Ecology

1.Chittka, L. & Raine, N. E. Recognition of flowers by pollinators. Curr. Opin. Plant Biol. 9, 428–435 (2006).PubMed 
Article 
PubMed Central 

Google Scholar 
2.Raguso, R. A. Flowers as sensory billboards: Progress towards an integrated understanding of floral advertisement. Curr. Opin. Plant Biol. 7, 434–440 (2004).PubMed 
Article 
PubMed Central 

Google Scholar 
3.Goulson, D., Stout, J. C. & Hawson, S. A. Can flower constancy in nectaring butterflies be explained by Darwin’s interference hypothesis?. Oecologia 112, 225–231 (1997).ADS 
CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
4.Goulson, D. & Wright, N. P. Flower constancy in the hoverflies Episyrphus balteatus (Degeer) and Syrphus ribesii (L.) (Syrphidae). Behav. Ecol. 9, 213–219 (1998).Article 

Google Scholar 
5.Von Arx, M., Goyret, J., Davidowitz, G. & Raguso, R. A. Floral humidity as a reliable sensory cue for profitability assessment by nectar-foraging hawkmoths. Proc. Natl. Acad. Sci. 109, 9471–9476 (2012).ADS 
Article 

Google Scholar 
6.Leonard, A. S., Dornhaus, A. & Papaj, D. R. Forget-me-not: Complex floral displays, inter-signal interactions, and pollinator cognition. Curr. Zool. 57, 215–224 (2011).Article 

Google Scholar 
7.Vaknin, Y., Gan-Mor, S., Bechar, A., Ronen, B. & Eisikowitch, D. The role of electrostatic forces in pollination. Plant Syst. Evol. 222, 133–142. https://doi.org/10.1007/bf00984099 (2000).Article 

Google Scholar 
8.Bowker, G. E. & Crenshaw, H. C. Electrostatic forces in wind-pollination—Part 2: Simulations of pollen capture. Atmos. Environ. 41, 1596–1603 (2007).ADS 
CAS 
Article 

Google Scholar 
9.Erickson, E. Surface electric potentials on worker honeybees leaving and entering the hive. J. Apic. Res. 14, 141–147 (1975).Article 

Google Scholar 
10.Edwards, D. Electrostatic charges on insects due to contact with different substrates. Can. J. Zool. 40, 579–584 (1962).Article 

Google Scholar 
11.Vaknin, Y., Gan-Mor, S., Bechar, A., Ronen, B. & Eisikowitch, D. Pollen and Pollination 133–142 (Springer, 2000).Book 

Google Scholar 
12.Eskov, E. & Sapozhnikov, A. Mechanism of generation and perception of electric fields by honey bees. Biofizika 21, 1097–1102 (1976).CAS 

Google Scholar 
13.Clarke, D., Whitney, H., Sutton, G. & Robert, D. Detection and learning of floral electric fields by bumblebees. Science 340, 66–69 (2013).ADS 
CAS 
PubMed 
Article 

Google Scholar 
14.Bowker, G. E. & Crenshaw, H. C. Electrostatic forces in wind-pollination—Part 1: Measurement of the electrostatic charge on pollen. Atmos. Environ. 41, 1587–1595 (2007).ADS 
CAS 
Article 

Google Scholar 
15.Gan-Mor, S., Schwartz, Y., Bechar, A., Eisikowitch, D. & Manor, G. Relevance of electrostatic forces in natural and artificial pollination. Can. Agric. Eng. 37, 189–194 (1995).
Google Scholar 
16.Colin, M., Richard, D. & Chauzy, S. Measurement of electric charges carried by bees: Evidence of biological variations. J. Bioelectr. 10, 17–32 (1991).Article 

Google Scholar 
17.Pinillos, V. & Cuevas, J. Artificial pollination in tree crop production. Horticult. Rev. 2, 2 (2008).
Google Scholar 
18.Corbet, S. A., Beament, J. & Eisikowitch, D. Are electrostatic forces involved in pollen transfer?. Plant Cell Environ. 5, 125–129 (1982).Article 

Google Scholar 
19.Inouye, D. W., Larson, B. M., Ssymank, A. & Kevan, P. G. Flies and flowers III: Ecology of foraging and pollination. J. Pollin. Ecol. 16, 115–133 (2015).Article 

Google Scholar 
20.Kanstrup, J. & Olesen, J. M. Plant-flower visitor interactions in a neotropical rain forest canopy: Community structure and generalisation level. The Scand. Assoc. Pollin. Ecol. honours knut Fægri 2, 33–42 (2000).
Google Scholar 
21.Orford, K. A., Vaughan, I. P. & Memmott, J. The forgotten flies: The importance of non-syrphid Diptera as pollinators. Proc. R. Soc. B Biol. Sci. 282, 20142934 (2015).Article 

Google Scholar 
22.Sakurai, A. & Takahashi, K. Flowering phenology and reproduction of the Solidago virgaurea L. complex along an elevational gradient on M t N orikura, central Japan. Plant Sp. Biol. 32, 270–278 (2017).Article 

Google Scholar 
23.Forup, M. L., Henson, K. S., Craze, P. G. & Memmott, J. The restoration of ecological interactions: Plant–pollinator networks on ancient and restored heathlands. J. Appl. Ecol. 45, 742–752 (2008).Article 

Google Scholar 
24.Solomon, M. & Kendall, D. Pollination by the syrphid fly, Eristalis tenax. (1970).25.Kendall, D., Wilson, D., Guttridge, C. & Anderson, H. Testing Eristalis as a pollinator of covered crops. Long Ashton Res. Stn. Rep. 1971, 120–121 (1971).
Google Scholar 
26.Ohsawa, R. & Namai, H. The effect of insect pollinators on pollination and seed setting in Brassica campestris cv. Nozawana and Brassica juncea cv Kikarashina. Jpn. J. Breed. 37, 453–463 (1987).ADS 
Article 

Google Scholar 
27.Jauker, F. & Wolters, V. Hover flies are efficient pollinators of oilseed rape. Oecologia 156, 819–823 (2008).ADS 
PubMed 
Article 

Google Scholar 
28.Rader, R. et al. Alternative pollinator taxa are equally efficient but not as effective as the honeybee in a mass flowering crop. J. Appl. Ecol. 46, 1080–1087 (2009).Article 

Google Scholar 
29.Kalmijn, A. J. The electric sense of sharks and rays. J. Exp. Biol. 55, 371–383 (1971).CAS 
PubMed 
Article 

Google Scholar 
30.Clarke, D., Morley, E. & Robert, D. The bee, the flower, and the electric field: Electric ecology and aerial electroreception. J. Comp. Physiol. A. 203, 737–748 (2017).Article 

Google Scholar 
31.Greggers, U. et al. Reception and learning of electric fields in bees. Proc. R. Soc. B Biol. Sci. 280, 20130528 (2013).Article 

Google Scholar 
32.Casas, J. & Dangles, O. Physical ecology of fluid flow sensing in arthropods. Annu. Rev. Entomol. 55, 505–520 (2010).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
33.Tautz, J. & Rostás, M. Honeybee buzz attenuates plant damage by caterpillars. Curr. Biol. 18, R1125–R1126 (2008).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
34.Bathellier, B., Steinmann, T., Barth, F. G. & Casas, J. Air motion sensing hairs of arthropods detect high frequencies at near-maximal mechanical efficiency. J. R. Soc. Interface 9, 1131–1143 (2011).PubMed 
PubMed Central 
Article 

Google Scholar 
35.Newland, P. L. et al. Static electric field detection and behavioural avoidance in cockroaches. J. Exp. Biol. 211, 3682–3690 (2008).PubMed 
Article 

Google Scholar 
36.Sutton, G. P., Clarke, D., Morley, E. L. & Robert, D. Mechanosensory hairs in bumblebees (Bombus terrestris) detect weak electric fields. Proc. Natl. Acad. Sci. 113, 7261–7265 (2016).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
37.Wędzony, M. & Filek, M. Changes of electric potential in pistils of Petunia hybrida Hort. and Brassica napus L. during pollination. Acta Physiol. Plantarum 20, 291–297 (1998).Article 

Google Scholar 
38.Stout, J. C. & Goulson, D. The use of conspecific and interspecific scent marks by foraging bumblebees and honeybees. Anim. Behav. 62, 183–189 (2001).Article 

Google Scholar 
39.Weiss, M. R. Floral color change: A widespread functional convergence. Am. J. Bot. 82, 167–185 (1995).Article 

Google Scholar 
40.Waser, N. M. & Price, M. V. Pollinator behaviour and natural selection for flower colour in Delphinium nelsonii. Nature 302, 422 (1983).ADS 
Article 

Google Scholar 
41.Shimozawa, T., Murakami, J. & Kumagai, T. Sensors and Sensing in Biology and Engineering 145–157 (Springer, 2003).Book 

Google Scholar 
42.Khan, S. & Hanif, H. Diversity and fauna of hoverflies (Syrphidae) in Chakwal, Pakistan. Int. J of Zool. Stud. 1, 22–23 (2016).
Google Scholar 
43.Khan, S. A. & Hanif, H. First record and redescription of Cheilosia albipila syrphid flies from Punjab, Pakistan. Int. J. Zool. Res. 1, 2 (2016).
Google Scholar 
44.Shehzad, A. et al. Faunistic study of hover flies (Diptera: Syrphidae) of Pakistan. Orient. Insects 51, 197–220 (2017).Article 

Google Scholar 
45.Nicholas, S., Thyselius, M., Holden, M. & Nordström, K. Rearing and long-term maintenance of eristalis tenax hoverflies for research studies. JoVE https://doi.org/10.3791/57711 (2018).Article 
PubMed 
PubMed Central 

Google Scholar 
46.Gilbert, F. S. Foraging ecology of hoverflies: Morphology of the mouthparts in relation to feeding on nectar and pollen in some urban species. Ecol. Entomol. 2, 2 (1981).
Google Scholar 
47.Nicholas, S., Thyselius, M., Holden, M. & Nordström, K. Rearing and long-term maintenance of Eristalis tenax hoverflies for research studies. J. Vis. Exp. JoVE 2, 2 (2018).
Google Scholar 
48.Hogg, B. N., Bugg, R. L. & Daane, K. M. Attractiveness of common insectary and harvestable floral resources to beneficial insects. Biol. Control 56, 76–84 (2011).Article 

Google Scholar 
49.McGonigle, D. F., Jackson, C. W. & Davidson, J. L. Triboelectrification of houseflies (Musca domestica L.) walking on synthetic dielectric surfaces. J. Electrostat. 54, 167–177 (2002).Article 

Google Scholar 
50.Koh, K., Montgomery, C., Clarke, D., Morley, E. & Robert, D. in Journal of Physics: Conference Series. 012001 (IOP Publishing).51.Rycroft, M., Israelsson, S. & Price, C. The global atmospheric electric circuit, solar activity and climate change. J. Atmos. Solar Terr. Phys. 62, 1563–1576 (2000).ADS 
CAS 
Article 

Google Scholar 
52.Whitney, H. M., Dyer, A., Chittka, L., Rands, S. A. & Glover, B. J. The interaction of temperature and sucrose concentration on foraging preferences in bumblebees. Naturwissenschaften 95, 845–850 (2008).ADS 
CAS 
PubMed 
Article 

Google Scholar 
53.Stanković, B. & Davies, E. Both action potentials and variation potentials induce proteinase inhibitor gene expression in tomato. FEBS Lett. 390, 275–279 (1996).PubMed 
Article 

Google Scholar  More

1.Pucciarelli, S. et al. Molecular cold-adaptation of protein function and gene regulation: the case for comparative genomic analyses in marine ciliated protozoa. Mar Genomics 2, 57–66. https://doi.org/10.1016/j.margen.2009.03.008 (2009).Article 
PubMed 

Google Scholar 
2.Pucciarelli, S., Marziale, F., Di Giuseppe, G., Barchetta, S. & Miceli, C. Ribosomal cold-adaptation: characterization of the genes encoding the acidic ribosomal P0 and P2 proteins from the Antarctic ciliate Euplotes focardii. Gene 360, 103–110. https://doi.org/10.1016/j.gene.2005.06.007 (2005).CAS 
Article 
PubMed 

Google Scholar 
3.Pucciarelli, S. & Miceli, C. Characterization of the cold-adapted alpha-tubulin from the psychrophilic ciliate Euplotes focardii. Extremophiles 6, 385–389. https://doi.org/10.1007/s00792-002-0268-5 (2002).CAS 
Article 
PubMed 

Google Scholar 
4.Yang, G. et al. Characterization and comparative analysis of psychrophilic and mesophilic alpha-amylases from Euplotes species: a contribution to the understanding of enzyme thermal adaptation. Biochem Biophys Res Commun 438, 715–720. https://doi.org/10.1016/j.bbrc.2013.07.113 (2013).CAS 
Article 
PubMed 

Google Scholar 
5.Prescott, D. M. The DNA of ciliated protozoa. Microbiol Rev 58, 233–267 (1994).CAS 
Article 

Google Scholar 
6.Mollenbeck, M. & Klobutcher, L. A. De novo telomere addition to spacer sequences prior to their developmental degradation in Euplotes crassus. Nucleic Acids Res 30, 523–531 (2002).Article 

Google Scholar 
7.Swart, E. C. et al. The Oxytricha trifallax macronuclear genome: a complex eukaryotic genome with 16,000 tiny chromosomes. PLoS Biol 11, e1001473. https://doi.org/10.1371/journal.pbio.1001473 (2013).CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
8.Heyse, G., Jonsson, F., Chang, W. J. & Lipps, H. J. RNA-dependent control of gene amplification. Proc Natl Acad Sci U S A 107, 22134–22139. https://doi.org/10.1073/pnas.1009284107 (2010).ADS 
Article 
PubMed 
PubMed Central 

Google Scholar 
9.Nowacki, M., Haye, J. E., Fang, W., Vijayan, V. & Landweber, L. F. RNA-mediated epigenetic regulation of DNA copy number. Proc Natl Acad Sci U S A 107, 22140–22144. https://doi.org/10.1073/pnas.1012236107 (2010).ADS 
CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
10.Dayeh, V. R. et al. Comparing a ciliate and a fish cell line for their sensitivity to several classes of toxicants by the novel application of multiwell filter plates to Tetrahymena. Res Microbiol 156, 93–103. https://doi.org/10.1016/j.resmic.2004.08.005 (2005).ADS 
CAS 
Article 
PubMed 

Google Scholar 
11.Detrich, H. W., 3rd, Parker, S. K., Williams, R. C., Jr., Nogales, E. & Downing, K. H. Cold adaptation of microtubule assembly and dynamics. Structural interpretation of primary sequence changes present in the alpha- and beta-tubulins of Antarctic fishes. J Biol Chem 275, 37038–37047. https://doi.org/10.1074/jbc.M005699200 (2000).12.Manka, S. W. & Moores, C. A. Microtubule structure by cryo-EM: snapshots of dynamic instability. Essays Biochem 62, 737–751. https://doi.org/10.1042/EBC20180031 (2018).Article 
PubMed 
PubMed Central 

