 

Google Scholar 
16.Kelling, S. et al. Can observation skills of citizen scientists be estimated using species accumulation curves?. PLoS ONE 10, e0139600 (2015).PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
17.Tiago, P., Ceia-Hasse, A., Marques, T. A., Capinha, C. & Pereira, H. M. Spatial distribution of citizen science casuistic observations for different taxonomic groups. Sci. Rep. 7, 1–9 (2017).CAS 
Article 

Google Scholar 
18.Geldmann, J. et al. What determines spatial bias in citizen science? Exploring four recording schemes with different proficiency requirements. Divers. Distrib. 22, 1139–1149 (2016).Article 

Google Scholar 
19.Callaghan, C. T. et al. Three frontiers for the future of biodiversity research using citizen science data. Bioscience 71, 55–63 (2021).
Google Scholar 
20.Ward, D. F. Understanding sampling and taxonomic biases recorded by citizen scientists. J. Insect Conserv. 18, 753–756 (2014).Article 

Google Scholar 
21.Troudet, J., Grandcolas, P., Blin, A., Vignes-Lebbe, R. & Legendre, F. Taxonomic bias in biodiversity data and societal preferences. Sci. Rep. 7, 1–14 (2017).CAS 
Article 

Google Scholar 
22.Martı́n-López, B., Montes, C., Ramı́rez, L. & Benayas, J. What drives policy decision-making related to species conservation? Biol. Conserv. 142, 1370–1380 (2009).23.Boakes, E. H. et al. Distorted views of biodiversity: Spatial and temporal bias in species occurrence data. PLoS Biol 8, e1000385 (2010).PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
24.Aceves-Bueno, E. et al. The accuracy of citizen science data: A quantitative review. Bull. Ecol. Soc. Am. 98, 278–290 (2017).Article 

Google Scholar 
25.Davies, T. K., Stevens, G., Meekan, M. G., Struve, J. & Rowcliffe, J. M. Can citizen science monitor whale-shark aggregations? Investigating bias in mark–recapture modelling using identification photographs sourced from the public. Wildl. Res. 39, 696–704 (2013).Article 

Google Scholar 
26.Crall, A. W. et al. Assessing citizen science data quality: An invasive species case study. Conserv. Lett. 4, 433–442 (2011).Article 

Google Scholar 
27.van Strien, A. J., van Swaay, C. A. & Termaat, T. Opportunistic citizen science data of animal species produce reliable estimates of distribution trends if analysed with occupancy models. J. Appl. Ecol. 50, 1450–1458 (2013).Article 

Google Scholar 
28.Johnston, A., Moran, N., Musgrove, A., Fink, D. & Baillie, S. R. Estimating species distributions from spatially biased citizen science data. Ecol. Model. 422, 108927 (2020).Article 

Google Scholar 
29.Tiago, P., Pereira, H. M. & Capinha, C. Using citizen science data to estimate climatic niches and species distributions. Basic Appl. Ecol. 20, 75–85 (2017).Article 

Google Scholar 
30.Sullivan, B. L. et al. Using open access observational data for conservation action: A case study for birds. Biol. Cons. 208, 5–14 (2017).Article 

Google Scholar 
31.Callaghan, C. T. et al. Citizen science data accurately predicts expert-derived species richness at a continental scale when sampling thresholds are met. Biodivers. Conserv. 29, 1323–1337 (2020).Article 

Google Scholar 
32.Birkin, L. & Goulson, D. Using citizen science to monitor pollination services. Ecol. Entomol. 40, 3–11 (2015).Article 

Google Scholar 
33.Delaney, D. G., Sperling, C. D., Adams, C. S. & Leung, B. Marine invasive species: Validation of citizen science and implications for national monitoring networks. Biol. Invasions 10, 117–128 (2008).Article 

Google Scholar 
34.Schultz, C. B., Brown, L. M., Pelton, E. & Crone, E. E. Citizen science monitoring demonstrates dramatic declines of monarch butterflies in western north america. Biol. Cons. 214, 343–346 (2017).Article 

Google Scholar 
35.Bird, T. J. et al. Statistical solutions for error and bias in global citizen science datasets. Biol. Cons. 173, 144–154 (2014).Article 

Google Scholar 
36.Isaac, N. J., van Strien, A. J., August, T. A., de Zeeuw, M. P. & Roy, D. B. Statistics for citizen science: Extracting signals of change from noisy ecological data. Methods Ecol. Evol. 5, 1052–1060 (2014).Article 

Google Scholar 
37.Dickinson, J. L. et al. The current state of citizen science as a tool for ecological research and public engagement. Front. Ecol. Environ. 10, 291–297 (2012).Article 

Google Scholar 
38.Bonney, R. et al. Next steps for citizen science. Science 343, 1436–1437 (2014).ADS 
PubMed 
Article 

Google Scholar 
39.Jordan, R. C., Gray, S. A., Howe, D. V., Brooks, W. R. & Ehrenfeld, J. G. Knowledge gain and behavioral change in citizen-science programs. Conserv. Biol. 25, 1148–1154 (2011).PubMed 
Article 

Google Scholar 
40.Crall, A. W. et al. The impacts of an invasive species citizen science training program on participant attitudes, behavior, and science literacy. Public Underst. Sci. 22, 745–764 (2013).PubMed 
Article 
PubMed Central 

Google Scholar 
41.Jordan, R. C., Ballard, H. L. & Phillips, T. B. Key issues and new approaches for evaluating citizen-science learning outcomes. Front. Ecol. Environ. 10, 307–309 (2012).Article 

Google Scholar 
42.Evans, C. et al. The neighborhood nestwatch program: Participant outcomes of a citizen-science ecological research project. Conserv. Biol. 19, 589–594 (2005).Article 

Google Scholar 
43.Haywood, B. K., Parrish, J. K. & Dolliver, J. Place-based and data-rich citizen science as a precursor for conservation action. Conserv. Biol. 30, 476–486 (2016).PubMed 
Article 
PubMed Central 

