Ecology

1.
McRae, L. et al. Arctic Species Trend Index 2010. Tracking Trends in Arctic Wildlife (CAFF International Secretariat, 2010).
2.
Lameris, T. K. et al. Potential for an Arctic-breeding migratory bird to adjust spring migration phenology to Arctic amplification. Glob. Change Biol. 23, 4058–4067 (2017).
Article  Google Scholar 

3.
Trautmann, S. in Bird Species (ed. Tietze, D. T.) 217–234 (Springer, 2018).

4.
Zurell, D., Graham, C. H., Gallien, L., Thuiller, W. & Zimmermann, N. E. Long-distance migratory birds threatened by multiple independent risks from global change. Nat. Clim. Change 8, 992–996 (2018).
ADS  Article  Google Scholar 

5.
Bay, R. A. et al. Genomic signals of selection predict climate-driven population declines in a migratory bird. Science 359, 83–86 (2018).
ADS  CAS  Article  Google Scholar 

6.
White, C. M., Cade, T. J. & Enderson, J. H. Peregrine Falcons of the World (Lynx, 2013).

7.
Clark, P. U. et al. The last glacial maximum. Science 325, 710–714 (2009).
ADS  CAS  PubMed  Article  PubMed Central  Google Scholar 

8.
Otto-Bliesner, B. L., Marshall, S. J., Overpeck, J. T., Miller, G. H. & Hu, A. Simulating Arctic climate warmth and icefield retreat in the last interglaciation. Science 311, 1751–1753 (2006).
ADS  CAS  PubMed  Article  PubMed Central  Google Scholar 

9.
Brambilla, M., Rubolini, D. & Guidali, F. Factors affecting breeding habitat selection in a cliff-nesting peregrine Falco peregrinus population. J. Ornithol. 147, 428–435 (2006).
Article  Google Scholar 

10.
Hausdorff, F. Bemerkung über den Inhalt von Punktmengen. Math. Ann. 75, 428–433 (1914).
MathSciNet  MATH  Article  Google Scholar 

11.
Pulido, F. The genetics and evolution of avian migration. Bioscience 57, 165–174 (2007).
Article  Google Scholar 

12.
Perdeck, A. C. An experiment on the ending of autumn migration in starlings. Ardea 52, 133–139 (1964).
Google Scholar 

13.
Delmore, K. E., Toews, D. P., Germain, R. R., Owens, G. L. & Irwin, D. E. The genetics of seasonal migration and plumage color. Curr. Biol. 26, 2167–2173 (2016).
CAS  PubMed  Article  PubMed Central  Google Scholar 

14.
Impey, S. et al. Stimulation of cAMP response element (CRE)-mediated transcription during contextual learning. Nat. Neurosci. 1, 595–601 (1998).
CAS  PubMed  Article  PubMed Central  Google Scholar 

15.
Bourtchuladze, R. et al. Deficient long-term memory in mice with a targeted mutation of the cAMP-responsive element-binding protein. Cell 79, 59–68 (1994).
CAS  PubMed  Article  PubMed Central  Google Scholar 

16.
Buenrostro, J. D., Giresi, P. G., Zaba, L. C., Chang, H. Y. & Greenleaf, W. J. Transposition of native chromatin for fast and sensitive epigenomic profiling of open chromatin, DNA-binding proteins and nucleosome position. Nat. Methods 10, 1213–1218 (2013).
CAS  PubMed  PubMed Central  Article  Google Scholar 

17.
Mayr, B. & Montminy, M. Transcriptional regulation by the phosphorylation-dependent factor CREB. Nat. Rev. Mol. Cell Biol. 2, 599–609 (2001).
CAS  PubMed  Article  PubMed Central  Google Scholar 

18.
Iguchi-Ariga, S. M. & Schaffner, W. CpG methylation of the cAMP-responsive enhancer/promoter sequence TGACGTCA abolishes specific factor binding as well as transcriptional activation. Genes Dev. 3, 612–619 (1989).
CAS  PubMed  Article  PubMed Central  Google Scholar 