Google Scholar 
13.Inclan, Y. F. & Nogales, E. Structural models for the self-assembly and microtubule interactions of gamma-, delta- and epsilon-tubulin. J Cell Sci 114, 413–422 (2001).CAS 
Article 

Google Scholar 
14.Chiappori, F. et al. Structural thermal adaptation of beta-tubulins from the Antarctic psychrophilic protozoan Euplotes focardii. Proteins 80, 1154–1166. https://doi.org/10.1002/prot.24016 (2012).CAS 
Article 
PubMed 

Google Scholar 
15.Marziale, F. et al. Different roles of two gamma-tubulin isotypes in the cytoskeleton of the Antarctic ciliate Euplotes focardii: remodelling of interaction surfaces may enhance microtubule nucleation at low temperature. FEBS J 275, 5367–5382. https://doi.org/10.1111/j.1742-4658.2008.06666.x (2008).CAS 
Article 
PubMed 

Google Scholar 
16.Pucciarelli, S., Miceli, C. & Melki, R. Heterologous expression and folding analysis of a beta-tubulin isotype from the Antarctic ciliate Euplotes focardii. Eur J Biochem 269, 6271–6277 (2002).CAS 
Article 

Google Scholar 
17.Gromer, S., Urig, S. & Becker, K. The thioredoxin system–from science to clinic. Med Res Rev 24, 40–89. https://doi.org/10.1002/med.10051 (2004).CAS 
Article 
PubMed 

Google Scholar 
18.Birben, E., Sahiner, U. M., Sackesen, C., Erzurum, S. & Kalayci, O. Oxidative stress and antioxidant defense. World Allergy Organ J 5, 9–19. https://doi.org/10.1097/WOX.0b013e3182439613 (2012).CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
19.Alin, P., Danielson, U. H. & Mannervik, B. 4-Hydroxyalk-2-enals are substrates for glutathione transferase. FEBS Lett 179, 267–270 (1985).CAS 
Article 

Google Scholar 
20.Juganson, K. et al. Mechanisms of toxic action of silver nanoparticles in the protozoan Tetrahymena thermophila: From gene expression to phenotypic events. Environ Pollut 225, 481–489. https://doi.org/10.1016/j.envpol.2017.03.013 (2017).CAS 
Article 
PubMed 

Google Scholar 
21.Clark, M. S., Fraser, K. P. & Peck, L. S. Antarctic marine molluscs do have an HSP70 heat shock response. Cell Stress Chaperones 13, 39–49. https://doi.org/10.1007/s12192-008-0014-8 (2008).CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
22.Tomanek, L. The heat-shock response: its variation, regulation and ecological importance in intertidal gastropods (genus Tegula). Integr Comp Biol 42, 797–807. https://doi.org/10.1093/icb/42.4.797 (2002).CAS 
Article 
PubMed 

Google Scholar 
23.Morimoto, R. I., Kline, M. P., Bimston, D. N. & Cotto, J. J. The heat-shock response: regulation and function of heat-shock proteins and molecular chaperones. Essays Biochem 32, 17–29 (1997).CAS 
PubMed 

Google Scholar 
24.Gonzalez-Aravena, M. et al. HSP70 from the Antarctic sea urchin Sterechinus neumayeri: molecular characterization and expression in response to heat stress. Biol Res 51, 8. https://doi.org/10.1186/s40659-018-0156-9 (2018).CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
25.Hofmann, G. E., Buckley, B. A., Airaksinen, S., Keen, J. E. & Somero, G. N. Heat-shock protein expression is absent in the antarctic fish Trematomus bernacchii (family Nototheniidae). J Exp Biol 203, 2331–2339 (2000).CAS 
Article 

Google Scholar 
26.La Terza, A., Papa, G., Miceli, C. & Luporini, P. Divergence between two Antarctic species of the ciliate Euplotes, E. focardii and E. nobilii, in the expression of heat-shock protein 70 genes. Mol Ecol 10, 1061–1067. https://doi.org/10.1046/j.1365-294x.2001.01242.x (2001).27.Klobutcher, L. A. & Farabaugh, P. J. Shifty ciliates: frequent programmed translational frameshifting in euplotids. Cell 111, 763–766 (2002).CAS 
Article 

Google Scholar 
28.Lobanov, A. V. et al. Position-dependent termination and widespread obligatory frameshifting in Euplotes translation. Nat Struct Mol Biol 24, 61–68. https://doi.org/10.1038/nsmb.3330 (2017).CAS 
Article 
PubMed 

Google Scholar 
29.Coordinators, N. R. Database resources of the National Center for Biotechnology Information. Nucleic Acids Res 45, D12–D17. https://doi.org/10.1093/nar/gkw1071 (2017).CAS 
Article 

Google Scholar 
30.Pucciarelli, S. et al. Microbial consortium associated with the antarctic marine ciliate Euplotes focardii: an investigation from genomic sequences. Microb Ecol 70, 484–497. https://doi.org/10.1007/s00248-015-0568-9 (2015).CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
31.Klobutcher, L. A. et al. Conserved DNA sequences adjacent to chromosome fragmentation and telomere addition sites in Euplotes crassus. Nucleic Acids Res 26, 4230–4240. https://doi.org/10.1093/nar/26.18.4230 (1998).CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
32.Aeschlimann, S. H. et al. The draft assembly of the radically organized Stylonychia lemnae macronuclear genome. Genome Biol Evol 6, 1707–1723. https://doi.org/10.1093/gbe/evu139 (2014).CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
33.Swart, E. C. (personal communication).34.Cavalcanti, A. R., Stover, N. A., Orecchia, L., Doak, T. G. & Landweber, L. F. Coding properties of Oxytricha trifallax (Sterkiella histriomuscorum) macronuclear chromosomes: analysis of a pilot genome project. Chromosoma 113, 69–76. https://doi.org/10.1007/s00412-004-0295-3 (2004).CAS 
Article 
PubMed 

Google Scholar 
35.Lozupone, C. A., Knight, R. D. & Landweber, L. F. The molecular basis of nuclear genetic code change in ciliates. Curr Biol 11, 65–74. https://doi.org/10.1016/s0960-9822(01)00028-8 (2001).CAS 
Article 
PubMed 

Google Scholar 
36.Salas-Marco, J. et al. Distinct paths to stop codon reassignment by the variant-code organisms Tetrahymena and Euplotes. Mol Cell Biol 26, 438–447. https://doi.org/10.1128/MCB.26.2.438-447.2006 (2006).CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
37.Klobutcher, L. A. Sequencing of random Euplotes crassus macronuclear genes supports a high frequency of +1 translational frameshifting. Eukaryot Cell 4, 2098–2105. https://doi.org/10.1128/EC.4.12.2098-2105.2005 (2005).CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
38.Wang, R., Xiong, J., Wang, W., Miao, W. & Liang, A. High frequency of +1 programmed ribosomal frameshifting in Euplotes octocarinatus. Sci Rep 6, 21139. https://doi.org/10.1038/srep21139 (2016).ADS 
CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
39.Turanov, A. A. et al. Genetic code supports targeted insertion of two amino acids by one codon. Science 323, 259–261. https://doi.org/10.1126/science.1164748 (2009).CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
40.Maehigashi, T., Dunkle, J. A., Miles, S. J. & Dunham, C. M. Structural insights into +1 frameshifting promoted by expanded or modification-deficient anticodon stem loops. Proc Natl Acad Sci U S A 111, 12740–12745. https://doi.org/10.1073/pnas.1409436111 (2014).ADS 
CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
41.Miceli, C., Ballarini, P., Di Giuseppe, G., Valbonesi, A. & Luporini, P. Identification of the tubulin gene family and sequence determination of one beta-tubulin gene in a cold-poikilotherm protozoan, the antarctic ciliate Euplotes focardii. J Eukaryot Microbiol 41, 420–427. https://doi.org/10.1111/j.1550-7408.1994.tb06100.x (1994).CAS 
Article 
PubMed 

Google Scholar 
42.Ricci, F. et al. The sub-chromosomic macronuclear pheromone genes of the ciliate Euplotes raikovi: comparative structural analysis and insights into the mechanism of expression. J Eukaryot Microbiol 66, 376–384. https://doi.org/10.1111/jeu.12677 (2019).CAS 
Article 
PubMed 

Google Scholar 
43.Wang, R., Liu, J., Di Giuseppe, G. & Liang, A. UAA and UAG may Encode Amino Acid in Cathepsin B Gene of Euplotes octocarinatus. J Eukaryot Microbiol 67, 144–149. https://doi.org/10.1111/jeu.12755 (2020).CAS 
Article 
PubMed 

Google Scholar 
44.Heaphy, S. M., Mariotti, M., Gladyshev, V. N., Atkins, J. F. & Baranov, P. V. Novel ciliate genetic code variants including the reassignment of all three stop codons to sense codons in condylostoma magnum. Mol Biol Evol 33, 2885–2889. https://doi.org/10.1093/molbev/msw166 (2016).CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
45.Swart, E. C., Serra, V., Petroni, G. & Nowacki, M. Genetic codes with no dedicated stop codon: context-dependent translation termination. Cell 166, 691–702. https://doi.org/10.1016/j.cell.2016.06.020 (2016).CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
46.Roy, B., Leszyk, J. D., Mangus, D. A. & Jacobson, A. Nonsense suppression by near-cognate tRNAs employs alternative base pairing at codon positions 1 and 3. Proc Natl Acad Sci U S A 112, 3038–3043. https://doi.org/10.1073/pnas.1424127112 (2015).ADS 
CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
47.Dunn, J. G., Foo, C. K., Belletier, N. G., Gavis, E. R. & Weissman, J. S. Ribosome profiling reveals pervasive and regulated stop codon readthrough in Drosophila melanogaster. Elife 2, e01179. https://doi.org/10.7554/eLife.01179 (2013).Article 
PubMed 
PubMed Central 

Google Scholar 
48.Frechin, M., Duchene, A. M. & Becker, H. D. Translating organellar glutamine codons: a case by case scenario?. RNA Biol 6, 31–34. https://doi.org/10.4161/rna.6.1.7564 (2009).CAS 
Article 
PubMed 

Google Scholar 
49.Wilcox, M. & Nirenberg, M. Transfer RNA as a cofactor coupling amino acid synthesis with that of protein. Proc Natl Acad Sci U S A 61, 229–236. https://doi.org/10.1073/pnas.61.1.229 (1968).ADS 
CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
50.Detrich, H. W. 3rd., Fitzgerald, T. J., Dinsmore, J. H. & Marchese-Ragona, S. P. Brain and egg tubulins from antarctic fishes are functionally and structurally distinct. J Biol Chem 267, 18766–18775 (1992).CAS 
Article 

Google Scholar 
51.Detrich, H. W. 3rd., Johnson, K. A. & Marchese-Ragona, S. P. Polymerization of Antarctic fish tubulins at low temperatures: energetic aspects. Biochemistry 28, 10085–10093 (1989).CAS 
Article 

Google Scholar 
52.Wloga, D. et al. Glutamylation on alpha-tubulin is not essential but affects the assembly and functions of a subset of microtubules in Tetrahymena thermophila. Eukaryot Cell 7, 1362–1372. https://doi.org/10.1128/EC.00084-08 (2008).CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
53.Eisen, J. A. et al. Macronuclear genome sequence of the ciliate Tetrahymena thermophila, a model eukaryote. PLoS Biol 4, e286. https://doi.org/10.1371/journal.pbio.0040286 (2006).CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
54.Aury, J. M. et al. Global trends of whole-genome duplications revealed by the ciliate Paramecium tetraurelia. Nature 444, 171–178. https://doi.org/10.1038/nature05230 (2006).ADS 
CAS 
Article 
PubMed 

Google Scholar 
55.Pucciarelli, S. et al. Distinct functional roles of beta-tubulin isotypes in microtubule arrays of Tetrahymena thermophila, a model single-celled organism. PLoS ONE 7, e39694. https://doi.org/10.1371/journal.pone.0039694 (2012).ADS 
CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
56.Pucciarelli, S. et al. Tubulin folding: the special case of a beta-tubulin isotype from the Antarctic psychrophilic ciliate Euplotes focardii. Polar Biol 36, 1833–1838. https://doi.org/10.1007/s00300-013-1390-9 (2013).Article 

Google Scholar 
57.Pucci, F. & Rooman, M. Physical and molecular bases of protein thermal stability and cold adaptation. Curr Opin Struct Biol 42, 117–128. https://doi.org/10.1016/j.sbi.2016.12.007 (2017).CAS 
Article 
PubMed 

Google Scholar 
58.Aqvist, J., Isaksen, G. V. & Brandsdal, B. O. Computation of enzyme cold adaptation. Nat Rev Chem 1, 0051. https://doi.org/10.1038/s41570-017-0051 (2017).CAS 
Article 

Google Scholar 
59.Lesser, M. P. Oxidative stress in marine environments: biochemistry and physiological ecology. Annu Rev Physiol 68, 253–278. https://doi.org/10.1146/annurev.physiol.68.040104.110001 (2006).CAS 
Article 
PubMed 

Google Scholar 
60.McCord, J. M. & Fridovich, I. Superoxide dismutase. An enzymic function for erythrocuprein (hemocuprein). J Biol Chem 244, 6049–6055 (1969).61.McCord, J. M. & Fridovich, I. Superoxide dismutase: the first twenty years (1968–1988). Free Radic Biol Med 5, 363–369 (1988).CAS 
Article 

Google Scholar 
62.Miller, A. F. Superoxide dismutases: ancient enzymes and new insights. FEBS Lett 586, 585–595. https://doi.org/10.1016/j.febslet.2011.10.048 (2012).CAS 
Article 
PubMed 

Google Scholar 
63.Benov, L. T. & Fridovich, I. Escherichia coli expresses a copper- and zinc-containing superoxide dismutase. J Biol Chem 269, 25310–25314 (1994).CAS 
Article 

Google Scholar 
64.Steinman, H. M. & Ely, B. Copper-zinc superoxide dismutase of Caulobacter crescentus: cloning, sequencing, and mapping of the gene and periplasmic location of the enzyme. J Bacteriol 172, 2901–2910. https://doi.org/10.1128/jb.172.6.2901-2910.1990 (1990).CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
65.Antonyuk, S. V., Strange, R. W., Marklund, S. L. & Hasnain, S. S. The structure of human extracellular copper-zinc superoxide dismutase at 1.7 A resolution: insights into heparin and collagen binding. J Mol Biol 388, 310–326. https://doi.org/10.1016/j.jmb.2009.03.026 (2009).66.Marklund, S. L. Extracellular superoxide dismutase and other superoxide dismutase isoenzymes in tissues from nine mammalian species. Biochem J 222, 649–655. https://doi.org/10.1042/bj2220649 (1984).CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
67.Bannister, J. V., Bannister, W. H. & Rotilio, G. Aspects of the structure, function, and applications of superoxide dismutase. CRC Crit Rev Biochem 22, 111–180 (1987).CAS 
Article 