Google Scholar 
44.Pocock, M. J. et al. A vision for global biodiversity monitoring with citizen science. In Advances in Ecological Research vol. 59, 169–223 (Elsevier, 2018).45.Tiago, P., Gouveia, M. J., Capinha, C., Santos-Reis, M. & Pereira, H. M. The influence of motivational factors on the frequency of participation in citizen science activities. Nat. Conserv. 18, 61 (2017).Article 

Google Scholar 
46.Isaac, N. J. & Pocock, M. J. Bias and information in biological records. Biol. J. Lin. Soc. 115, 522–531 (2015).Article 

Google Scholar 
47.Angulo, E. & Courchamp, F. Rare species are valued big time. PLoS ONE 4, e5215 (2009).ADS 
PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
48.Booth, J. E., Gaston, K. J., Evans, K. L. & Armsworth, P. R. The value of species rarity in biodiversity recreation: A birdwatching example. Biol. Cons. 144, 2728–2732 (2011).Article 

Google Scholar 
49.Rowley, J. J. et al. FrogID: Citizen scientists provide validated biodiversity data on frogs of australia. Herpetol. Conserv. Biol. 14, 155–170 (2019).
Google Scholar 
50.Boakes, E. H. et al. Patterns of contribution to citizen science biodiversity projects increase understanding of volunteers recording behaviour. Sci. Rep. 6, 33051 (2016).ADS 
CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
51.Garrard, G. E., McCarthy, M. A., Williams, N. S., Bekessy, S. A. & Wintle, B. A. A general model of detectability using species traits. Methods Ecol. Evol. 4, 45–52 (2013).Article 

Google Scholar 
52.Denis, T. et al. Biological traits, rather than environment, shape detection curves of large vertebrates in neotropical rainforests. Ecol. Appl. 27, 1564–1577 (2017).PubMed 
Article 
PubMed Central 

Google Scholar 
53.Sólymos, P., Matsuoka, S. M., Stralberg, D., Barker, N. K. & Bayne, E. M. Phylogeny and species traits predict bird detectability. Ecography 41, 1595–1603 (2018).Article 

Google Scholar 
54.Wood, C., Sullivan, B., Iliff, M., Fink, D. & Kelling, S. eBird: Engaging birders in science and conservation. PLoS Biol 9, 1001220 (2011).Article 
CAS 

Google Scholar 
55.GBIF.org (3rd December 2019). GBIF occurrence download. https://doi.org/10.15468/dl.lpwczr56.Gilfedder, M. et al. Brokering trust in citizen science. Soc. Nat. Resour. 32, 292–302 (2019).Article 

Google Scholar 
57.Callaghan, C., Lyons, M., Martin, J., Major, R. & Kingsford, R. Assessing the reliability of avian biodiversity measures of urban greenspaces using eBird citizen science data. Avian Conserv. Ecol. 12, 66 (2017).
Google Scholar 
58.Johnston, A. et al. Best practices for making reliable inferences from citizen science data: Case study using eBird to estimate species distributions. BioRxiv 574392 (2019).59.Myhrvold, N. P. et al. An amniote life-history database to perform comparative analyses with birds, mammals, and reptiles: Ecological archives E096–269. Ecology 96, 3109–3109 (2015).Article 

Google Scholar 
60.Dale, J., Dey, C. J., Delhey, K., Kempenaers, B. & Valcu, M. The effects of life history and sexual selection on male and female plumage colouration. Nature 527, 367–370 (2015).ADS 
CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
61.R Core Team. R: A Language and Environment for Statistical Computing (R Foundation for Statistical Computing, 2020).62.Wickham, H. et al. Welcome to the tidyverse. J. Open Source Softw. 4, 1686 (2019).ADS 
Article 

Google Scholar 
63.Bates, D., Mächler, M., Bolker, B. & Walker, S. Fitting linear mixed-effects models using lme4. J. Stat. Softw. 67, 1–48 (2015).
Google Scholar 
64.Kuznetsova, A., Brockhoff, P. B. & Christensen, R. H. B. lmerTest package: Tests in linear mixed effects models. J. Stat. Softw. 82, 1–26 (2017).Article 

Google Scholar 
65.Johnston, A. et al. Species traits explain variation in detectability of UK birds. Bird Study 61, 340–350 (2014).Article 

Google Scholar 
66.Steen, V. A., Elphick, C. S. & Tingley, M. W. An evaluation of stringent filtering to improve species distribution models from citizen science data. Divers. Distrib. 25, 1857–1869 (2019).Article 

Google Scholar 
67.Henckel, L., Bradter, U., Jönsson, M., Isaac, N. J. & Snäll, T. Assessing the usefulness of citizen science data for habitat suitability modelling: Opportunistic reporting versus sampling based on a systematic protocol. Divers. Distrib. 26, 1276–1290 (2020).Article 

Google Scholar 
68.Caley, P., Welvaert, M. & Barry, S. C. Crowd surveillance: Estimating citizen science reporting probabilities for insects of biosecurity concern. J. Pest. Sci. 93, 543–550 (2020).Article 

Google Scholar 
69.Périquet, S., Roxburgh, L., le Roux, A. & Collinson, W. J. Testing the value of citizen science for roadkill studies: A case study from South Africa. Front. Ecol. Evol. 6, 15 (2018).Article 

Google Scholar 
70.Nakagawa, S. & Freckleton, R. P. Model averaging, missing data and multiple imputation: A case study for behavioural ecology. Behav. Ecol. Sociobiol. 65, 103–116 (2011).Article 

Google Scholar 
71.Schlossberg, S., Chase, M. & Griffin, C. Using species traits to predict detectability of animals on aerial surveys. Ecol. Appl. 28, 106–118 (2018).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
72.Aristeidou, M., Scanlon, E. & Sharples, M. Profiles of engagement in online communities of citizen science participation. Comput. Hum. Behav. 74, 246–256 (2017).Article 

Google Scholar 
73.Troscianko, J., Skelhorn, J. & Stevens, M. Quantifying camouflage: How to predict detectability from appearance. BMC Evol. Biol. 17, 1–13 (2017).Article 