19.
Bartsch, D. et al. Aplysia CREB2 represses long-term facilitation: relief of repression converts transient facilitation into long-term functional and structural change. Cell 83, 979–992 (1995).
CAS  PubMed  Article  PubMed Central  Google Scholar 

20.
Wieczorek, L. et al. Absence of Ca2+-stimulated adenylyl cyclases leads to reduced synaptic plasticity and impaired experience-dependent fear memory. Transl. Psychiatry 2, e126 (2012).
CAS  PubMed  PubMed Central  Article  Google Scholar 

21.
Rosenegger, D., Wright, C. & Lukowiak, K. A quantitative proteomic analysis of long-term memory. Mol. Brain 3, 9 (2010).
PubMed  PubMed Central  Article  CAS  Google Scholar 

22.
Ferguson, G. D. & Storm, D. R. Why calcium-stimulated adenylyl cyclases? Physiology (Bethesda) 19, 271–276 (2004).
CAS  Google Scholar 

23.
Zhang, M. et al. Ca-stimulated type 8 adenylyl cyclase is required for rapid acquisition of novel spatial information and for working/episodic-like memory. J. Neurosci. 28, 4736–4744 (2008).
CAS  PubMed  PubMed Central  Article  Google Scholar 

24.
Yin, J. C. & Tully, T. CREB and the formation of long-term memory. Curr. Opin. Neurobiol. 6, 264–268 (1996).
CAS  PubMed  Article  PubMed Central  Google Scholar 

25.
Wauchope, H. S. et al. Rapid climate-driven loss of breeding habitat for Arctic migratory birds. Glob. Change Biol. 23, 1085–1094 (2017).
ADS  Article  Google Scholar 

26.
Lok, T., Overdijk, O. & Piersma, T. The cost of migration: spoonbills suffer higher mortality during trans-Saharan spring migrations only. Biol. Lett. 11, 20140944 (2015).
PubMed  PubMed Central  Article  Google Scholar 

27.
Brown, J. W. et al. Appraisal of the consequences of the DDT-induced bottleneck on the level and geographic distribution of neutral genetic variation in Canadian peregrine falcons, Falco peregrinus. Mol. Ecol. 16, 327–343 (2007).
CAS  PubMed  Article  PubMed Central  Google Scholar 

28.
Wilcove, D. S. & Wikelski, M. Going, going, gone: is animal migration disappearing. PLoS Biol. 6, e188 (2008).
PubMed  PubMed Central  Article  CAS  Google Scholar 

29.
Mueller, J. C., Pulido, F. & Kempenaers, B. Identification of a gene associated with avian migratory behaviour. Proc. R. Soc. Lond. B 278, 2848–2856 (2011).
CAS  Google Scholar 

30.
Peterson, M. P. et al. Variation in candidate genes CLOCK and ADCYAP1 does not consistently predict differences in migratory behavior in the songbird genus Junco. F1000Res. 2, 115 (2013).
ADS  PubMed  PubMed Central  Article  Google Scholar 

31.
Douglas, D. C. et al. Moderating Argos location errors in animal tracking data. Methods Ecol. Evol. 3, 999–1007 (2012).
Article  Google Scholar 

32.
Mueller, T., O’Hara, R. B., Converse, S. J., Urbanek, R. P. & Fagan, W. F. Social learning of migratory performance. Science 341, 999–1002 (2013).
ADS  CAS  Article  Google Scholar 

33.
Trierweiler, C. et al. Migratory connectivity and population-specific migration routes in a long-distance migratory bird. Proc. R. Soc. Lond. B 281, 20132897 (2014).
Google Scholar 

34.
Ambrosini, R., Møller, A. P. & Saino, N. A quantitative measure of migratory connectivity. J. Theor. Biol. 257, 203–211 (2009).
MathSciNet  PubMed  Article  Google Scholar 

35.
Baddeley, A., Rubak, E. & Turner, R. Spatial Point Patterns: Methodology and Applications with R (Chapman and Hall/CRC, 2015).