Google Scholar 
68.James, E. R. Superoxide dismutase. Parasitol Today 10, 481–484. https://doi.org/10.1016/0169-4758(94)90161-9 (1994).CAS 
Article 
PubMed 

Google Scholar 
69.Ferro, D. et al. Cu, Zn superoxide dismutases from Tetrahymena thermophila: molecular evolution and gene expression of the first line of antioxidant defenses. Protist 166, 131–145. https://doi.org/10.1016/j.protis.2014.12.003 (2015).CAS 
Article 
PubMed 

Google Scholar 
70.Arnaiz, O. & Sperling, L. ParameciumDB in 2011: new tools and new data for functional and comparative genomics of the model ciliate Paramecium tetraurelia. Nucleic Acids Res 39, D632-636. https://doi.org/10.1093/nar/gkq918 (2011).CAS 
Article 
PubMed 

Google Scholar 
71.Fink, R. C. & Scandalios, J. G. Molecular evolution and structure–function relationships of the superoxide dismutase gene families in angiosperms and their relationship to other eukaryotic and prokaryotic superoxide dismutases. Arch Biochem Biophys 399, 19–36. https://doi.org/10.1006/abbi.2001.2739 (2002).CAS 
Article 
PubMed 

Google Scholar 
72.Lee, Y. M., Friedman, D. J. & Ayala, F. J. Superoxide dismutase: an evolutionary puzzle. Proc Natl Acad Sci U S A 82, 824–828. https://doi.org/10.1073/pnas.82.3.824 (1985).ADS 
CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
73.Pischedda, A. et al. Antarctic marine ciliates under stress: superoxide dismutases from the psychrophilic Euplotes focardii are cold-active yet heat tolerant enzymes. Sci Rep 8, 14721. https://doi.org/10.1038/s41598-018-33127-1 (2018).ADS 
CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
74.Yang, G. et al. Characterization of the first eukaryotic cold-adapted patatin-like phospholipase from the psychrophilic Euplotes focardii: Identification of putative determinants of thermal-adaptation by comparison with the homologous protein from the mesophilic Euplotes crassus. Biochimie 95, 1795–1806. https://doi.org/10.1016/j.biochi.2013.06.008 (2013).CAS 
Article 
PubMed 

Google Scholar 
75.Li, J., Zhou, L., Lin, X., Yi, Z. & Al-Rasheid, K. A. Characterizing dose-responses of catalase to nitrofurazone exposure in model ciliated protozoan Euplotes vannus for ecotoxicity assessment: enzyme activity and mRNA expression. Ecotoxicol Environ Saf 100, 294–302. https://doi.org/10.1016/j.ecoenv.2013.08.021 (2014).CAS 
Article 
PubMed 

Google Scholar 
76.Prast-Nielsen, S., Huang, H. H. & Williams, D. L. Thioredoxin glutathione reductase: its role in redox biology and potential as a target for drugs against neglected diseases. Biochim Biophys Acta 1262–1271, 2011. https://doi.org/10.1016/j.bbagen.2011.06.024 (1810).CAS 
Article 

Google Scholar 
77.Kabani, M. & Martineau, C. N. Multiple hsp70 isoforms in the eukaryotic cytosol: mere redundancy or functional specificity?. Curr Genomics 9, 338–248. https://doi.org/10.2174/138920208785133280 (2008).CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
78.La Terza, A., Miceli, C. & Luporini, P. The gene for the heat-shock protein 70 of Euplotes focardii, an Antarctic psychrophilic ciliate. Antarct. Sci. 16, 23–28. https://doi.org/10.1017/S0954102004001774 (2004).ADS 
Article 

Google Scholar 
79.Chen, X. et al. Genome analyses of the new model protist Euplotes vannus focusing on genome rearrangement and resistance to environmental stressors. Mol Ecol Resour 19, 1292–1308. https://doi.org/10.1111/1755-0998.13023 (2019).CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
80.Chen, Z. et al. Transcriptomic and genomic evolution under constant cold in Antarctic notothenioid fish. Proc Natl Acad Sci U S A 105, 12944–12949. https://doi.org/10.1073/pnas.0802432105 (2008).ADS 
Article 
PubMed 
PubMed Central 

Google Scholar 
81.Li, Y. et al. Comparative transcriptomic analysis reveals gene expression associated with cold adaptation in the tea plant Camellia sinensis. BMC Genomics 20, 624. https://doi.org/10.1186/s12864-019-5988-3 (2019).CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
82.Bolger, A. M., Lohse, M. & Usadel, B. Trimmomatic: a flexible trimmer for Illumina sequence data. Bioinformatics 30, 2114–2120. https://doi.org/10.1093/bioinformatics/btu170 (2014).CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
83.Andrews, S. (2010).84.Bankevich, A. et al. SPAdes: a new genome assembly algorithm and its applications to single-cell sequencing. J Comput Biol 19, 455–477. https://doi.org/10.1089/cmb.2012.0021 (2012).MathSciNet 
CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
85.Nikolenko, S. I., Korobeynikov, A. I. & Alekseyev, M. A. BayesHammer: Bayesian clustering for error correction in single-cell sequencing. BMC Genomics 14 Suppl 1, S7. https://doi.org/10.1186/1471-2164-14-S1-S7 (2013).86.Gurevich, A., Saveliev, V., Vyahhi, N. & Tesler, G. QUAST: quality assessment tool for genome assemblies. Bioinformatics 29, 1072–1075. https://doi.org/10.1093/bioinformatics/btt086 (2013).CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
87.Boscaro, V., Husnik, F., Vannini, C. & Keeling, P. J. Symbionts of the ciliate Euplotes: diversity, patterns and potential as models for bacteria-eukaryote endosymbioses. Proc Biol Sci 286, 20190693. https://doi.org/10.1098/rspb.2019.0693 (2019).Article 
PubMed 
PubMed Central 

Google Scholar 
88.Serra, V. et al. Morphology, ultrastructure, genomics, and phylogeny of Euplotes vanleeuwenhoeki sp. nov. and its ultra-reduced endosymbiont “Candidatus Pinguicoccus supinus” sp. nov. Sci Rep 10, 20311. https://doi.org/10.1038/s41598-020-76348-z (2020).89.Stanke, M. et al. AUGUSTUS: ab initio prediction of alternative transcripts. Nucleic Acids Res 34, W435-439. https://doi.org/10.1093/nar/gkl200 (2006).CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
90.Li, B. & Dewey, C. N. RSEM: accurate transcript quantification from RNA-Seq data with or without a reference genome. BMC Bioinformatics 12, 323. https://doi.org/10.1186/1471-2105-12-323 (2011).CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
91.Conesa, A. et al. Blast2GO: a universal tool for annotation, visualization and analysis in functional genomics research. Bioinformatics 21, 3674–3676. https://doi.org/10.1093/bioinformatics/bti610 (2005).CAS 
Article 
PubMed 

Google Scholar 
92.Gotz, S. et al. High-throughput functional annotation and data mining with the Blast2GO suite. Nucleic Acids Res 36, 3420–3435. https://doi.org/10.1093/nar/gkn176 (2008).CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
93.Parra, G., Bradnam, K. & Korf, I. CEGMA: a pipeline to accurately annotate core genes in eukaryotic genomes. Bioinformatics 23, 1061–1067. https://doi.org/10.1093/bioinformatics/btm071 (2007).CAS 
Article 
PubMed 

Google Scholar 
94.Laslett, D. & Canback, B. ARAGORN, a program to detect tRNA genes and tmRNA genes in nucleotide sequences. Nucleic Acids Res 32, 11–16. https://doi.org/10.1093/nar/gkh152 (2004).CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
95.Gruber, A. R., Lorenz, R., Bernhart, S. H., Neubock, R. & Hofacker, I. L. The Vienna RNA websuite. Nucleic Acids Res 36, W70-74. https://doi.org/10.1093/nar/gkn188 (2008).CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
96.Popenda, M. et al. Automated 3D structure composition for large RNAs. Nucleic Acids Res 40, e112. https://doi.org/10.1093/nar/gks339 (2012).CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
97.Fu, L., Niu, B., Zhu, Z., Wu, S. & Li, W. CD-HIT: accelerated for clustering the next-generation sequencing data. Bioinformatics 28, 3150–3152. https://doi.org/10.1093/bioinformatics/bts565 (2012).CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
98.Li, W. & Godzik, A. Cd-hit: a fast program for clustering and comparing large sets of protein or nucleotide sequences. Bioinformatics 22, 1658–1659. https://doi.org/10.1093/bioinformatics/btl158 (2006).CAS 
Article 
PubMed 

Google Scholar 
99.Shigematsu, M. et al. YAMAT-seq: an efficient method for high-throughput sequencing of mature transfer RNAs. Nucleic Acids Res 45, e70. https://doi.org/10.1093/nar/gkx005 (2017).CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
100.Bushnell, B., Rood, J. & Singer, E. BBMerge: accurate paired shotgun read merging via overlap. PLoS ONE 12, e0185056. https://doi.org/10.1371/journal.pone.0185056 (2017).CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
101.Martin, M. Cutadapt removes adapter sequences from high-throughput sequencing reads. 2011 17, 3. https://doi.org/10.14806/ej.17.1.200 (2011).102.Holmes, A. D., Howard, J. M., Chan, P. P. & Lowe, T. M. tRNA Analysis of eXpression (tRAX): A tool for integrating analysis of tRNAs, tRNA-derived small RNAs, and tRNA modifications. (Submitted) (2020).103.Sievers, F. & Higgins, D. G. Clustal omega. Curr Protoc Bioinformatics 48, 3 13 11–16. https://doi.org/10.1002/0471250953.bi0313s48 (2014).104.Kumar, S., Stecher, G., Li, M., Knyaz, C. & Tamura, K. MEGA X: molecular evolutionary genetics analysis across computing platforms. Mol Biol Evol 35, 1547–1549. https://doi.org/10.1093/molbev/msy096 (2018).CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
105.Webb, B. & Sali, A. Comparative protein structure modeling using MODELLER. Curr Protoc Bioinform. 54, 5 6 1–5 6 37. https://doi.org/10.1002/cpbi.3 (2016).106.Waterhouse, A. et al. SWISS-MODEL: homology modelling of protein structures and complexes. Nucleic Acids Res 46, W296–W303. https://doi.org/10.1093/nar/gky427 (2018).CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
107.Ichikawa, M. et al. Tubulin lattice in cilia is in a stressed form regulated by microtubule inner proteins. Proc Natl Acad Sci U S A 116, 19930–19938. https://doi.org/10.1073/pnas.1911119116 (2019).CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
108.Chaaban, S. et al. The Structure and Dynamics of C. elegans Tubulin Reveals the Mechanistic Basis of Microtubule Growth. Dev Cell 47, 191–204 e198. https://doi.org/10.1016/j.devcel.2018.08.023 (2018).109.Kikkawa, M. et al. Switch-based mechanism of kinesin motors. Nature 411, 439–445. https://doi.org/10.1038/35078000 (2001).ADS 
CAS 
Article 
PubMed 

Google Scholar 
110.Howes, S. C. et al. Structural differences between yeast and mammalian microtubules revealed by cryo-EM. J Cell Biol 216, 2669–2677. https://doi.org/10.1083/jcb.201612195 (2017).CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
111.Ma, M. et al. Structure of the Decorated Ciliary Doublet Microtubule. Cell 179, 909–922 e912. https://doi.org/10.1016/j.cell.2019.09.030 (2019).112.Abraham, M. J. et al. GROMACS: High performance molecular simulations through multi-level parallelism from laptops to supercomputers. SoftwareX 1–2, 19–25. https://doi.org/10.1016/j.softx.2015.06.001 (2015).ADS 
Article 

Google Scholar 
113.Morrison, T. B., Weis, J. J. & Wittwer, C. T. Quantification of low-copy transcripts by continuous SYBR Green I monitoring during amplification. Biotechniques 24, 954–958, 960, 962 (1998).114.Pfaffl, M. W. A new mathematical model for relative quantification in real-time RT-PCR. Nucleic Acids Res 29, e45. https://doi.org/10.1093/nar/29.9.e45 (2001).CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
115.Pfaffl, M. W., Horgan, G. W. & Dempfle, L. Relative expression software tool (REST) for group-wise comparison and statistical analysis of relative expression results in real-time PCR. Nucleic Acids Res 30, e36. https://doi.org/10.1093/nar/30.9.e36 (2002).Article 
PubMed 
PubMed Central 

Google Scholar  More

1.Beauchamp, G. Long-distance migrating species of birds travel in larger groups. Biol. Lett. 7, 692–694 (2011).PubMed 
PubMed Central 
Article 

Google Scholar 
2.Watts, H. E., Cornelius, J. M., Fudickar, A. M., Pérez, J. & Ramenofsky, M. Understanding variation in migratory movements: A mechanistic approach. Gen. Comp. Endocrinol. 256, 112–122 (2018).CAS 
PubMed 
Article 

Google Scholar 
3.Amezaga, J. M., Santamaría, L. & Green, A. J. Biotic wetland connectivity—Supporting a new approach for wetland policy. Acta Oecol. 23, 213–222 (2002).ADS 
Article 

Google Scholar 
4.O’Connell, M. Threats to waterbirds and wetlands: Implications for conservation, inventory and research. Wildfowl 51, 1–16 (2000).
Google Scholar 
5.Darrah, S. E. et al. Improvements to the wetland extent trends (WET) index as a tool for monitoring natural and human-made wetlands. Ecol. Ind. 99, 294–298 (2019).Article 

Google Scholar 
6.BirdLife International. Waterbirds are Showing Widespread Declines, Particularly in Asia. http://www.birdlife.org (2017).7.Maron, M. et al. The many meanings of no net loss in environmental policy. Nat. Sustain. 1, 19–27 (2018).Article 

Google Scholar 
8.He, Q. Conservation: ‘No net loss’ of wetland quantity and quality. Curr. Biol. 29, R1070–R1072 (2019).CAS 
PubMed 
Article 

Google Scholar 
9.Mander, L., Marie-Orleach, L. & Elliott, M. The value of wader foraging behaviour study to assess the success of restored intertidal areas. Estuar. Coast. Shelf Sci. 131, 1–5 (2013).ADS 
Article 