Google Scholar 
74.Schuetz, J. G. & Johnston, A. Characterizing the cultural niches of North American birds. Proc. Natl. Acad. Sci. 22, 10868–10873 (2019).Article 
CAS 

Google Scholar 
75.Lišková, S. & Frynta, D. What determines bird beauty in human eyes?. Anthrozoös 26, 27–41 (2013).Article 

Google Scholar 
76.Tulloch, A. I., Possingham, H. P., Joseph, L. N., Szabo, J. & Martin, T. G. Realising the full potential of citizen science monitoring programs. Biol. Cons. 165, 128–138 (2013).Article 

Google Scholar 
77.Kobori, H. et al. Citizen science: A new approach to advance ecology, education, and conservation. Ecol. Res. 31, 1–19 (2016).CAS 
Article 

Google Scholar 
78.Callaghan, C. T., Poore, A. G., Major, R. E., Rowley, J. J. & Cornwell, W. K. Optimizing future biodiversity sampling by citizen scientists. Proc. R. Soc. B 286, 20191487 (2019).PubMed 
PubMed Central 
Article 

Google Scholar 
79.Pacifici, K. et al. Integrating multiple data sources in species distribution modeling: A framework for data fusion. Ecology 98, 840–850 (2017).PubMed 
Article 
PubMed Central 

Google Scholar 
80.Robinson, O. J. et al. Integrating citizen science data with expert surveys increases accuracy and spatial extent of species distribution models. Divers. Distrib. 26, 976–986 (2020).Article 

Google Scholar 
81.van Strien, A. J., Termaat, T., Groenendijk, D., Mensing, V. & Kery, M. Site-occupancy models may offer new opportunities for dragonfly monitoring based on daily species lists. Basic Appl. Ecol. 11, 495–503 (2010).Article 

Google Scholar 
82.Van der Wal, R. et al. Mapping species distributions: A comparison of skilled naturalist and lay citizen science recording. Ambio 44, 584–600 (2015).PubMed 
PubMed Central 
Article 

Google Scholar 
83.Dennis, E. B., Morgan, B. J., Brereton, T. M., Roy, D. B. & Fox, R. Using citizen science butterfly counts to predict species population trends. Conserv. Biol. 31, 1350–1361 (2017).PubMed 
Article 
PubMed Central 

Google Scholar 
84.Stoudt, S., Goldstein, B. R. & De Valpine, P. Identifying charismatic bird species and traits with community science data. bioRxiv. https://doi.org/10.1101/2021.06.05.446577 More

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24 September 2021

Coral conservation strikes a balance

Australia–Fiji collaboration matches community needs with reef protection.

Clare Watson

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Clare Watson

Clare Watson is a freelance writer in Wollongong, Australia.

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A spear fisherman catches reef fish, a cultural mainstay on Mali Island in Fiji.Credit: Juergen Freund/naturepl.com

Coral reefs are under threat, and so too are the livelihoods of more 500 million people who depend on them. Global climate change is causing longer and more frequent marine heatwaves, leading to widespread and repeated coral bleaching. Overfishing and pollution exacerbate the problem, adding pressure to these marine biodiversity hotspots that sustain coastal communities.Reef-management programmes that limit or prohibit fishing and other commercial activities are bound to be ineffective if local communities are not involved in their design and management, says Sangeeta Mangubhai, a coral-reef ecologist in Fiji. “If people haven’t been engaged in the management [of conservation strategies], they’re not as likely to understand what the rules are, or they might not comply with it,” she says. Initiatives that are designed to protect coral reefs without incorporating insights from local communities may also affect them in unintended ways, she adds.
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In collaboration with environmental social scientist, Georgina Gurney, Mangubhai is identifying the conditions that support both conservation outcomes and the wellbeing of coastal communities who often have cultural practices and spiritual ties to the sea. Their work explores the social factors that influence coral-reef-management programmes, such as the perceived fairness of payment schemes that direct tourism revenue back to the communities who manage local reefs (G. G. Gurney et al. Environ. Sci. Policy 124, 23–32; 2021).“First and foremost, it’s an ethical and moral issue,” says Gurney. “Conservation should not impinge on the wellbeing of people; it should promote the wellbeing of people.”Based at James Cook University (JCU) in Townsville, a city on the northeastern coast of Queensland, Australia, Gurney has close access to the Great Barrier Reef, which contains the world’s largest coral reef ecosystem. The university has long-standing ties with researchers in nearby Pacific island nations, such as Papua New Guinea, Fiji and New Caledonia.Townsville was the second most-prolific city in the 82 high-quality natural-sciences journals tracked by the Nature Index for research related to the United Nations’ Sustainable Development Goal (SDG) Life below water (SDG14) in 2015–20, with a Share of 15.59, 52% of which is attributed to JCU. Beijing, placed first by output related to SDG14, had a Share of 17.88 for the same period. (For more information on the analyses used in this article, see ‘A guide to Nature Index’.)

Georgina Gurney and Sangeeta Mangubhai at a fish market in Suva, Fiji.Credit: Isabelle Gurney

According to Gurney, successful conservation programmes should evaluate social factors alongside ecological outcomes, such as fish stocks and coral health, although this is rarely the case. With Mangubhai and other collaborators, Gurney has developed a framework that combines 90 social and ecological indicators, from coral cover and fish biomass to household incomes derived from the reef, equitable benefit-sharing and conflicts occurring over marine resources (G. G. Gurney et al. Biol. Conserv. 240, 108298; 2019).In principle, the framework standardizes how outcomes of coral-reef programmes are evaluated to improve data collection and enable cross-country comparisons. It has been adopted by the New York-based non-governmental organization, the Wildlife Conservation Society (WCF), and its partners in 7 countries and more than 130 communities across Africa, Asia and the Pacific.Besides improving conservation efforts, Mangubhai, who leads the WCF’s Fiji programme, says the partnership gives equal footing to local conservation scientists and policymakers, empowering them to direct independent research. “If you have these meaningful collaborations, the outcome is going to have so much more of an impact on the ground,” she says.Incorporating an understanding of the social factors that influence coral-reef conservation into marine-management strategies translates to respect for local traditional cultural practices of Indigenous Fijians, says Mangubhai. Temporary closures called tabu, which are used to maintain the productivity of their customary fishing grounds, are a good example. “It’s a real merging of traditional knowledge and other best practices, such as size limits on fish catch, to help communities achieve the outcomes they want for themselves,” she says.

doi: https://doi.org/10.1038/d41586-021-02409-6This article is part of Nature Index 2021 Science cities, an editorially independent supplement produced with the financial support of third parties. About this content.