36.
López-López, D. P., García-Ripollés, C. & Urios, V. Individual repeatability in timing and spatial flexibility of migration routes of trans-Saharan migratory raptors. Curr. Zool. 60, 642–652 (2014).
Article  Google Scholar 

37.
Benhamou, S. How to reliably estimate the tortuosity of an animal’s path: straightness, sinuosity, or fractal dimension? J. Theor. Biol. 229, 209–220 (2004).
MathSciNet  PubMed  MATH  Article  Google Scholar 

38.
Stoffel, M. A., Nakagawa, S. & Schielzeth, H. rptR: repeatability estimation and variance decomposition by generalized linear mixed‐effects models. Methods Ecol. Evol. 8, 1639–1644 (2017).
Article  Google Scholar 

39.
Cohen, J. Statistical Power Analysis for the Behavioral Sciences (Routledge Academic, 1988).

40.
Ganusevich, S. A. et al. Autumn migration and wintering areas of peregrine falcons Falco peregrinus nesting on the Kola Peninsula, northern Russia. Ibis 146, 291–297 (2004).
Article  Google Scholar 

41.
Bolger, A. M., Lohse, M. & Usadel, B. Trimmomatic: a flexible trimmer for Illumina sequence data. Bioinformatics 30, 2114–2120 (2014).
CAS  PubMed  PubMed Central  Google Scholar 

42.
Zhao, S. et al. Whole-genome sequencing of giant pandas provides insights into demographic history and local adaptation. Nat. Genet. 45, 67–71 (2013).
CAS  PubMed  Article  PubMed Central  Google Scholar 

43.
Damas, J. et al. Upgrading short-read animal genome assemblies to chromosome level using comparative genomics and a universal probe set. Genome Res. 27, 875–884 (2017).
CAS  PubMed  PubMed Central  Article  Google Scholar 

44.
Zhan, X. et al. Peregrine and saker falcon genome sequences provide insights into evolution of a predatory lifestyle. Nat. Genet. 45, 563–566 (2013).
CAS  PubMed  Article  PubMed Central  Google Scholar 

45.
Li, H. & Durbin, R. Fast and accurate short read alignment with Burrows–Wheeler transform. Bioinformatics 25, 1754–1760 (2009).
CAS  PubMed  PubMed Central  Article  Google Scholar 

46.
DePristo, M. A. et al. A framework for variation discovery and genotyping using next-generation DNA sequencing data. Nat. Genet. 43, 491–498 (2011).
CAS  PubMed  PubMed Central  Article  Google Scholar 

47.
Rodríguez-Ramilo, S. T. & Wang, J. The effect of close relatives on unsupervised Bayesian clustering algorithms in population genetic structure analysis. Mol. Ecol. Resour. 12, 873–884 (2012).
PubMed  Article  PubMed Central  Google Scholar 

48.
Purcell, S. et al. PLINK: a tool set for whole-genome association and population-based linkage analyses. Am. J. Hum. Genet. 81, 559–575 (2007).
CAS  PubMed  PubMed Central  Article  Google Scholar 

49.
Schliep, K. P. phangorn: phylogenetic analysis in R. Bioinformatics 27, 592–593 (2011).
CAS  PubMed  Article  PubMed Central  Google Scholar 

50.
Tang, H. et al. Genetic structure, self-identified race/ethnicity, and confounding in case–control association studies. Am. J. Hum. Genet. 76, 268–275 (2005).
CAS  PubMed  Article  PubMed Central  Google Scholar 

51.
Terhorst, J., Kamm, J. A. & Song, Y. S. Robust and scalable inference of population history from hundreds of unphased whole genomes. Nat. Genet. 49, 303–309 (2017).
CAS  PubMed  Article  PubMed Central  Google Scholar 