Google Scholar 
10.Choi, C., Gan, X., Hua, N., Wang, Y. & Ma, Z. The habitat use and home range analysis of Dunlin (Calidris alpina) in Chongming Dongtan, China and their conservation implications. Wetlands 34, 255–266 (2014).Article 

Google Scholar 
11.Xia, S. et al. Identifying priority sites and gaps for the conservation of migratory waterbirds in China’s coastal wetlands. Biol. Cons. 210, 72–82 (2017).Article 

Google Scholar 
12.Ramsar Sites Information Service. Mai Po Marshes and Inner Deep Bay. https://rsis.ramsar.org/ris/750 (2021).13.Environment Bureau. Hong Kong Biodiversity Strategy Action Plan 2016–2021 (The Government of the Hong Kong Special Administrative Region, 2016).
Google Scholar 
14.Sung, Y. H., Tse, I. W. L. & Yu, Y. T. Population trends of the Black-faced Spoonbill Platalea minor: Analysis of data from international synchronised censuses. Bird Conserv. Int. 28, 157–167. https://doi.org/10.1017/s0959270917000016 (2017).Article 

Google Scholar 
15.Wei, P. et al. Impact of habitat management on waterbirds in a degraded coastal wetland. Mar. Pollut. Bull. 124, 645–652 (2017).CAS 
PubMed 
Article 

Google Scholar 
16.Cheung, S. C. The politics of wetlandscape: Fishery heritage and natural conservation in Hong Kong. Int. J. Herit. Stud. 17, 36–45 (2011).Article 

Google Scholar 
17.AFCD. Agriculture, Fisheries and Conservation Department (AFCD). Marine Fish Culture, Pond Fish Culture and Oyster Culture. https://www.afcd.gov.hk/english/fisheries/fish_aqu/fish_aqu_mpo/fish_aqu_mpo.html.18.Yu, Y. T., Li, C. H., Tse, I. W. L. & Fong, H. N. F. International Black-Faced Spoonbill Census 2019 (The Hong Kong Bird Watching Society, 2019).
Google Scholar 
19.Pickett, E. J. et al. Cryptic and cumulative impacts on the wintering habitat of the endangered black-faced spoonbill (Platalea minor) risk its long-term viability. Environ. Conserv. 45, 147–154. https://doi.org/10.1017/s0376892917000340 (2018).Article 

Google Scholar 
20.The Hong Kong Bird Watching Society. Black-Faced Spoonbill Population Hits Record High. Number in HK Continues to Decline. Protection of Deep Bay in Urgent Need. https://cms.hkbws.org.hk/cms/ (2020).21.Swennen, C. & Yu, Y. T. Food and feeding behavior of the black-faced spoonbill. Waterbirds 28, 19–27. https://doi.org/10.1675/1524-4695(2005)028[0019:Fafbot]2.0.Co;2 (2005).Article 

Google Scholar 
22.Nichols, R. V., Åkesson, M. & Kjellander, P. Diet assessment based on rumen contents: A comparison between DNA metabarcoding and macroscopy. PLoS ONE 11, e0157977 (2016).PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
23.Taberlet, P., Coissac, E., Pompanon, F., Brochmann, C. & Willerslev, E. Towards next-generation biodiversity assessment using DNA metabarcoding. Mol. Ecol. 21, 2045–2050 (2012).CAS 
PubMed 
Article 

Google Scholar 
24.Elbrecht, V., Vamos, E. E., Meissner, K., Aroviita, J. & Leese, F. Assessing strengths and weaknesses of DNA metabarcoding-based macroinvertebrate identification for routine stream monitoring. Methods Ecol. Evol. 8, 1265–1275 (2017).Article 

Google Scholar 
25.McInnes, J. C. et al. Optimised scat collection protocols for dietary DNA metabarcoding in vertebrates. Methods Ecol. Evol. 8, 192–202 (2017).Article 

Google Scholar 
26.Thuo, D. et al. Food from faeces: Evaluating the efficacy of scat DNA metabarcoding in dietary analyses. PLoS ONE 14, e0225805 (2019).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
27.De Sousa, L., Silva, S. M. & Xavier, R. DNA metabarcoding in diet studies: Unveiling ecological aspects in aquatic and terrestrial ecosystem. Environ. DNA 1, 199–214 (2019).Article 

Google Scholar 
28.Ueng, Y. T., Perng, J. J., Wang, J. P., Weng, J. H. & Hou, P. C. Diet of the black-faced spoonbill wintering at Chiku Wetland in Southwestern Taiwan. Waterbirds 29, 185–191 (2006).Article 

Google Scholar 
29.Veen, J., Overdijk, O. & Veen, T. The diet of an endemic subspecies of the Eurasian Spoonbill Platalea leucorodia balsaci, breeding at the Banc d’Arguin, Mauritania. Ardea 100, 123–130 (2012).Article 

Google Scholar 
30.Lee, S. Y. The Mangrove Ecosystem of Deep Bay and the Mai Po Marshes, Hong Kong (Hong Kong University Press, 1999).
Google Scholar 
31.Wong, L. C., Corlett, R. T., Young, L. & Lee, J. S. Comparative feeding ecology of Little Egrets on intertidal mudflats in Hong Kong, South China. Waterbirds 23, 214–225 (2000).
Google Scholar 
32.Yang, K. Y., Lee, S. Y. & Williams, G. A. Selective feeding by the mudskipper (Boleophthalmus pectinirostris) on the microalgal assemblage of a tropical mudflat. Mar. Biol. 143, 245–256 (2003).Article 

Google Scholar 
33.Froese, R., Pauly, D. & eds. FishBase. World Wide Web Electronic Publication. https://www.fishbase.org, version 12/2019 (2019).34.Aguilera, E., Ramo, C. & de le Court, C. Food and feeding sites of the Eurasian spoonbill (Platalea leucorodia) in southwestern Spain. Colon. Waterbirds 19, 159–166 (1996).Article 

Google Scholar 
35.Yu, Y. T. & Swennen, C. K. Habitat use of the black-faced spoonbill. Waterbirds 27, 129–135 (2004).Article 

Google Scholar 
36.World Wide Fund Hong Kong. Mai Po Nature Reserve Habitat Management, Monitoring and Research Plan 2013–2018 (World Wide Fund Hong Kong, 2013).
Google Scholar 
37.Sazima, I. Waterbirds catch and release a poisonous fish at a mudflat in southeastern Australia. Ornithol. Res. 27, 126–128 (2019).Article 

Google Scholar 
38.Marchetti, K. & Price, T. Differences in the foraging of juvenile and adult birds: The importance of developmental constraints. Biol. Rev. 64, 51–70 (1989).Article 

Google Scholar 
39.Jiguet, F. Arthropods in diet of Little Bustards Tetrax tetrax during the breeding season in western France. Bird Study 49, 105–109 (2002).Article 

Google Scholar 
40.Birks, J. D. S. & Dunstone, N. Sex-related differences in the diet of the mink Mustela vison. Ecography 8, 245–252 (1985).Article 

Google Scholar 
41.Mata, V. A. et al. Female dietary bias towards large migratory moths in the European free-tailed bat (Tadarida teniotis). Biol. Lett. 12, 20150988 (2016).PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
42.Carreiro, A. R. et al. Metabarcoding, stables isotopes, and tracking: Unraveling the trophic ecology of a winter-breeding storm petrel (Hydrobates castro) with a multimethod approach. Mar. Biol. 167, 14 (2020).CAS 
Article 

Google Scholar 
43.Rose, L. M. Sex differences in diet and foraging behavior in white-faced capuchins (Cebus capucinus). Int. J. Primatol. 15, 95–114 (1994).Article 

Google Scholar 
44.Beeston, R., Baines, D. & Richardson, M. Seasonal and between-sex differences in the diet of Black Grouse Tetrao tetrix. Bird Study 52, 276–281 (2005).Article 

Google Scholar 
45.Durell, S. L. V. D., Goss-Custard, J. D. & Caldow, R. W. G. Sex-related differences in diet and feeding method in the oystercatcher Haematopus ostralegus. J. Anim. Ecol. 62, 205–215 (1993).Article 

Google Scholar 
46.Faegre, S. K., Nietmann, L., Hannon, P., Ha, J. C. & Ha, R. R. Age-related differences in diet and foraging behavior of the critically endangered Mariana Crow (Corvus kubaryi), with notes on the predation of Coenobita hermit crabs. J. Ornithol. 161, 149–158 (2020).Article 

Google Scholar 
47.Dunn, E. K. Effect of age on the fishing ability of sandwich terns Sterna sandvicensis. Ibis 114, 360–366 (1972).Article 

Google Scholar 
48.Watson, M. J. & Hatch, J. J. Differences in foraging performance between juvenile and adult roseate terns at a pre-migratory staging area. Waterbirds 22, 463–465 (1999).Article 

Google Scholar 
49.AEC Limited. Ecological Monitoring and Adaptive Management Advice Services for Lok Ma Chau and West Rail Wetlands. Lok Ma Chau Habitat Creation and Management Plan (AEC Limited, 2019).
Google Scholar 
50.The Hong Kong Bird Watching Society. Hong Kong Fishpond Conservation Scheme Project. https://cms.hkbws.org.hk/cms/ (2020).51.Miya, M. et al. MiFish, a set of universal PCR primers for metabarcoding environmental DNA from fishes: Detection of more than 230 subtropical marine species. R. Soc. Open Sci. 2, 150088 (2015).ADS 
CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
52.Edgar, R. Search and clustering orders of magnitude faster than BLAST. Bioinformatics 26, 2460–2461. https://doi.org/10.1093/bioinformatics/btq461 (2010).CAS 
Article 
PubMed 

Google Scholar 
53.Martin, M. Cutadapt removes adapter sequences from high-throughput sequencing reads. EMBnet.journal 17, 10–12. https://doi.org/10.14806/ej.17.1.200 (2011).Article 

Google Scholar 
54.Andrews, S., Krueger, F. & Segonds-Pichon, A. FastQC a Quality Control Tool for High Throughput Sequence Data. http://www.bioinformatics.babraham.ac.uk/projects/fastqc/ (2010).55.Rognes, T., Flouri, T., Nichols, B., Quince, C. & Mahe, F. VSEARCH: A versatile open source tool for metagenomics. PeerJ 4, e2584. https://doi.org/10.7717/peerj.2584 (2016).Article 
PubMed 
PubMed Central 

Google Scholar 
56.Edgar, R. C. & Flyvbjerg, H. Error filtering, pair assembly and error correction for next-generation sequencing reads. Bioinformatics (Oxford, England) 31, 3476–3482. https://doi.org/10.1093/bioinformatics/btv401 (2015).CAS 
Article 

Google Scholar 
57.Edgar, R. C. UNOISE2: Improved error-correction for Illumina 16S and ITS amplicon sequencing. BioRxiv https://doi.org/10.1101/081257 (2016).Article 

Google Scholar 
58.Edgar, R. SINTAX: A simple non-Bayesian taxonomy classifier for 16S and ITS sequences. BioRxiv https://doi.org/10.1101/074161 (2016).Article 

Google Scholar 
59.Callahan, B. J. et al. DADA2: High-resolution sample inference from Illumina amplicon data. Nat. Methods 13, 581 (2016).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
60.Machida, R. J., Leray, M., Ho, S. L. & Knowlton, N. Metazoan mitochondrial gene sequence reference datasets for taxonomic assignment of environmental samples. Sci. Data 4, 170027. https://doi.org/10.1038/sdata.2017.27 (2017).CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
61.Sato, K., Miya, M., Fukunaga, T., Sado, T. & Iwasaki, W. MitoFish and MiFish pipeline: A mitochondrial genome database of fish with an analysis pipeline for environmental DNA metabarcoding. Mol. Biol. Evol. 35, 1553–1555 (2018).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
62.Quast, C. et al. The SILVA ribosomal RNA gene database project: Improved data processing and web-based tools. Nucleic Acids Res. 41, D590-596. https://doi.org/10.1093/nar/gks1219 (2013).CAS 
Article 

Google Scholar 
63.Kahlke, T. & Ralph, P. J. BASTA—Taxonomic classification of sequences and sequence bins using last common ancestor estimations. Methods Ecol. Evol. 10, 100–103. https://doi.org/10.1111/2041-210X.13095 (2019).Article 

Google Scholar 
64.Deagle, B. E. et al. Counting with DNA in metabarcoding studies: How should we convert sequence reads to dietary data?. Mol. Ecol. 28, 391–406. https://doi.org/10.1111/mec.14734 (2019).Article 
PubMed 

Google Scholar 
65.Lahti, L. & Shetty, S. Microbiome R Package Version 1.6.0. http://microbiome.github.io (2012).66.Oksanen, J. et al. vegan: Community Ecology Package Version 2.5–6. https://cran.r-project.org, https://github.com/vegandevs/vegan (2019).67.McMurdie, P. J. & Holmes, S. phyloseq: An R package for reproducible interactive analysis and graphics of microbiome census data. PLoS ONE 8, e61217. https://doi.org/10.1371/journal.pone.0061217 (2013).ADS 
CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
68.Martinez-Arbizu, P. pairwiseAdonis: Pairwise Multilevel Comparison Using Adonis. R Package Version 0.3. https://github.com/pmartinezarbizu/pairwiseAdonis (2019).69.Steinberger, A. J. Asteinberger9/seq_scripts: Release v1. https://github.com/asteinberger9/seq_scripts (2018).70.ArcGIS. ArcGIS Version 10.7. https://desktop.arcgis.com/en/arcmap/ (2020). More

Mineralogy and thermal propertiesThe bulk mineral composition of sapropels is detailed in Table 1. The XRD analysis indicates that Amara and Tekirghiol sapropels are enriched in silicates, i.e., quartz (30.8% and 29.1% respectively), plagioclase-albite (10.1% and 8.9%), carbonates, mainly calcite (6.8%) and aragonite (13.1%) in Amara, and calcite (8.7%) in Tekirghiol (Fig. 2). By contrast, Ursu sapropel contains lower concentrations of silicates, mainly quartz (15.4%), plagioclase (5.5% albite and 8% andesine), sulfides, i.e., pyrite (1.5%) and is enriched in halite (34.5%). The major clay components in the sapropels were 2:1 dioactahedral and 2:1 trioctahedral clays, representing 28.9%, 23.6% and 20.8% of clay minerals in Tekirghiol, Amara and Ursu samples, respectively. Muscovite was detected in similar concentrations in Tekirghiol (4.5%) and Amara (4.2%). Quantitative mineralogical clay composition of the fraction  90% in each sample), and kaolinite and chlorite as minor fractions (Table 2; Fig. 3).Table 1 Quantitative bulk mineralogical compositions of saline sapropels collected from Tekirghiol, Amara and Ursu lakes.Full size tableFigure 2X-ray diffraction patterns on the raw mud samples (upper image) collected from the three lakes. The main minerals that contribute to the most important reflections are indicated. Chl: Chlorite, M: Muscovite, K: Kaolinite Group minerals, Q: Quartz, A: Anatase, 2:1: 2:1 phyllosilicate (e.g., illite and smectite), Ca: Calcite, Pl: Plagioclase/Albite/Andesine, R: Rutile, P: Pyrite, Ar: Aragonite, H: Halite.Full size imageTable 2 Quantitative mineralogical clay composition of the fraction  More