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NATURE INDEX
24 September 2021

Rising tide of floating plastics spurs surge in research

Strong government policies and research insights are essential to deliver on a pledge to clean up the sea.

Michael Eisenstein

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Michael Eisenstein

Michael Eisenstein is a freelance writer in Philadelphia, Pennsylvania.

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A jellyfish swims beneath a slick of floating plastic debris in the Indian Ocean near Sri Lanka.Credit: Alex Mustard/naturepl.com

Many stories have been written about the ‘Great Pacific garbage patch’, a name evoking a vast Sargasso Sea of plastic bottles and bags. But the reality is that much of this debris has been broken down into a murky suspension of ‘microplastics’ spanning an area three times the size of France.
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These plastic flecks introduce long-lasting chemical pollution into marine and coastal ecosystems, says Daoji Li, an oceanographer at East China Normal University in Shanghai. In 2020, Li and his colleagues found that microplastic debris is highly concentrated in even the deepest underwater trenches (G. Peng et al. Water Res. 168, 115121; 2020). Staving off this influx of pollutants is a target of the United Nations’ Sustainable Development Goal (SDG) Life below water (SDG14), with its aim to “prevent and significantly reduce marine pollution of all kinds” by 2025.Between 4.8 million and 12.7 million tonnes of plastic waste entered the oceans in 2010, according to a study in Science, and those numbers are expected to increase dramatically by 2050 without improvements to waste-management infrastructure (J. Jambeck et al. Science 347, 768–771; 2015). Scientists in China, which is a major producer and importer of plastic waste, are taking the lead in amelioration. According to the 2021 UNESCO Science Report, floating plastic debris was the fastest-growing area of SDG-related research in 2012–19 (see ‘A buoyant field’). Publications from the Chinese mainland on the topic jumped from 7 in the period 2012–15 to 286 in 2016–19, placing it third by volume after the United States and United Kingdom. Much of this work has come from investigators in Beijing, the top-ranked city in the Nature Index for SDG14-related research. (For more information on the analyses used in this article, see ‘A guide to Nature Index’.)

Source: UNESCO

Li is sceptical that much can be done to eliminate existing plastic pollution. “But what we can do is stop them entering to the ocean,” he says. His team has developed a monitoring framework that outlines ‘gold-standard’ technologies and assays for detecting and quantifying microplastic contamination.Government action is essential to stem the flow of plastic debris. UNESCO reports that 127 countries have adopted legislation to regulate plastic bags. In 2020, China launched an ambitious effort to ban plastic bags nationwide by 2022 and cut single-use plastic in restaurants by one-third by 2025 — although the COVID-19 pandemic created a surge in demand for delivery that derailed this effort.Despite the many hurdles to overcome, Li feels positive about the future. “I am pretty confident that we could meet the target set for SDG14,” he says, “but when we realize those challenges, we should keep going.”

Source: UNESCO

doi: https://doi.org/10.1038/d41586-021-02408-7This article is part of Nature Index 2021 Science cities, an editorially independent supplement produced with the financial support of third parties. About this content.

Related Articles

Sustainable Development Goals research speaks to city strengths and priorities

Tracking 20 leading cities’ Sustainable Development Goals research

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Uneven spread of research leaves poorer cities short of solutions

Pursuit of better batteries underpins China’s lead in energy research

How Belo Horizonte’s bid to tackle hunger inspired other cities

New York bids to level the playing field in a metropolis of inequality

Subjects

Conservation biology

Ocean sciences

Sustainability

Latest on:

Ocean sciences

Coral conservation strikes a balance
Nature Index 24 SEP 21

How cities are collaborating to help safeguard oceans
Nature Index 24 SEP 21

Widespread phytoplankton blooms triggered by 2019–2020 Australian wildfires
Article 15 SEP 21

Sustainability

Sustainable Development Goals research speaks to city strengths and priorities
Nature Index 24 SEP 21

Tracking 20 leading cities’ Sustainable Development Goals research
Nature Index 24 SEP 21

Coral conservation strikes a balance
Nature Index 24 SEP 21

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Postdoctoral Research Fellow in Bioinformatics and Genomics

Max Planck Institute for Molecular Biomedicine
Münster, Germany

Associate Professor (Tenure) or Professor (Tenure), Biomaterials

The University of British Columbia (UBC)
Vancouver, Canada

Postdoctoral Fellow in Functional Genomics/Glycomics

The University of British Columbia (UBC)
Vancouver, Canada

60048: Physicist, Statistician, theoretical Computer Scientist or similar (f/m/x) – Development of causal inference methods in the field causal Inference and machine learning as part of the EU project XAIDA

German Aerospace Center (DLR)
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NATURE INDEX
24 September 2021

How cities are collaborating to help safeguard oceans

Despite missed deadlines in 2020 for key targets in marine conservation, momentum for these Sustainable Development Goals is growing.

Michael Eisenstein

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Michael Eisenstein

Michael Eisenstein is a freelance writer in Philadelphia, Pennsylvania.

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Bart Shepherd, co-leader of the Hope for Reefs initiative, guides fish into a decompression chamber while on expedition in Vanuatu.Credit: Luiz Rocha/California Academy of Sciences

For about 30 minutes each year, vast colonies of corals in the waters of Palau, an island nation in the western Pacific, erupt in an almost perfectly synchronized mass-spawning event. Releasing buoyant packages of sperm and egg cells into the water to be fertilized by neighbouring colonies, these hermaphroditic species must make the most of rare opportunities to seed new life.In one of the world’s few indoor coral-culturing labs, Rebecca Albright and her team at the California Academy of Sciences in San Francisco are recreating the seasonal and lunar shifts that trigger such an event. The aim is to create multiple spawning systems that can be studied under controlled conditions. “Corals are notorious for being fickle animals to keep in captivity,” says Albright, a coral biologist and co-leader of Hope for Reefs, a global initiative to research and restore crucial coral-reef systems. “Most only sexually reproduce once a year, so you have to simulate all these environmental cues to elicit that.”
Nature Index 2021 Science cities
Strategies for cultivating and transplanting healthy corals into depleted areas are a crucial part of strengthening populations against what Albright describes as the “one-two punch effect” of climate change. Rising temperatures cause coral bleaching and death, while ocean acidification caused by increased levels of carbon dioxide makes corals less resilient and prevents regrowth. “If we are able to cap warming at 1.5  °C, we’re still going to lose 90% of reefs by 2050,” she says. “And if we edge towards 2 °C, we risk losing 97% to 99%.”Of the United Nations’ 17 Sustainable Development Goals (SDGs), Life below water (SDG14) and other SDGs related to environmental sustainability — Responsible consumption and production (SDG12), Climate action (SDG13) and Life on land (SDG15) — were the weakest in both donor funding and outcomes, attracting less than US$25 billion between them in 2000–13, according to the 2021 UNESCO Science Report (see go.nature.com/3zlojva). SDGs that are more directly related to economic growth — Industry, innovation and infrastructure (SDG9) and Sustainable cities and communities (SDG11) — by comparison, received $130 billion and $147 billion, respectively, over the same period.James Leape, co-director of Stanford University’s Center for Ocean Solutions in California, notes that four of the ten targets for SDG14, which aims to “conserve and sustainably use the oceans, seas and marine resources”, were due in 2020. All were missed. These include controlling the global damage wrought by illegal and unregulated fishing, which remains largely unchecked, and implementing scientifically grounded strategies for restoring affected fish stocks.But there are signs of momentum. The amount of ocean being conserved and managed within marine protected areas (MPAs), for example, has increased from 0.9% to 7.7% since 2000, says Leape. MPAs are regions in which fishing, mining and other activities are restricted. Efforts are under way to further expand the number of MPAs globally.Coastal collaborationsAs the world’s leading fishing nation, responsible for 15% of the reported global wild fish catch, China has ramped up efforts to designate new MPAs. Since 1980, China has designated more than 270 MPAs, comprising about 5% of its national waters. But it’s a long way off efforts by countries such as the United States, which has more than 1,000 MPAs that cover about 26% of its waters, and the United Kingdom, with 371 MPAs comprising 38% of its seas. In a 2019 Nature correspondence, fisheries researchers Yunzhou Li and Yiping Ren, from the Ocean University of China in Qingdao and Yong Chen from the University of Maine, Orono, say that effective monitoring and strict enforcement will also be essential to the success of China’s efforts (see Nature 573, 346; 2019).In a city-based analysis by the Nature Index, Beijing had the greatest output related to SDG14 in the 82 natural-sciences journals tracked by the index in 2015–20, with a Share of 17.88, followed by the coastal city of Townsville in northeastern Queensland, Australia (Share 15.59) and the Boston metropolitan area (Share 13.66). The San Francisco Bay Area, second only to Beijing in output related to all 17 SDGs, had the sixth-highest Share for SDG14 (13.24). (For more information on the analyses used in this article, see ‘A guide to Nature Index’.)

Residents in the coastal town of Maroantsetra, in northeastern Madagascar, display their catch.Credit: Rebecca Gaal

Many small island states face serious threats from the rapid decline of their coral reefs, which represent one of the world’s most diverse ecosystems. Gildas Todinanahary, a marine biologist at the Fisheries and Marine Science Institute at the University of Toliara in Madagascar, says the percentage of live coral cover surrounding the island nation has dropped from more than 80% in the 1980s to less than 10%, on average, today. “Decades ago, they used to say there will always be fish in the sea,” says Todinanahary. “Now they say there are no more fish.” This has jeopardized the livelihood of the fishing communities on the island’s western shore, he says.Christopher Golden, an ecologist and epidemiologist at the Harvard School of Public Health in Boston, is working with Todinanahary and his colleagues to deploy a series of small tiered platforms, designed to mimic the cracks and crevices of the reef, into healthy coral communities along the Madagascar coast. Once colonized, these structures are transported into degraded reefs in an effort to repopulate them. “If we can create a healthier reef, we can then rehabilitate some of the fish populations, and that will lead to improved fish-catch and greater access to seafood as a nutritional resource,” says Golden.Todinanahary is enthusiastic about the potential for seeding new reefs in barren coastal stretches, but says education and outreach to fishing communities will be key to ensuring that those restoration efforts endure. “It’s important to help communities change their habits and activities,” he says — for example, by providing training for alternative livelihoods such as aquaculture.Buy-in from community leaders is also crucial to the success of partnerships between researchers in leading science cities and colleagues in low- and middle-income maritime nations in SDG-related projects. In 2016, the government of Palau invited Leape and his team at Stanford to develop a strategy for turning 80% of its exclusive economic zone, a 370-km radius surrounding the island, into a protected area where fishing is prohibited. The initiative went into effect in January 2020. “We’re using satellite tracking to understand the patterns of use of the sanctuary by large pelagic species, and using DNA analysis to monitor biodiversity in the sanctuary,” says Leape. Palau’s programme has helped to motivate other island nations in the region to extend marine protection and conservation efforts as part of the Micronesia Challenge, an initiative to conserve 50% of marine resources and 30% of terrestrial resources by 2030.Golden’s research emphasizes both the sustainability and food-security sides of the fisheries-management coin, with routine health assessments of communities in places such as Madagascar and the Republic of Kiribati, an island nation in the central Pacific Ocean, coupled with close monitoring of the ecological health of their surrounding waters. To help this effort, Golden and his colleagues developed the Aquatic Food Composition Database, which compiles detailed nutritional information on more than 3,700 local plant and animal species to provide ecologically grounded guidance to local fishers. “We can look at what type of resilience there might be if we lose access to one species and have to focus on another,” says Golden. “We can understand the type of nourishment that people are actually getting from their catch.”Stanford’s Center for Ocean Solutions is also leveraging new technologies to guide sustainable fishing practices that benefit small-scale fishers, whose livelihood SDG14 aims to safeguard. “Their catches account for about two-thirds of the seafood we eat, and 90% of the fishery jobs,” says Leape. The centre is partnering with ABALOBI, an organization in South Africa founded by fisheries researcher Serge Raemaekers, from the University of Cape Town. ABALOBI has designed a mobile app toolbox to help fishers track specific fish populations, coordinate boats and crews, and bring catches to market. Leape is hopeful that early pilot testing in Africa and the Indian Ocean will pave the way for broader deployment in the near future.In parallel, Leape’s team is working on strategies to crack down on illegal fishing — currently estimated to account for roughly 20% of the global catch. This is being achieved partly through tools such as the satellite-based fishery monitoring efforts of Global Fishing Watch, a website run by Google in partnership with conservation non-profit organizations Oceana and SkyTruth. But technology is only part of the solution. Leape sees a crucial role for aggressive government enforcement and getting major corporations to engage in closer oversight of fishing practices. “We’ve been using Global Fishing Watch and other data sources to understand the patterns and areas for illegal fishing,” he says. “We’re working with these partners to try to translate that data into a more concerted effort to crack the problem.”