1.Lobell, D. B. & Field, C. B. Global scale climate-crop yield relationships and the impacts of recent warming. Environ. Res. Lett. 2, 014002 (2007).ADS 
Article 

Google Scholar 
2.Lobell, D. B. et al. The critical role of extreme heat for maize production in the United States. Nat. Clim. Change 3, 497–501 (2013).ADS 
Article 

Google Scholar 
3.Zhao, C. et al. Temperature increase reduces global yields of major crops in four independent estimates. Proc. Natl Acad. Sci. USA 114, 9326–9331 (2017).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
4.Schlenker, W. & Roberts, M. J. Nonlinear temperature effects indicate severe damages to U.S. crop yields under climate change. Proc. Natl Acad. Sci. USA 106, 15594–15598 (2009).ADS 
CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
5.Vogel, E. et al. The effects of climate extremes on global agricultural yields. Environ. Res. Lett. 14, 054010 (2019).ADS 
Article 

Google Scholar 
6.Lobell, D. B., Bänziger, M., Magorokosho, C. & Vivek, B. Nonlinear heat effects on African maize as evidenced by historical yield trials. Nat. Clim. Change 1, 42–45 (2011).ADS 
Article 

Google Scholar 
7.Urban, D. W., Sheffield, J. & Lobell, D. B. The impacts of future climate and carbon dioxide changes on the average and variability of US maize yields under two emission scenarios. Environ. Res. Lett. 10, 045003 (2015).ADS 
Article 
CAS 

Google Scholar 
8.Prasad, P. V. V. et al. in Response of Crops to Limited Water: Understanding and Modeling Water Stress Effects on Plant Growth Processes (eds Ahuja, L. R. et al.) 301–356 (American Society of Agronomy, Crop Science Society of America, Soil Science Society of America, 2008); https://doi.org/10.2134/advagricsystmodel1.c119.Troy, T. J., Kipgen, C. & Pal, I. The impact of climate extremes and irrigation on US crop yields. Environ. Res. Lett. 10, 054013 (2015).ADS 
Article 

Google Scholar 
10.Carter, E. K., Melkonian, J., Riha, S. J. & Shaw, S. B. Separating heat stress from moisture stress: analyzing yield response to high temperature in irrigated maize. Environ. Res. Lett. 11, 094012 (2016).ADS 
Article 

Google Scholar 
11.Matiu, M., Ankerst, D. P. & Menzel, A. Interactions between temperature and drought in global and regional crop yield variability during 1961-2014. PLoS ONE 12, e0178339 (2017).PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
12.Coffel, E. D. et al. Future hot and dry years worsen Nile Basin water scarcity despite projected precipitation increases. Earth’s Future 7, 967–977 (2019).ADS 
Article 

Google Scholar 
13.Rigden, A. J., Mueller, N. D., Holbrook, N. M., Pillai, N. & Huybers, P. Combined influence of soil moisture and atmospheric evaporative demand is important for accurately predicting US maize yields. Nat. Food 1, 127–133 (2020).Article 

Google Scholar 
14.Schauberger, B. et al. Consistent negative response of US crops to high temperatures in observations and crop models. Nat. Commun. 8, 13931 (2017).ADS 
CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
15.Ortiz-Bobea, A., Wang, H., Carrillo, C. M. & Ault, T. R. Unpacking the climatic drivers of US agricultural yields. Environ. Res. Lett. 14, 064003 (2019).16.Siebert, S., Webber, H., Zhao, G. & Ewert, F. Heat stress is overestimated in climate impact studies for irrigated agriculture. Environ. Res. Lett. 12, 044012 (2017).17.Lesk, C. & Anderson, W. Decadal variability modulates trends in concurrent heat and drought over global croplands. Environ. Res. Lett. 16 055024 (2021).18.Berg, A. et al. Interannual coupling between summertime surface temperature and precipitation over land: processes and implications for climate change. J. Clim. 28, 1308–1328 (2015).ADS 
Article 

Google Scholar 
19.Seneviratne, S. I. et al. Investigating soil moisture–climate interactions in a changing climate: a review. Earth Sci. Rev. 99, 125–161 (2010).ADS 
CAS 
Article 

Google Scholar 
20.Zscheischler, J. & Seneviratne, S. I. Dependence of drivers affects risks associated with compound events. Sci. Adv. 3, e1700263 (2017).21.Trenberth, K. E. & Shea, D. J. Relationships between precipitation and surface temperature. Geophys. Res. Lett. 32, 1–4 (2005).Article 

Google Scholar 
22.Seneviratne, S. I., Lüthi, D., Litschi, M. & Schär, C. Land–atmosphere coupling and climate change in Europe. Nature 443, 205–209 (2006).ADS 
CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
23.Horton, R. M., Mankin, J. S., Lesk, C., Coffel, E. & Raymond, C. A review of recent advances in research on extreme heat events. Curr. Clim. Change Rep. 2, 242–259 (2016).Article 

Google Scholar 
24.Berg, A. et al. Impact of soil moisture–atmosphere interactions on surface temperature distribution. J. Clim. 27, 7976–7993 (2014).ADS 
Article 

Google Scholar 
25.Miralles, D. G., Teuling, A. J., Van Heerwaarden, C. C. & De Arellano, J. V. G. Mega-heatwave temperatures due to combined soil desiccation and atmospheric heat accumulation. Nat. Geosci. 7, 345–349 (2014).ADS 
CAS 
Article 

Google Scholar 
26.Ray, D. K. et al. Climate change has likely already affected global food production. PLoS ONE 14, e0217148 (2019).27.Ray, D. K., Gerber, J. S., Macdonald, G. K. & West, P. C. Climate variation explains a third of global crop yield variability. Nat. Commun. 6, 5989 (2015).28.Liu, B. et al. Similar estimates of temperature impacts on global wheat yield by three independent methods. Nat. Clim. Change 6, 1130–1136 (2016).ADS 
Article 

Google Scholar 
29.Sánchez, B., Rasmussen, A. & Porter, J. R. Temperatures and the growth and development of maize and rice: a review. Glob. Change Biol. 20, 408–417 (2014).ADS 
Article 

Google Scholar 
30.Welch, J. R. et al. Rice yields in tropical/subtropical Asia exhibit large but opposing sensitivities to minimum and maximum temperatures. Proc. Natl Acad. Sci. USA 107, 14562–14567 (2010).ADS 
CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
31.Zhang, T., Lin, X. & Sassenrath, G. F. Current irrigation practices in the central United States reduce drought and extreme heat impacts for maize and soybean, but not for wheat. Sci. Total Environ. 508, 331–342 (2015).ADS 
CAS 
PubMed 
Article 

Google Scholar 
32.Mittler, R. Abiotic stress, the field environment and stress combination. Trends Plant Sci. 11, 15–19 (2006).CAS 
PubMed 
Article 

Google Scholar 
33.Swann, A. L. S. Plants and drought in a changing climate. Curr. Clim. Change Rep. 4, 192–201 (2018).Article 

Google Scholar 
34.Skinner, C. B., Poulsen, C. J. & Mankin, J. S. Amplification of heat extremes by plant CO2 physiological forcing. Nat. Commun. 9, 1–11 (2018).CAS 
Article 

Google Scholar 
35.Gates, D. M. Transpiration and leaf temperature. Annu. Rev. Plant Physiol. 19, 211–238 (1968).Article 

Google Scholar 
36.Crafts-Brandner, S. J. & Salvucci, M. E. Sensitivity of photosynthesis in a C4 plant, maize, to heat stress. Plant Physiol. 129, 1773–1780 (2002).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
37.Grossiord, C. et al. Plant responses to rising vapor pressure deficit. N. Phytol. 226, 1550–1566 (2020).Article 

Google Scholar 
38.Rosenzweig, C. et al. Assessing agricultural risks of climate change in the 21st century in a global gridded crop model intercomparison. Proc. Natl Acad. Sci. USA 111, 3268–3273 (2014).ADS 
CAS 
PubMed 
Article 

Google Scholar 
39.Eyring, V. et al. Overview of the Coupled Model Intercomparison Project Phase 6 (CMIP6) experimental design and organization. Geosci. Model Dev. 9, 1937–1958 (2016).ADS 
Article 

Google Scholar 
40.Seth, A. et al. Monsoon responses to climate changes—connecting past, present and future. Curr. Clim. Change Rep. 5, 63–79 (2019).41.Orlowsky, B. & Seneviratne, S. I. Statistical analyses of land–atmosphere feedbacks and their possible pitfalls. J. Clim. 23, 3918–3932 (2010).ADS 
Article 

Google Scholar 
42.Lesk, C., Coffel, E. & Horton, R. Net benefits to US soy and maize yields from intensifying hourly rainfall. Nat. Clim. Change 10, 819–822 (2020).ADS 
Article 

Google Scholar 
43.Vogel, M. M. et al. Regional amplification of projected changes in extreme temperatures strongly controlled by soil moisture–temperature feedbacks. Geophys. Res. Lett. 44, 1511–1519 (2017).ADS 
Article 

Google Scholar 
44.Mueller, B. et al. Evaluation of global observations-based evapotranspiration datasets and IPCC AR4 simulations. Geophys. Res. Lett. 38, 1–7 (2011).
Google Scholar 
45.Pendergrass, A. G. et al. Flash droughts present a new challenge for subseasonal-to-seasonal prediction. Nat. Clim. Change 10, 191–199 (2020).ADS 
Article 

Google Scholar 
46.Mueller, N. D. et al. Global relationships between cropland intensification and summer temperature extremes over the last 50 years. J. Clim. 30, 7505–7528 (2017).ADS 
Article 

Google Scholar 
47.He, Y., Lee, E. & Mankin, J. S. Seasonal tropospheric cooling in northeast China associated with cropland expansion. Environ. Res. Lett. 15, 034032 (2020).48.Ainsworth, E. A. & Long, S. P. 30 years of free-air carbon dioxide enrichment (FACE): what have we learned about future crop productivity and its potential for adaptation? Glob. Change Biol. 27, 27–49 (2021).ADS 
Article 

Google Scholar 
49.Deryng, D. et al. Regional disparities in the beneficial effects of rising CO2 concentrations on crop water productivity. Nat. Clim. Change 6, 786–790 (2016).ADS 
Article 

Google Scholar 
50.Challinor, A. J., Koehler, A.-K., Ramirez-Villegas, J., Whitfield, S. & Das, B. Current warming will reduce yields unless maize breeding and seed systems adapt immediately. Nat. Clim. Change 6, 954–958 (2016).ADS 
Article 

Google Scholar 
51.Lobell, D. B., Deines, J. M. & Di Tommaso, S. Changes in the drought sensitivity of US maize yields. Nat. Food 1, 729–735 (2020).Article 

Google Scholar 
52.Bassu, S. et al. How do various maize crop models vary in their responses to climate change factors? Glob. Change Biol. 20, 2301–2320 (2014).ADS 
Article 

Google Scholar 
53.Harris, I., Jones, P. D., Osborn, T. J. & Lister, D. H. Updated high-resolution grids of monthly climatic observations—the CRU TS3.10 dataset. Int. J. Clim. 34, 623–642 (2014).Article 

Google Scholar 
54.Rodell, M. et al. The Global Land Data Assimilation System. Bull. Am. Meteorol. Soc. 85, 381–394 (2004).ADS 
Article 

Google Scholar 
55.Sacks, W. J., Deryng, D. & Foley, J. A. Crop planting dates: an analysis of global patterns. Glob. Ecol. Biogeogr. 19, 607–620 (2010).
Google Scholar 
56.Hersbach, H. et al. The ERA5 global reanalysis. Q. J. R. Meteorol. Soc. 146, 1999–2049 (2020).ADS 
Article 

Google Scholar 
57.Vautard, R., Yiou, P. & Ghil, M. Singular-spectrum analysis: a toolkit for short, noisy chaotic signals. Physica D 58, 95–126 (1992).ADS 
Article 

Google Scholar  More

1.Descamps, S. et al. Climate change impacts on wildlife in a High Arctic archipelago–Svalbard, Norway. Glob. Change Biol. 23, 490–502 (2017).ADS 
Article 

Google Scholar 
2.Yletyinen, J. Arctic climate resilience. Nat. Clim. Change 9, 805–806 (2019).ADS 
Article 

Google Scholar 
3.Renaud, P. E. et al. Pelagic food-webs in a changing Arctic: A trait-based perspective suggests a mode of resilience. ICES J. Mar. Sci. 75, 1871–1881 (2018).Article 

Google Scholar 
4.Möller, E. F. & Nielsen, T. G. Borealization of Arctic zooplankton—smaller and less fat zooplankton species in Disko Bay, Western Greenland. Limnol. Oceanogr. 65, 1175–1188 (2020).ADS 
Article 

Google Scholar 
5.Dalpadado, P. et al. Climate effects on temporal and spatial dynamics of phytoplankton and zooplankton in the Barents Sea. Prog. Oceanogr. 185, 102320. https://doi.org/10.1016/j.pocean.2020.102320 (2020).Article 

Google Scholar 
6.Csapó, H. K., Grabowski, M. & Węsławski, J. M. Coming home – Boreal ecosystem claims Atlantic sector of the Arctic. Sci. Total. Environ. 771, 144817. https://doi.org/10.1016/j.scitotenv.2020.144817 (2021).ADS 
CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
7.Bauerfeind, E., Nöthig, E. M., Pauls, B., Kraft, A. & Beszczynska-Möller, A. Variability in pteropod sedimentation and corresponding aragonite flux at the Arctic deep-sea long-term observatory HAUSGARTEN in the eastern Fram Strait from 2000 to 2009. J. Mar. Syst. 132, 95–10 (2014).Article 

Google Scholar 
8.Weydmann, A. et al. Shift towards the dominance of boreal species in the Arctic: Inter-annual and spatial zooplankton variability in the West Spitsbergen Current. Mar. Ecol. Prog. Ser. 501, 41–52 (2014).ADS 
Article 

Google Scholar 
9.Gluchowska, M. et al. Zooplankton in Svalbard fjords on the Atlantic-Arctic boundary. Polar. Biol. 39, 1785–1802 (2016).Article 

Google Scholar 
10.Wassmann, P. et al. The contiguous domains of Arctic Ocean advection: Trails of life and death. Prog. Oceanogr. 139, 42–65 (2015).ADS 
Article 