doi: https://doi.org/10.1038/d41586-021-02407-8This article is part of Nature Index 2021 Science cities, an editorially independent supplement produced with the financial support of third parties. About this content.

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1.China. China statistical yearbook. (China Statistics Press, 2020).2.Shiferaw, B., Prasanna, B. M., Hellin, J. & Bänziger, M. Crops that feed the world 6. Past successes and future challenges to the role played by maize in global food security. Food Secur. 3, 307–327 (2011).Article 

Google Scholar 
3.Chen, M. P., Sun, F. & Shindo, J. China’s agricultural nitrogen flows in 2011: Environmental assessment and management scenarios. Resour. Conserv. Recycl. 111, 10–27 (2016).Article 

Google Scholar 
4.He, Y. X. et al. Tracking ammonia morning peak, sources and transport with 1 Hz measurements at a rural site in North China Plain. Atmos. Environ. 235, 117630 (2020).CAS 
Article 

Google Scholar 
5.Zhang, Y. et al. Agricultural ammonia emissions inventory and spatial distribution in the North China Plain. Environ. Pollut. 158, 490–501 (2010).CAS 
PubMed 
Article 

Google Scholar 
6.Ayars, J. E., Fulton, A. & Taylor, B. Subsurface drip irrigation in California—Here to stay?. Agric. Water Manag. 157, 39–47 (2015).Article 

Google Scholar 
7.Chauhdary, J. N., Bakhsh, A., Engel, B. A. & Ragab, R. Improving corn production by adopting efficient fertigation practices: Experimental and modeling approach. Agric. Water Manag. 221, 449–461 (2019).Article 

Google Scholar 
8.Mali, S. S., Naik, S. K., Jha, B. K., Singh, A. K. & Bhatt, B. P. Planting geometry and growth stage linked fertigation patterns: Impact on yield, nutrient uptake and water productivity of Chilli pepper in hot and sub-humid climate. Sci. Hortic. (Amsterdam) 249, 289–298 (2019).Article 

Google Scholar 
9.Silber, A. et al. High fertigation frequency: the effects on uptake of nutrients, water and plant growth. Plant Soil 253, 467–477 (2003).CAS 
Article 

Google Scholar 
10.Wu, D. L. et al. Effect of different drip fertigation methods on maize yield, nutrient and water productivity in two-soils in Northeast China. Agric. Water Manag. 213, 200–211 (2019).Article 

Google Scholar 
11.Ning, D. et al. Deficit irrigation combined with reduced N-fertilizer rate can mitigate the high nitrous oxide emissions from Chinese drip-fertigated maize field. Glob. Ecol. Conserv. 20, e00803 (2019).Article 

Google Scholar 
12.Sandhu, O. S. et al. Drip irrigation and nitrogen management for improving crop yields, nitrogen use efficiency and water productivity of maize-wheat system on permanent beds in north-west India. Agric. Water Manag. 219, 19–26 (2019).Article 

Google Scholar 
13.Li, H. et al. Effects of different nitrogen fertilizers on the yield, water- and nitrogen-use efficiencies of drip-fertigated wheat and maize in the North China Plain. Agric. Water Manag. 243, 106474 (2021).Article 

Google Scholar 
14.Lamm, F. R., Stone, L. R., Manges, H. L. & O’Brien, D. M. Optimum lateral spacing for subsurface drip-irrigated corn. Trans. ASAE 40, 1021–1027 (1997).Article 

Google Scholar 
15.Bozkurt, Y., Yazar, A., Gençel, B. & Sezen, M. S. Optimum lateral spacing for drip-irrigated corn in the Mediterranean Region of Turkey. Agric. Water Manag. 85, 113–120 (2006).Article 

Google Scholar 
16.Chen, R. et al. Lateral spacing in drip-irrigated wheat: The effects on soil moisture, yield, and water use efficiency. Field Crop. Res. 179, 52–62 (2015).Article 

Google Scholar 
17.Zhou, L. et al. Drip irrigation lateral spacing and mulching affects the wetting pattern, shoot-root regulation, and yield of maize in a sand-layered soil. Agric. Water Manag. 184, 114–123 (2017).Article 

Google Scholar 
18.Eissa, M. A. Efficiency of P fertigation for drip-irrigated potato grown on calcareous sandy soils. Potato Res. 62, 97–108 (2019).CAS 
Article 