Google Scholar 
11.Nielsen, T. G. & Andersen, C. Plankton community structure and production along a freshwater-influenced Norwegian fjord system. Mar. Biol. 141, 707–724 (2002).Article 

Google Scholar 
12.Lischka, S. & Hagen, W. Life histories of the copepods Pseudocalanus minutus, P. acuspes, (Calanoida) and Oithona similis (Cyclopoida) in the Arctic Kongsfjorden (Svalbard). Polar Biol. 28, 910–921 (2005).Article 

Google Scholar 
13.Arendt, K. E., Nielsen, T. G., Rysgaard, S. & Tönnesson, K. Differences in plankton community structure along the Godthåbsfjord, from the Greenland Ice Sheet to offshore waters. Mar. Ecol. Prog. Ser. 401, 49–62 (2010).ADS 
CAS 
Article 

Google Scholar 
14.Trudnowska, E., Stemmann, L., Błachowiak-Samołyk, K. & Kwasniewski, S. Taxonomic and size structures of zooplankton communities in the fjords along the Atlantic water passage to the Arctic. J. Mar. Sys. 204, 103306. https://doi.org/10.1016/j.jmarsys.2020.103306 (2020).Article 

Google Scholar 
15.Balazy, K., Trudnowska, E., Wichorowski, M. & Błachowiak-Samołyk, K. Large versus small zooplankton in relation to temperature in the Arctic shelf region. Polar. Res. 37, 1427409. https://doi.org/10.1080/17518369.2018.1427409 (2018).Article 

Google Scholar 
16.Turner, J. T. The importance of small planktonic copepods and their roles in pelagic marine food webs. Zool. Stud. 43, 255–266 (2004).
Google Scholar 
17.Turner, J. T. Planktonic copepods of Boston Harbor, Massachusetts Bay and Cape Cod Bay. Hydrobiologia 292(293), 405–413 (1994).Article 

Google Scholar 
18.Castellani, C., Robinson, C., Smith, T. & Lampitt, R. S. Temperature affects respiration rate of Oithona similis. Mar. Ecol. Prog. Ser. 285, 129–135 (2005).ADS 
Article 

Google Scholar 
19.Turner, J. T., Levinsen, H., Nielsen, T. G. & Hansen, B. W. Zooplankton feeding ecology: Grazing on phytoplankton and predation on protozoans by copepod and barnacle nauplii in Disko Bay, West Greenland. Mar. Ecol. Prog. Ser. 221, 209–219 (2001).ADS 
Article 

Google Scholar 
20.Boissonnot, L., Niehoff, B., Hagen, W., Søreide, J. E. & Graeve, M. Lipid turnover reflects life-cycle strategies of small-sized Arctic copepods. J. Plankton Res. 38, 1420–1432 (2016).CAS 

Google Scholar 
21.Błachowiak-Samołyk, K. et al. Winter Tales: The dark side of planktonic life. Polar Biol. 38, 23–36 (2015).Article 

Google Scholar 
22.Berge, J. et al. Zooplankton in the Polar Night in Polar Night Marine Ecology. In Advances in Polar Ecology Vol. 4 (eds Berge, J. et al.) (Springer, New York, 2020). https://doi.org/10.1007/978-3-030-33208-2_5.Chapter 

Google Scholar 
23.Hobbs, L., Banas, N. S., Cottier, F. R., Berge, J. & Daase, M. Eat or sleep: Availability of winter prey explains mid-winter and early-spring activity in an Arctic Calanus population. Front. Mar. Sci. 7, 744. https://doi.org/10.3389/fmars.2020.541564 (2020).Article 

Google Scholar 
24.Svensen, C., Seuthe, L., Vasilyeva, Y., Pasternak, A. & Hansen, E. Zooplankton distribution across Fram Strait in autumn: Are small copepods and protozooplankton important?. Prog. Oceanog. 91, 534–544 (2011).Article 

Google Scholar 
25.Węsławski, J. M., Kwasniewski, S. & Wiktor, J. Winter in Svalbard fjord ecosystem. Arctic 44, 115–123 (1991).Article 

Google Scholar 
26.Lischka, S., Giménez, L., Hagen, W. & Ueberschär, B. Seasonal changes in digestive enzyme (trypsin) activity of the copepods Pseudocalanus minutus (Calanoida) and Oithona similis (Cyclopoida) in the Arctic Kongsfjorden (Svalbard). Polar Biol. 30, 1331–1341 (2007).Article 

Google Scholar 
27.Lischka, S. & Hagen, W. Seasonal dynamics of mesozooplankton in the Arctic Kongsfjord (Svalbard) during year-round observations from August 1998 to July 1999. Polar Biol. 39, 1859–1878 (2016).Article 

Google Scholar 
28.Weydmann-Zwolicka, A. et al. Zooplankton and sediment flux in two contrasting fjords reveal Atlantification of the Arctic. Sci. Total. Environ. 773, 145599. https://doi.org/10.1016/j.scitotenv.2021.145599 (2021).ADS 
CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
29.Zamora-Terol, S., Nielsen, T. G. & Saiz, E. Plankton community structure and role of Oithona similis on the western coast of Greenland during the winter-spring transition. Mar. Ecol. Prog. Ser. 483, 85–102 (2013).ADS 
Article 

Google Scholar 
30.Zamora-Terol, S., Kjellerup, S., Swalethorp, R., Saiz, E. & Nielsen, T. G. Population dynamics and production of the small copepod Oithona spp. in a subarctic fjord of West Greenland. Polar. Biol. 37, 953–965 (2014).Article 

Google Scholar 
31.Dvoretsky, V. G. & Dvoretsky, A. G. Life cycle of Oithona similis (Copepoda: Cyclopoida) in Kola Bay (Barents Sea). Mar. Biol. 156, 1433–1446 (2009).Article 

Google Scholar 
32.Glad, P. Seasonal occurrence of Oithona similis (cyclopoida), Microsetella norvegica (harpacticoida) and Microcalanus spp. (calanoida), and productivity of O. similis, in three high-latitude Norwegian fjords. Master thesis (UiT The Arctic University of Norway, 2018).33.Kosobokova, K. & Hirche, H. J. Biomass of zooplankton in the eastern Arctic Ocean—a baseline study. Progr. Oceanogr. 82, 265–280 (2009).ADS 
Article 

Google Scholar 
34.Bluhm, B., Kosobokova, K. & Carmack, E. A tale of two basins: An integrated physical and biological perspective of the deep Arctic Ocean. Prog. Oceanog. 139, 89–121 (2015).Article 

Google Scholar 
35.Hop, H. et al. Zooplankton in Kongsfjorden (1996–2016) in Relation to Climate Change in The Ecosystem of Kongsfjorden, Svalbard. In Advances in Polar Ecology Vol. 2 (eds Hop, H. & Wiencke, C.) 10.1007/978-3-319-46425–1_7 (Springer, New York, 2019).
Google Scholar 
36.Böttger-Schnack, R., Schnack, D. & Hagen, W. Microcopepod community structure in the Gulf of Aqaba and northern Red Sea, with special reference to Oncaeidae. J. Plankton Res. 30, 529–550 (2008).Article 

Google Scholar 
37.Cornwell, L. E. et al. Seasonality of Oithona similis and Calanus helgolandicus reproduction and abundance: Contrasting responses to environmental variation at a shelf site. J. Plankton Res. 40, 295–310 (2018).Article 

Google Scholar 
38.Kubiszyn, A. M. et al. The annual planktonic protist community structure in an ice-free high Arctic fjord (Adventfjorden, West Spitsbergen). J. Mar. Syst. 169, 61–72 (2017).Article 

Google Scholar 
39.Kellogg, C. T. E., McClelland, J. W., Dunton, K. H. & Crump, B. C. Strong seasonality in Arctic estuarine microbial food webs. Front. Microbiol. 10, 2628. https://doi.org/10.3389/fmicb.2019.02628 (2019).Article 
PubMed 
PubMed Central 

Google Scholar 
40.Bhaskar, J. T., Parli, B. V. & Tripathy, S. C. Spatial and seasonal variations of dinoflagellates and ciliates in the Kongsfjorden. Svalbard. Mar. Ecol. 41, 1–12 (2020).Article 

Google Scholar 
41.Skogseth, R. et al. Variability and decadal trends in the Isfjorden (Svalbard) ocean climate and circulation–An indicator for climate change in the European Arctic. Prog. Oceanog. 187, 102394. https://doi.org/10.1016/j.pocean.2020.102394 (2020).Article 

Google Scholar 
42.Ward, P. & Hirst, A. G. Oithona similis in a high latitude ecosystem: Abundance, distribution and temperature limitation of fecundity rates in a sac spawning copepod. Mar. Biol. 151, 1099–1110 (2007).Article 

Google Scholar 
43.Nielsen, T. G. & Sabatini, M. Role of cyclopoid copepods Oithona spp. in North Sea plankton communities. Mar. Ecol. Prog. Ser. 139, 79–93 (1996).ADS 
Article 

Google Scholar 
44.Nilsen, F., Cottier, F., Skogseth, R. & Mattsson, S. Fjord–shelf exchanges controlled by ice and brine production: The interannual variation of Atlantic Water in Isfjorden, Svalbard. Cont. Shelf Res. 28, 1838–1853 (2008).ADS 
Article 

Google Scholar 
45.Cohen, J. H., Berge, J., Moline, M. A., Johnsen, G. & Zolich, A. P. Light in the Polar Night. In Polar Night Marine Ecology Advances in Polar Ecology Vol. 4 (eds Berge, J. et al.) (Springer, New York, 2020). https://doi.org/10.1007/978-3-030-33208-2_3.Chapter 

Google Scholar 
46.Wiedmann, I., Reigstad, M., Marquardt, M., Vader, A. & Gabrielsen, T. M. Seasonality of vertical flux and sinking particle characteristics in an ice-free high arctic fjord—different from subarctic fjords?. J. Mar. Syst. 154, 192–205 (2015).Article 

Google Scholar 
47.Holm-Hansen, O. & Riemann, B. Chlorophyll a determination: Improvements in methodology. Oikos 30, 438–447 (1978).CAS 
Article 

Google Scholar 
48.Stübner, E. I., Søreide, J. E., Reigstad, M., Marquardt, M. & Blachowiak-Samolyk, K. Year-round meroplankton dynamics in high-Arctic Svalbard. J. Plankton Res. 38, 522–536 (2016).Article 

Google Scholar 
49.Marquardt, M., Vader, A., Stübner, E. I., Reigstad, M. & Gabrielsen, T. M. Strong seasonality of marine microbial eukaryotes in a high-Arctic fjord (Isfjorden, West Spitsbergen). Appl. Environ. Microb. 82, 1868–1880 (2016).ADS 
CAS 
Article 

Google Scholar 
50.Trantner, D. J. & Fraser, H. Zooplankton sampling. Monographs on Oceanographic Methodology 2. (UNESCO, 1968).51.Harris, R., Wiebe, L., Lenz, J., Skjoldal, H. R. & Huntley, M. ICES Zooplankton Methodology Manual (Academic Press, Cambridge, 2000).
Google Scholar 
52.Espinasse, M. et al. Interannual phenological variability in two North-East Atlantic populations of Calanus finmarchicus. Mar. Biol. Res. 14, 752–767 (2018).Article 

Google Scholar 
53.Mackas, D. L., Batten, S. & Trudel, M. Effects on zooplankton of a warmer ocean: Recent evidence from the Northeast Pacific. Prog. Oceanogr. 75, 223–252 (2007).ADS 
Article 

Google Scholar 
54.Head, E. J. H., Melle, W., Pepin, P., Bagøien, E. & Broms, C. On the ecology of Calanus finmarchicus in the Subarctic North Atlantic: A comparison of population dynamics and environmental conditions in areas of the Labrador Sea-Labrador/Newfoundland Shelf and Norwegian Sea Atlantic and Coastal Waters. Prog. Oceanog. 114, 46–63 (2013).Article 

Google Scholar 
55.Kwasniewski, S. et al. Interannual changes in zooplankton on theWest Spitsbergen Shelf in relation to hydrography and their consequences for the diet of planktivorous seabirds. J. Mar. Sci. 69, 890–901 (2012).
Google Scholar 
56.Kiorboe, T. Sex, sex-ratios, and the dynamics of pelagic copepod populations. Oecol. 148, 40–50 (2006).ADS 
Article 

Google Scholar 
57.Thackeray, et al. Food web de-synchronization in England’s largest lake: An assessment based on multiple phenological metrics. Glob. Change Biol. 19, 3568–3580 (2013).ADS 
Article 

Google Scholar 
58.Anderson, M. J., Gorley, R. N. & Clarke, K. R. PERMANOVA+ for PRIMER: Guide to Software and Statistical Methods. (Primer-E Ltd., 2008).59.Clarke, K. R. & Gorley, R. N. Primer. (Primer-E Ltd., 2001).60.Anderson, M. J. & Braak, C. J. F. Permutation tests for multi-factorial analysis of variance. J. Stat. Comput. Simul. 73, 85–113 (2003).MathSciNet 
MATH 
Article 

Google Scholar 
61.Schlitzer, R. Ocean Data View; https://odv.awi.de, (2021).62.Walczowski, W., Piechura, J., Goszczko, I. & Wieczorek, P. Changes in Atlantic water properties: An important factor in the European Arctic marine climate. ICES J. Mar. Sci 69, 864–869 (2012).Article 

Google Scholar 
63.Wassman, P., Duarte, C. M., Agustí, S. & Sejr, M. L. Footprints of climate change in the Arctic marine ecosystem. Glob. Change Biol. 17, 1235–1249 (2010).ADS 
Article 

Google Scholar 
64.Andrews, A. J. et al. Boreal marine fauna from the Barents Sea disperse to Arctic Northeast Greenland. Sci Rep 9, 5799 (2019).ADS 
PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
65.Atkinson, D. & Sibly, R. M. Why are organisms usually bigger in colder environments? Making sense of life history puzzle. Trends Ecol. Evol. 12, 235–239 (1997).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
66.Beaugrand, G., Ibanez, F. & Reid, P. C. Spatial seasonal and long term fluctuations of plankton in relation to hydroclimatic features in the English Channel, Celtic Sea and Bay of Biscay. Mar. Ecol. Prog. Ser. 200, 93–102 (2000).ADS 
Article 

Google Scholar 
67.Beaugrand, G., Reid, P. C., Ibañez, F., Lindley, A. & Edwards, M. Reorganization of North Atlantic Marine Copepod Biodiversity and Climate. Science 31, 1692–1694 (2002).ADS 
Article 

Google Scholar 
68.Coyle, K. O. et al. Climate change in the southeastern Bering Sea: Impacts on pollock stocks and implications for the oscillating control hypothesis. Fisher. Oceanogr. 20, 139–156 (2011).Article 

Google Scholar 
69.Edwards, M. & Richardson, A. J. The impact of climate change on the phenology of the plankton community and trophic mismatch. Nature 430, 881–884 (2004).ADS 
CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
70.Stevens, G. C. The latitudinal gradient in geographical range: How so many species coexist in the tropics. Am. Nat. 133, 240–256 (1989).Article 

Google Scholar 
71.Kortsch, S., Primicerio, R., Fossheim, M., Dolgov, A. V. & Aschan, M. Climate change alters the structure of arctic marine food webs due to poleward shifts of boreal generalists. Proc. R. Soc. B. 282, 20151546 (2015).PubMed 
PubMed Central 
Article 

Google Scholar 
72.Richardson, A. J. In hot water: Zooplankton and climate change. ICES J. Mar. Sci. 65, 279–295 (2008).Article 

Google Scholar 
73.Kwasniewski, S. A note on zooplankton of the Hornsund Fjord and its seasonal changes. Oceanografia 12, 7–27 (1990).
Google Scholar 
74.Piwosz, K. et al. Comparison of productivity and phytoplankton in a warm (Kongsfjorden) and a cold (Hornsund) Spitsbergen fjord in midsummer 2002. Polar Biol. 32, 549–559 (2009).Article 

Google Scholar 
75.Trudnowska, E., Basedow, S. L. & Blachowiak-Samolyk, K. Mid-summer mesozooplankton biomass, its size distribution, and estimated production within a glacial Arctic fjord (Hornsund, Svalbard). J. Mar. Syst. 137, 55–66 (2014).Article 

Google Scholar 
76.Castellani, C., Licandro, P., Fileman, E., di Capua, I. & Mazzocchi, M. G. Oithona similis likes it cool: Evidence from two long-term time series. J. Plankton Res. 38, 703–717 (2016).CAS 
Article 

Google Scholar 
77.Cornwell, L. E. et al. Resilience of the copepod Oithona similis to climatic variability: Egg production, mortality, and vertical habitat partitioning. Front. Mar. Sci. https://doi.org/10.3389/fmars.2020.00029 (2020).Article 

Google Scholar 
78.Eiane, K. & Ohman, M. D. Stage-specific mortality of Calanus finmarchicus, Pseudocalanus elongatus and Oithona similis on Fladen Ground, North Sea, during a spring bloom. Mar. Ecol. Prog. Ser. 268, 183–193 (2004).ADS 
Article 

Google Scholar 
79.Thor, P. et al. Post-spring bloom community structure of pelagic copepods in the Disko Bay, Western Greenland. J. Plankton Res. 27, 341–356 (2005).CAS 
Article 

Google Scholar 
80.Dvoretsky, V. G. Seasonal mortality rates of Oithona similis (Cyclopoida) in a large Arctic fjord. Polar Sci. 6, 263–269 (2012).ADS 
Article 

Google Scholar 
81.Ussing, H. H. The biology of some important plankton animals in the fjords of east Greenland. Medd Grønland 100–108 (1938).
82.Lonsdale, D. J., Caron, D. A., Dennett, M. R. & Schaffner, R. Predation by Oithona spp on protozooplankton in the Ross Sea. Antarctica. Deep-Sea Res. II 47, 3273–3283 (2000).
Google Scholar 
83.Castellani, C., Irigoien, X., Harris, R. P. & Lampitt, R. S. Feeding and egg production of Oithona similis in the North Atlantic. Mar. Ecol. Prog. Ser. 288, 173–182 (2005).ADS 
Article 

Google Scholar 
84.Barth-Jensen, C. et al. Temperature-dependent egg production and egg hatching rates of small egg-carrying and broadcast-spawning copepods Oithona similis, Microsetella norvegica and Microcalanus pusillus. J. Plankton Res. 42, 564–580 (2020).PubMed 
PubMed Central 
Article 

Google Scholar 
85.Falk-Petersen, S., Pedersen, G., Kwasniewski, S., Hegseth, E. N. & Hop, H. Spatial distribution and life-cycle timing of zooplankton in the marginal ice zone of the Barents Sea during the summer melt season in 1995. J. Plankton Res. 21, 1249–1264 (1999).Article 

Google Scholar 
86.Gluchowska, M. et al. Interannual zooplankton variability in the main pathways of the Atlantic water flow into the Arctic Ocean (Fram Strait and Barents Sea branches). ICES J. Mar. Sci. 74, 1921–1936 (2017).Article 

Google Scholar 
87.Balazy, K., Trudnowska, E. & Błachowiak-Samołyk, K. Dynamics of Calanus copepodite structure during Little Auks’ breeding seasons in two different Svalbard locations. Water 11, 1405. https://doi.org/10.3390/w11071405 (2019).CAS 
Article 

Google Scholar 
88.Hop, H. et al. The marine ecosystem of Kongsfjorden, Svalbard. Polar Res. 21, 167–208 (2002).Article 

Google Scholar 
89.Poje, A. The relationship between plankton and water mass properties in high Arctic (Svalbard) fjords. Clark Honors College Theses, (University of Oregon, 2016).90.Falk-Petersen, S., Mayzaud, P., Kattner, G. & Sargent, J. R. Lipids and life strategy of Arctic Calanus. Mar. Biol. Res. 5, 18–39 (2009).Article 

Google Scholar 
91.Svensen, C. et al. Zooplankton communities associated with new and regenerated primary production in the Atlantic inflow North of Svalbard. Front. Mar. Sci. 6, 293. https://doi.org/10.3389/fmars.2019.00293 (2019).Article 

Google Scholar 
92.González, H. E. & Smetacek, V. The possible role of the cyclopoid copepod Oithona in retarding vertical flux of zooplankton faecal material. Mar. Ecol. Prog. Ser. 113, 233–246 (1994).ADS 
Article 

Google Scholar 
93.Berge, J. et al. Unexpected levels of biological activity during the polar night offer new perspectives on a warming Arctic. Curr. Biol. 25, 2555–2561 (2015).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
94.Berge, J. et al. In the dark: A review of ecosystem processes during the Arctic polar night. Progr. Oceanog. 139, 258–271 (2015).ADS 
Article 

Google Scholar 
95.Narcy, F. et al. Seasonal and individual variability of lipid reserves in Oithona similis (Cyclopoida) in an Arctic fjord. Polar Biol. 32, 233–242 (2009).Article 

Google Scholar 
96.Kattner, G. & Hagen, W. Lipids in marine copepods: Latitudinal characteristics and perspective to global warming. In Lipids in Aquatic Ecosystems (eds Kainz, M. et al.) 257–280 (Springer, New York, 2009).Chapter 

Google Scholar 
97.Rokkan Iversen, K. & Seuthe, L. Seasonal microbial processes in a high-latitude fjord (Kongsfjorden, Svalbard): I. Heterotrophic bacteria, picoplankton and nanoflagellates. Polar Biol. 34, 731–749 (2011).Article 

Google Scholar 
98.Auel, H. & Hagen, W. Mesozooplankton community structure, abundance and biomass in the central Arctic Ocean. Mar. Biol. 140, 1013–1021 (2002).Article 

Google Scholar 
99.Madsen, S., Nielsen, T. & Hansen, B. Annual population development and production by small copepods in Disko Bay, western Greenland. Mar. Biol. 155, 63–77 (2008).Article 

Google Scholar 
100.Corkett, C. J. & McLaren, I. A. The biology of Pseudocalanus. In Advances in Marine Biology Vol. 15 (eds Russell, F. S. & Yonge, M.) 1–231 (Academic Press, Cambridge, 1978).
Google Scholar 
101.Kwasniewski, S., Hop, H., Falk-Petersen, S. & Pedersen, G. Distribution of Calanus species in Kongsfjorden, a glacial fjord in Svalbard. J. Plankton Res. 2003(25), 1–20 (2003).Article 

Google Scholar 
102.Willis, K., Cottier, F., Kwasniewski, S., Wold, A. & Falk-Petersen, S. The influence of advection on zooplankton community composition in an Arctic fjord (Kongsfjorden, Svalbard). J. Mar. Syst. 61, 39–54 (2006).Article 

Google Scholar  More

1.Cardinale, B. J. et al. Effects of biodiversity on the functioning of trophic groups and ecosystems. Nature 443, 989–992 (2006).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
2.Hooper, D. U. et al. A global synthesis reveals biodiversity loss as a major driver of ecosystem change. Nature 486, 105–108 (2012).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
3.Cardinale, B. J. et al. Biodiversity loss and its impact on humanity. Nature 489, 326–326 (2012).CAS 
Article 

Google Scholar 
4.Isbell, F. et al. Linking the influence and dependence of people on biodiversity across scales. Nature 546, 65–72 (2017).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
5.Mori, A. S., Isbell, F. & Seidl, R. β-Diversity, community assembly, and ecosystem functioning. Trends Ecol. Evol. 33, 549–564 (2018).PubMed 
Article 
PubMed Central 

Google Scholar 
6.Gonzalez, A. et al. Scaling‐up biodiversity–ecosystem functioning research. Ecol. Lett. 23, 757–776 (2020).PubMed 
PubMed Central 
Article 

Google Scholar 
7.Genung, M. A., Fox, J. & Winfree, R. Species loss drives ecosystem function in experiments, but in nature the importance of species loss depends on dominance. Glob. Ecol. Biogeogr. 29, 1531–1541 (2020).Article 

Google Scholar 
8.Duffy, J. E., Godwin, C. M. & Cardinale, B. J. Biodiversity effects in the wild are common and as strong as key drivers of productivity. Nature 549, 261–264 (2017).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
9.Wardle, D. A. Do experiments exploring plant diversity–ecosystem functioning relationships inform how biodiversity loss impacts natural ecosystems? J. Veg. Sci. 27, 646–653 (2016).Article 

Google Scholar 
10.Wardle, D. A., Bardgett, R. D., Callaway, R. M. & Van der Putten, W. H. Terrestrial ecosystem responses to species gains and losses. Science 332, 1273–1277 (2011).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
11.Hillebrand, H. & Matthiessen, B. Biodiversity in a complex world: consolidation and progress in functional biodiversity research. Ecol. Lett. 12, 1405–1419 (2009).PubMed 
Article 
PubMed Central 

Google Scholar 
12.Jochum, M. et al. The results of biodiversity–ecosystem functioning experiments are realistic. Nat. Ecol. Evol. 4, 1485–1494 (2020).PubMed 
Article 
PubMed Central 

Google Scholar 
13.van der Plas, F. Biodiversity and ecosystem functioning in naturally assembled communities. Biol. Rev. 94, 1220–1245 (2019).PubMed 
PubMed Central 

Google Scholar 
14.Bannar-Martin, K. H. et al. Integrating community assembly and biodiversity to better understand ecosystem function: the Community Assembly and the Functioning of Ecosystems (CAFE) approach. Ecol. Lett. 21, 167–180 (2018).PubMed 
Article 
PubMed Central 

Google Scholar 
15.Leibold, M. A., Chase, J. M. & Ernest, S. K. M. Community assembly and the functioning of ecosystems: how metacommunity processes alter ecosystems attributes. Ecology 98, 909–919 (2017).PubMed 
Article 
PubMed Central 

Google Scholar 
16.Tilman, D., Isbell, F. & Cowles, J. M. Biodiversity and ecosystem functioning. Annu. Rev. Ecol. Evol. Syst. 45, 471–493 (2014).Article 

Google Scholar 
17.Leibold, M. A. et al. The metacommunity concept: a framework for multi-scale community ecology. Ecol. Lett. 7, 601–613 (2004).Article 

Google Scholar 
18.HilleRisLambers, J., Adler, P. B., Harpole, W. S., Levine, J. M. & Mayfield, M. M. Rethinking community assembly through the lens of coexistence theory. Annu. Rev. Ecol. Evol. Syst. 43, 227–248 (2012).Article 

Google Scholar 
19.Stein, A., Gerstner, K. & Kreft, H. Environmental heterogeneity as a universal driver of species richness across taxa, biomes and spatial scales. Ecol. Lett. 17, 866–880 (2014).Article 

Google Scholar 
20.Grace, J. B. et al. Integrative modelling reveals mechanisms linking productivity and plant species richness. Nature 529, 390–393 (2016).CAS 
PubMed 
Article 

Google Scholar 
21.Harpole, W. S. et al. Addition of multiple limiting resources reduces grassland diversity. Nature 537, 93–96 (2016).CAS 
PubMed 
Article 

Google Scholar 
22.Legendre, P. & De Cáceres, M. Beta diversity as the variance of community data: dissimilarity coefficients and partitioning. Ecol. Lett. 16, 951–963 (2013).PubMed 
Article 

Google Scholar 
23.Legendre, P. Interpreting the replacement and richness difference components of beta diversity. Glob. Ecol. Biogeogr. 23, 1324–1334 (2014).Article 

Google Scholar 
24.Craven, D. et al. A cross‐scale assessment of productivity–diversity relationships. Glob. Ecol. Biogeogr. 29, 1940–1955 (2020).Article 

Google Scholar 
25.Barry, K. E. et al. A graphical null model for scaling biodiversity–ecosystem functioning relationships. J. Ecol. 109, 1549–1560 (2021).Article 

Google Scholar 
26.Winfree, R. et al. Species turnover promotes the importance of bee diversity for crop pollination at regional scales. Science 359, 791–793 (2018).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
27.Isbell, F. et al. Quantifying effects of biodiversity on ecosystem functioning across times and places. Ecol. Lett. 21, 763–778 (2018).PubMed 
PubMed Central 
Article 

Google Scholar 
28.Isbell, F. et al. High plant diversity is needed to maintain ecosystem services. Nature 477, 199–202 (2011).CAS 
PubMed 
Article 

Google Scholar 
29.Bell, T., Newman, J. A., Silverman, B. W., Turner, S. L. & Lilley, A. K. The contribution of species richness and composition to bacterial services. Nature 436, 1157–1160 (2005).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
30.Fox, J. W. & Kerr, B. Analyzing the effects of species gain and loss on ecosystem function using the extended Price equation partition. Oikos 121, 290–298 (2012).Article 

Google Scholar 
31.Peters, M. K. et al. Predictors of elevational biodiversity gradients change from single taxa to the multi-taxa community level. Nat. Commun. 7, 13736 (2016).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
32.Peters, M. K. et al. Climate–land-use interactions shape tropical mountain biodiversity and ecosystem functions. Nature 568, 88–92 (2019).CAS 
PubMed 
Article 

Google Scholar 
33.Winfree, R., Fox, J. W., Williams, N. M., Reilly, J. R. & Cariveau, D. P. Abundance of common species, not species richness, drives delivery of a real-world ecosystem service. Ecol. Lett. 18, 626–635 (2015).PubMed 
Article 

Google Scholar 
34.Garnier, E. et al. Plant functional markers capture ecosystem properties during secondary succession. Ecology 85, 2630–2637 (2004).Article 