Google Scholar 
19.Irmak, S., Djaman, K. & Rudnick, D. R. Effect of full and limited irrigation amount and frequency on subsurface drip-irrigated maize evapotranspiration, yield, water use efficiency and yield response factors. Irrig. Sci. 34, 271–286 (2016).Article 

Google Scholar 
20.Yao, Y. L. et al. Urea deep placement for minimizing NH3 loss in an intensive rice cropping system. Field Crop. Res. 218, 254–266 (2018).Article 

Google Scholar 
21.Ziadi, N., Cambouris, A. N., Nyiraneza, J. & Nolin, M. C. Across a landscape, soil texture controls the optimum rate of N fertilizer for maize production. Field Crop. Res. 148, 78–85 (2013).Article 

Google Scholar 
22.Fang, H. et al. An optimized model for simulating grain-filling of maize and regulating nitrogen application rates under different film mulching and nitrogen fertilizer regimes on the Loess Plateau. China. Soil Tillage Res. 199, 104546 (2020).Article 

Google Scholar 
23.Zheng, J. et al. Interactive effects of mulching practice and nitrogen rate on grain yield, water productivity, fertilizer use efficiency and greenhouse gas emissions of rainfed summer maize in northwest China. Agric. Water Manag. 248, 106778 (2021).Article 

Google Scholar 
24.Qi, X. L. et al. Grain yield and apparent N recovery efficiency of dry direct-seeded rice under different N treatments aimed to reduce soil ammonia volatilization. Field Crop. Res. 134, 138–143 (2012).Article 

Google Scholar 
25.Han, K., Zhou, C. J. & Wang, L. Q. Reducing ammonia volatilization from maize fields with separation of nitrogen fertilizer and water in an alternating furrow irrigation system. J. Integr. Agric. 13, 1099–1112 (2014).CAS 
Article 

Google Scholar 
26.Amin, A.E.-E.A.Z. Carbon sequestration, kinetics of ammonia volatilization and nutrient availability in alkaline sandy soil as a function on applying calotropis biochar produced at different pyrolysis temperatures. Sci. Total Environ. 726, 138489 (2020).ADS 
CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
27.Li, H. T. et al. Film mulching, residue retention and N fertilization affect ammonia volatilization through soil labile N and C pools. Agric. Ecosyst. Environ. 308, 107272 (2021).CAS 
Article 

Google Scholar 
28.Sun, B. et al. Bacillus subtilis biofertilizer mitigating agricultural ammonia emission and shifting soil nitrogen cycling microbiomes. Environ. Int. 144, 105989 (2020).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
29.Tabli, N. et al. Plant growth promoting and inducible antifungal activities of irrigation well water-bacteria. Biol. Control 117, 78–86 (2018).Article 

Google Scholar 
30.Zhong, X. M. et al. Reducing ammonia volatilization and increasing nitrogen use efficiency in machine-transplanted rice with side-deep fertilization in a double-cropping rice system in Southern China. Agric. Ecosyst. Environ. 306, 107183 (2021).CAS 
Article 

Google Scholar 
31.Li, C., Sun, M. X., Xu, X. B. & Zhang, L. X. Characteristics and influencing factors of mulch film use for pollution control in China: Microcosmic evidence from smallholder farmers. Resour. Conserv. Recycl. 164, 105222 (2021).Article 

Google Scholar 
32.Li, M. N., Wang, Y. L., Adeli, A. & Yan, H. J. Effects of application methods and urea rates on ammonia volatilization, yields and fine root biomass of alfalfa. Field Crop. Res. 218, 115–125 (2018).Article 

Google Scholar 
33.Pinheiro, P. L. et al. Straw removal reduces the mulch physical barrier and ammonia volatilization after urea application in sugarcane. Atmos. Environ. 194, 179–187 (2018).ADS 
CAS 
Article 

Google Scholar 
34.Zhu, H. et al. Interactive effects of soil amendments (biochar and gypsum) and salinity on ammonia volatilization in coastal saline soil. CATENA 190, 104527 (2020).CAS 
Article 

Google Scholar 
35.Oppong Danso, E. et al. Effect of different fertilization and irrigation methods on nitrogen uptake, intercepted radiation and yield of okra (Abelmoschus esculentum L.) grown in the Keta Sand Spit of Southeast Ghana. Agric. Water Manag. 147, 34–42 (2015).Article 

Google Scholar 
36.Liu, R. H. et al. Chemical fertilizer pollution control using drip fertigation for conservation of water quality in Danjiangkou Reservoir. Nutr. Cycl. Agroecosystems 98, 295–307 (2014).CAS 
Article 

Google Scholar 
37.Sanz-Cobena, A. et al. Strategies for greenhouse gas emissions mitigation in mediterranean agriculture: A review. Agric. Ecosyst. Environ. 238, 5–24 (2017).CAS 
Article 

Google Scholar 
38.Zhou, J. B., Xi, J. G., Chen, Z. J. & Li, S. X. Leaching and transformation of nitrogen fertilizers in soil after application of n with irrigation: A soil column method. Pedosphere 16, 245–252 (2006).CAS 
Article 

Google Scholar 
39.Rosemary, F., Vitharana, U. W. A., Indraratne, S. P., Weerasooriya, R. & Mishra, U. Exploring the spatial variability of soil properties in an Alfisol soil catena. CATENA 150, 53–61 (2017).CAS 
Article 

Google Scholar 
40.Liu, Y., Lv, J. S., Zhang, B. & Bi, J. Spatial multi-scale variability of soil nutrients in relation to environmental factors in a typical agricultural region, Eastern China. Sci. Total Environ. 450–451, 108–119 (2013).ADS 
PubMed 
Article 
CAS 
PubMed Central 

Google Scholar 
41.Vasu, D. et al. Assessment of spatial variability of soil properties using geospatial techniques for farm level nutrient management. Soil Tillage Res. 169, 25–34 (2017).Article 