Google Scholar 
35.Stepp, J. R., Castaneda, H. & Cervone, S. Mountains and biocultural diversity. Mt. Res. Dev. 25, 223–227 (2005).Article 

Google Scholar 
36.Balehegn, M. Unintended consequences: the ecological repercussions of land grabbing in sub-Saharan Africa. Environment 57, 4–21 (2015).
Google Scholar 
37.The IPBES Regional Assessment Report on Biodiversity and Ecosystem Services for Africa (Intergovernmental Science-Policy Platform on Biodiversity and Ecosystem Services, 2018); https://doi.org/10.5281/ZENODO.323617838.Maitima, J. et al. The linkages between land use change, land degradation and biodiversity across East Africa. Afr. J. Environ. Sci. Technol. 3, 310–325 (2009).
Google Scholar 
39.Clough, Y. et al. Combining high biodiversity with high yields in tropical agroforests. Proc. Natl Acad. Sci. USA 108, 8311–8316 (2011).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
40.Muhumuza, M. & Balkwill, K. Factors affecting the success of conserving biodiversity in national parks: a review of case studies from Africa. Int. J. Biodivers. 2013, 798101 (2013).Article 

Google Scholar 
41.Mbow, C., van Noordwijk, M., Prabhu, R. & Simons, T. Knowledge gaps and research needs concerning agroforestry’s contribution to Sustainable Development Goals in Africa. Curr. Opin. Environ. Sustain. 6, 162–170 (2014).Article 

Google Scholar 
42.Kangalawe, R. Y. M., Noe, C., Tungaraza, F. S. K., Naimani, G. & Mlele, M. Understanding of traditional knowledge and indigenous institutions on sustainable land management in Kilimanjaro Region, Tanzania. Open J. Soil Sci. 04, 469–493 (2014).Article 

Google Scholar 
43.Pretty, J., Toulmin, C. & Williams, S. Sustainable intensification in African agriculture. Int. J. Agric. Sustain. 9, 5–24 (2011).Article 

Google Scholar 
44.Mbow, C. et al. Agroforestry solutions to address food security and climate change challenges in Africa. Curr. Opin. Environ. Sustain. 6, 61–67 (2014).Article 

Google Scholar 
45.Ofori, D. A. et al. Developing more productive African agroforestry systems and improving food and nutritional security through tree domestication. Curr. Opin. Environ. Sustain. 6, 123–127 (2014).Article 

Google Scholar 
46.Munang, R. et al. Ecosystem Based Adaptation (EBA) for Food Security in Africa—Towards a Comprehensive Strategic Framework to Upscale and Out-scale EBA-Driven Agriculture in Africa (United Nations Environment Programme, 2015).47.Albrecht, A. & Kandji, S. T. Carbon sequestration in tropical agroforestry systems. Agric. Ecosyst. Environ. 99, 15–27 (2003).CAS 
Article 

Google Scholar 
48.van der Plas, F. et al. Biotic homogenization can decrease landscape-scale forest multifunctionality. Proc. Natl Acad. Sci. USA 113, 3557–3562 (2016).PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
49.Brown, J. H., Gillooly, J. F., Allen, A. P., Savage, V. M. & West, G. B. Toward a metabolic theory of ecology. Ecology 85, 1771–1789 (2004).Article 

Google Scholar 
50.Allen, A. P. Global biodiversity, biochemical kinetics, and the energetic-equivalence rule. Science 297, 1545–1548 (2002).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
51.Nottingham, A. T. et al. Microbes follow Humboldt: temperature drives plant and soil microbial diversity patterns from the Amazon to the Andes. Ecology 99, 2455–2466 (2018).PubMed 
Article 
PubMed Central 

Google Scholar 
52.Hemp, A. Continuum or zonation? Altitudinal gradients in the forest vegetation of Mt. Kilimanjaro. Plant Ecol. 184, 27–42 (2006).Article 

Google Scholar 
53.Hemp, A. Vegetation of Kilimanjaro: hidden endemics and missing bamboo. Afr. J. Ecol. 44, 305–328 (2006).Article 

Google Scholar 
54.Appelhans, T. et al. Eco-meteorological characteristics of the southern slopes of Kilimanjaro. Tanzan. Int. J. Climatol. 36, 3245–3258 (2016).Article 

Google Scholar 
55.van Genuchten, M. T. H. A closed-form equation for predicting the hydraulic conductivity of unsaturated soils. Soil Sci. Soc. Am. J. 44, 892–898 (1980).Article 

Google Scholar 
56.Gebert, F., Steffan-Dewenter, I., Moretto, P. & Peters, M. K. Climate rather than dung resources predict dung beetle abundance and diversity along elevational and land use gradients on Mt. Kilimanjaro. J. Biogeogr. 47, 371–381 (2019).Article 

Google Scholar 
57.Dunning, J. B. CRC Handbook of Avian Body Masses (CRC Press, 2008).58.Wilman, H. et al. EltonTraits 1.0: species-level foraging attributes of the world’s birds and mammals. Ecology 95, 2027 (2014).Article 

Google Scholar 
59.Kingdon, J. et al. Mammals of Africa (Bloomsbury, 2013).60.Kaspari, M. & Weiser, M. D. The size–grain hypothesis and interspecific scaling in ants. Funct. Ecol. 13, 530–538 (1999).Article 

Google Scholar 
61.Cane, J. H. Estimation of bee size using intertegular span (Apoidea). J. Kans. Entomol. Soc. 60, 145–147 (1987).
Google Scholar 
62.Classen, A., Steffan-Dewenter, I., Kindeketa, W. J. & Peters, M. K. Integrating intraspecific variation in community ecology unifies theories on body size shifts along climatic gradients. Funct. Ecol. 31, 768–777 (2017).Article 

Google Scholar 
63.Kendall, L. K. et al. Pollinator size and its consequences: robust estimates of body size in pollinating insects. Ecol. Evol. 9, 1702–1714 (2019).PubMed 
PubMed Central 
Article 

Google Scholar 
64.Hódar, J. A. The use of regression equations for estimation of arthropod biomass in ecological studies. Acta Oecol. 17, 421–433 (1996).
Google Scholar 
65.Ensslin, A. et al. Effects of elevation and land use on the biomass of trees, shrubs and herbs at Mount Kilimanjaro. Ecosphere 6, 45 (2015).Article 

Google Scholar 
66.Cheng, D.-L. & Niklas, K. J. Above- and below-ground biomass relationships across 1534 forested communities. Ann. Bot. 99, 95–102 (2007).PubMed 
Article 

Google Scholar 
67.Pabst, H., Gerschlauer, F., Kiese, R. & Kuzyakov, Y. Land use and precipitation affect organic and microbial carbon stocks and the specific metabolic quotient in soils of eleven ecosystems of Mt. Kilimanjaro, Tanzania. Land Degrad. Dev. 27, 592–602 (2016).Article 

Google Scholar 
68.Albrecht, J. et al. Plant and animal functional diversity drive mutualistic network assembly across an elevational gradient. Nat. Commun. 9, 3177 (2018).PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
69.Classen, A. et al. Specialization of plant–pollinator interactions increases with temperature at Mt. Kilimanjaro. Ecol. Evol. 10, 2182–2195 (2020).PubMed 
PubMed Central 
Article 

Google Scholar 
70.Mayr, A. V. et al. Climate and food resources shape species richness and trophic interactions of cavity-nesting Hymenoptera. J. Biogeogr. 47, 854–865 (2020).Article 

Google Scholar 
71.Peters, M. K., Mayr, A., Röder, J., Sanders, N. J. & Steffan-Dewenter, I. Variation in nutrient use in ant assemblages along an extensive elevational gradient on Mt Kilimanjaro. J. Biogeogr. 41, 2245–2255 (2014).Article 

Google Scholar 
72.Genung, M. A. et al. The relative importance of pollinator abundance and species richness for the temporal variance of pollination services. Ecology 98, 1807–1816 (2017).PubMed 
PubMed Central 
Article 

Google Scholar 
73.Manly, B. F. J. Randomization, Bootstrap, and Monte Carlo Methods in Biology (Chapman & Hall/CRC, 2007).74.Gelman, A. Prior distributions for variance parameters in hierarchical models (comment on article by Browne and Draper). Bayesian Anal. 1, 515–533 (2006).
Google Scholar 
75.Huang, A. & Wand, M. P. Simple marginally noninformative prior distributions for covariance matrices. Bayesian Anal. 8, 439–452 (2013).Article 

Google Scholar 
76.O’Hara, R. B. & Sillanpää, M. J. A review of Bayesian variable selection methods: what, how and which. Bayesian Anal. 4, 85–117 (2009).
Google Scholar 
77.Albrecht, J., Hagge, J., Schabo, D. G., Schaefer, H. M. & Farwig, N. Reward regulation in plant–frugivore networks requires only weak cues. Nat. Commun. 9, 4838 (2018).PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
78.Grace, J. B., Johnson, D. J., Lefcheck, J. S. & Byrnes, J. E. K. Quantifying relative importance: computing standardized effects in models with binary outcomes. Ecosphere 9, e02283 (2018).Article 

Google Scholar 
79.Kass, R. E. & Raftery, A. E. Bayes factors. J. Am. Stat. Assoc. 90, 773–795 (1995).Article 

Google Scholar 
80.Levy, R. Bayesian data–model fit assessment for structural equation modeling. Struct. Equ. Modeling 18, 663–685 (2011).Article 

Google Scholar 
81.Nakagawa, S. & Schielzeth, H. A general and simple method for obtaining R2 from generalized linear mixed-effects models. Methods Ecol. Evol. 4, 133–142 (2013).Article 

Google Scholar 
82.Nakagawa, S., Johnson, P. C. D. & Schielzeth, H. The coefficient of determination R2 and intra-class correlation coefficient from generalized linear mixed-effects models revisited and expanded. J. R. Soc. Interface 14, 20170213 (2017).PubMed 
PubMed Central 
Article 

Google Scholar 
83.R Development Core Team. R: A Language and Environment for Statistical Computing v. 4.0.3 (R Foundation for Statistical Computing, 2020).84.Oksanen, J. et al. vegan: Community ecology package. R package version 2.5-7 http://cran.r-project.org/package=vegan (2020).85.Plummer, M. JAGS: A program for analysis of Bayesian graphical models using Gibbs sampling http://mcmc-jags.sourceforge.net (2003)86.Plummer, M. rjags: Bayesian graphical models using MCMC. R package version 4-10 https://CRAN.R-project.org/package=rjags (2016)87.Statisticat, LLC. LaplacesDemon: Complete environment for Bayesian inference. R package version 16.1.4 https://CRAN.R-project.org/package=LaplacesDemon (2021)88.Plummer, M., Best, N., Cowles, K. & Vines, K. CODA: convergence diagnosis and output analysis for MCMC. R N. 6, 7–11 (2006).
Google Scholar 
89.Epskamp, S., Cramer, A. O. J., Waldorp, L. J., Schmittmann, V. D. & Borsboom, D. qgraph: network visualizations of relationships in psychometric data. J. Stat. Softw. 48, 1–18 (2012).Article 

Google Scholar 
90.Wood, S. N. Generalized Additive Models: An Introduction with R (CRC/Taylor & Francis, 2017).91.Chao, A. & Jost, L. Coverage-based rarefaction and extrapolation: standardizing samples by completeness rather than size. Ecology 93, 2533–2547 (2012).Article 

Google Scholar 
92.Hsieh, T. C., Ma, K. H. & Chao, A. iNEXT: an R package for rarefaction and extrapolation of species diversity (Hill numbers). Methods Ecol. Evol. 7, 1451–1456 (2016).Article 

Google Scholar 
93.Albrecht, J. et al. Data and code from ‘Species richness is more important for ecosystem functioning than species turnover along an elevational gradient’. Figshare https://doi.org/10.6084/m9.figshare.14544207 (2021). More

Download PDF

Here at the Natural History Museum at Tring, UK, I’m in our nest collection, which numbers just over 4,000. Behind me are 67 metal cabinets with nests arranged in taxonomic order. Each nest is labelled with the date and place of collection, and the collector’s name. Next to me is a 1928 mud nest from Argentina that was made by the rufous hornero (Furnarius rufus), known for its large, globular nests that shield eggs and young from predators.I’m the senior curator of birds’ eggs and nests. I ensure that specimens are stored appropriately to prevent damage and are well catalogued, so we know exactly what we have and where. Our nest and egg collections are the most comprehensive archive of information on bird breeding in the world. When I came here about 20 years ago, the nest collection was rarely used and we didn’t know how many examples of extinct and endangered species we had. I’ve spent a lot of time and effort cataloguing and understanding these particular 129 nests, 40 of which belong to extinct birds such as the Laysan crake (Zapornia palmeri) and the Aldabra brush warbler (Nesillas aldabrana).We have up to 300,000 sets of eggs. I am holding four dunlin (Calidris alpina) eggshells, collected in 1952 in Ireland. They were donated to the Wildfowl & Wetlands Trust, a UK conservation charity, which gave them to us as part of a larger collection.I have been interested in birds and natural history since childhood, and my mother used to take me to the Royal Museum of Scotland (now the National Museum of Scotland) in Edinburgh. After graduating in biological sciences from Edinburgh Napier University, I volunteered at the museum before getting my first paid museum job.When researchers want to access the collections, I check that we have specimens relevant to their research, discuss exactly what they intend to do and work with them to minimize the risk of damage. Although I want our collections to result in robust science, they must be preserved.

Nature 597, 586 (2021)
doi: https://doi.org/10.1038/d41586-021-02529-z

Related Articles

The bird librarian

Sharing space with 34 million dead insects

To look after these birds is to ‘fall in love’ with them

Subjects

Careers

Animal behaviour

Ecology

Latest on:

Careers

Cash boost looms for historically Black US colleges and universities
Career News 20 SEP 21

Australian funder backflips on controversial preprint ban
News 20 SEP 21

Stop undervaluing smaller institutions
Career Column 17 SEP 21

Animal behaviour

No hands, no problem: clever parrots craft and wield tools
Research Highlight 03 SEP 21

Australia’s cane toads evolved as cannibals with frightening speed
News 25 AUG 21

Clever orangutans invent nutcrackers from scratch
Research Highlight 18 AUG 21

Ecology

A spotlight on seafood for global human nutrition
News & Views 15 SEP 21

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

Preventing spillover as a key strategy against pandemics
Correspondence 14 SEP 21

Jobs

Senior Laboratory Research Scientist

Francis Crick Institute
London, United Kingdom

Bioinformatics Officer

Francis Crick Institute
London, United Kingdom

Laboratory Research Scientist – Human Embryo and Stem Cell Unit

Francis Crick Institute
London, United Kingdom

Postdoc Position on Nitrous Oxide Turnover in the Ocean

University of Southern Denmark (SDU)
Odense M, Denmark More