Google Scholar 
42.Jin, J. Y., Bai, Y. L. & Yang, L. P. High Efficiency Soil Nutrient Testing Technology and Equipment (China Agriculture Press, 2006) (in Chinese).
Google Scholar 
43.Tan, Y. et al. Improving wheat grain yield via promotion of water and nitrogen utilization in arid areas. Sci. Rep. 11, 13821 (2021).ADS 
CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
44.Ren, Y. et al. Effect of sowing proportion on above- and below-ground competition in maize–soybean intercrops. Sci. Rep. 11, 15760 (2021).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
45.Wang, Z. H., Liu, X. J., Ju, X. T., Zhang, F. S. & Malhi, S. S. Ammonia volatilization loss from surface-broadcast urea: comparison of vented- and closed-chamber methods and loss in winter wheat–summer maize rotation in North China plain. Commun. Soil Sci. Plant Anal. 35, 2917–2939 (2004).CAS 
Article 

Google Scholar 
46.Zhou, L. P. et al. Comparison of several slow-released nitrogen fertilizers in ammonia volatilization and nitrogen utilization in summer maize field. J. Plant Nutr. Fertil. 22, 1449–1457 (2016) (in Chinese).
Google Scholar 
47.Huang, T. M. et al. Grain zinc concentration and its relation to soil nutrient availability in different wheat cropping regions of China. Soil Tillage Res. 191, 57–65 (2019).Article 

Google Scholar 
48.Wang, Z., Li, J. & Li, Y. Effects of drip system uniformity and nitrogen application rate on yield and nitrogen balance of spring maize in the North China Plain. Field. Crop. Res. 159, 10–20 (2014).Article 

Google Scholar 
49.Brar, H. S., Vashist, K. K. & Bedi, S. Phenology and yield of spring maize (Zea mays L.) under different drip irrigation regimes and planting methods. J. Agric. Sci. Technol. 18, 831–843 (2016).
Google Scholar 
50.Poch-Massegú, R., Jiménez-Martínez, J., Wallis, K. J., Ramírez de Cartagena, F. & Candela, L. Irrigation return flow and nitrate leaching under different crops and irrigation methods in Western Mediterranean weather conditions. Agric. Water Manag. 134, 1–13 (2014).Article 

Google Scholar 
51.Yuan, Z. Q. et al. Film mulch with irrigation and rainfed cultivations improves maize production and water use efficiency in Ethiopia. Ann. Appl. Biol. 175, 215–227 (2019).Article 

Google Scholar 
52.Wang, J. L. Research on the use of water and fertilizer for drip irrigation multiple cropping silage maize (Shihezi University, 2016) (in Chinese).
Google Scholar 
53.Lamm, F. R. & Trooien, T. P. Subsurface drip irrigation for corn production: a review of 10 years of research in Kansas. Irrig. Sci. 22, 195–200 (2003).Article 

Google Scholar 
54.Yan, X. L., Jia, L. M. & Dai, T. F. Effects of water and nitrogen coupling under drip irrigation on tree growth and soil nitrogen content of Populus × euramericana cv. ‘Guariento’. Chin. J. Appl. Ecol. 29, 2195 (2018) (in Chinese).
Google Scholar 
55.Sun, W. T., Sun, Z. X., Wang, C. X., Gong, L. & Zhang, Y. L. Coupling effect of water and fertilizer on corn yield under drip fertigation. Sci. Agric. Sin. 39, 563–568 (2006) (in Chinese).
Google Scholar 
56.Banerjee, B., Pathak, H. & Aggarwal, P. Effects of dicyandiamide, farmyard manure and irrigation on crop yields and ammonia volatilization from an alluvial soil under a rice (Oryza sativa L.)-wheat (Triticum aestivum L.) cropping system. Biol. Fertil. Soils 36, 207–214 (2002).CAS 
Article 

Google Scholar 
57.Yang, Q. L., Liu, P., Dong, S. T., Zhang, J. W. & Zhao, B. Effects of fertilizer type and rate on summer maize grain yield and ammonia volatilization loss in northern China. J. Soils Sediments 19, 2200–2211 (2019).CAS 
Article 

Google Scholar 
58.Zhou, G. W. et al. Effects of saline water irrigation and N application rate on NH3 volatilization and N use efficiency in a drip-irrigated cotton field. Water Air Soil Pollut. 227, 103 (2016).ADS 
Article 
CAS 

Google Scholar 
59.Zheng, J., Kilasara, M. M., Mmari, W. N. & Funakawa, S. Ammonia volatilization following urea application at maize fields in the East African highlands with different soil properties. Biol. Fertil. Soils 54, 411–422 (2018).CAS 
Article 

Google Scholar 
60.Li, Z. et al. Nitrogen use efficiency and ammonia oxidation of corn field with drip irrigation in Hetao irrigation district. J. Irrig. Drain. 37, 37–42,49 (2018) (in Chinese).61.Zheng, L. et al. Impact of fertilization on ammonia volatilization and N2O emissions in an open vegetable field. Chin. J. Appl. Ecol. 29, 4063–4070 (2018) (in Chinese).
Google Scholar 
62.Li, Y. Q., Liu, G., Hong, M., Wu, Y. & Chang, F. Effect of optimized nitrogen application on nitrous oxide emission and ammonia volatilization in Hetao irrigation area. Acta Sci. Circumst. 39, 578–584 (2019) (in Chinese).CAS 

Google Scholar 
63.Das, P. et al. Emissions of ammonia and nitric oxide from an agricultural site following application of different synthetic fertilizers and manures. Geosci. J. 12, 177–190 (2008).ADS 
CAS 
Article 

Google Scholar 
64.Cai, G. X. et al. Nitrogen losses from fertilizers applied to maize, wheat and rice in the North China Plain. Nutr. Cycl. Agroecosyst. 63, 187–195 (2002).CAS 
Article 

Google Scholar 
65.Wang, X. L. et al. Corn compensatory growth upon post-drought rewatering based on the effects of rhizosphere soil nitrification on cytokinin. Agric. Water Manag. 241, 106436 (2020).Article 

Google Scholar 
66.Li, G. et al. Effect of drip fertigation on summer maize in north China. Sci. Agric. Sin. 52, 1930–1941 (2019) (in Chinese).
Google Scholar  More