52.
Staab, P. R., Zhu, S., Metzler, D. & Lunter, G. scrm: efficiently simulating long sequences using the approximated coalescent with recombination. Bioinformatics 31, 1680–1682 (2015).
CAS  PubMed  PubMed Central  Article  Google Scholar 

53.
Pudlo, P. et al. Reliable ABC model choice via random forests. Bioinformatics 32, 859–866 (2016).
CAS  PubMed  Article  PubMed Central  Google Scholar 

54.
Csilléry, K., François, O. & Blum, M. G. abc: an R package for approximate Bayesian computation (ABC). Methods Ecol. Evol. 3, 475–479 (2012).
Article  Google Scholar 

55.
Hijmans, R. J., Phillips, S., Leathwick, J. & Elith, J. dismo: species distribution modeling. R package version 1.3-3 https://cran.r-project.org/package=dismo (2020).

56.
Calenge, C. adhabitatHR: home range estimation. R package version 0.4.19 https://cran.r-project.org/package=adehabitatHR (2021).

57.
Fick, S. E. & Hijmans, R. J. WorldClim2: new 1‐km spatial resolution climate surfaces for global land areas. Int. J. Climatol. 37, 4302–4315 (2017).
Article  Google Scholar 

58.
Beyer, R. M., Krapp, M. & Manica, A. High-resolution terrestrial climate, bioclimate and vegetation for the last 120,000 years. Sci. Data 7, 236 (2020).
PubMed  PubMed Central  Article  Google Scholar 

59.
Cao, X., Tian, F., Dallmeyer, A. & Herzschuh, U. Northern Hemisphere biome changes ( > 30° N) since 40 cal ka bp and their driving factors inferred from model-data comparisons. Quat. Sci. Rev. 220, 291–309 (2019).

60.
Borchers, H. W. pracma: practical numerical math functions. R package version 2.3.3 https://cran.r-project.org/package=pracma (2021).

61.
Danecek, P. et al. The variant call format and VCFtools. Bioinformatics 27, 2156–2158 (2011).
CAS  PubMed  PubMed Central  Article  Google Scholar 

62.
Beck, H. E. et al. Present and future Köppen–Geiger climate classification maps at 1-km resolution. Sci. Data 5, 180214 (2018).
PubMed  PubMed Central  Article  Google Scholar 

63.
Sabeti, P. C. et al. Genome-wide detection and characterization of positive selection in human populations. Nature 449, 913–918 (2007).
ADS  CAS  PubMed  PubMed Central  Article  Google Scholar 

64.
Beissinger, T. M., Rosa, G. J., Kaeppler, S. M., Gianola, D. & de Leon, N. Defining window-boundaries for genomic analyses using smoothing spline techniques. Genet. Sel. Evol. 47, 30 (2015).
PubMed  PubMed Central  Article  CAS  Google Scholar 

65.
Szpiech, Z. A. & Hernandez, R. D. selscan: an efficient multithreaded program to perform EHH-based scans for positive selection. Mol. Biol. Evol. 31, 2824–2827 (2014).
CAS  PubMed  PubMed Central  Article  Google Scholar 

66.
Browning, S. R. & Browning, B. L. Rapid and accurate haplotype phasing and missing-data inference for whole-genome association studies by use of localized haplotype clustering. Am. J. Hum. Genet. 81, 1084–1097 (2007).
CAS  PubMed  PubMed Central  Article  Google Scholar 

67.
Zheng, G. X. et al. Haplotyping germline and cancer genomes with high-throughput linked-read sequencing. Nat. Biotechnol. 34, 303–311 (2016).
CAS  PubMed  PubMed Central  Article  Google Scholar 

68.
François, O., Martins, H., Caye, K. & Schoville, S. D. Controlling false discoveries in genome scans for selection. Mol. Ecol. 25, 454–469 (2016).
PubMed  Article  CAS  PubMed Central  Google Scholar 

69.
Fariello, M. I., Boitard, S., Naya, H., SanCristobal, M. & Servin, B. Detecting signatures of selection through haplotype differentiation among hierarchically structured populations. Genetics 193, 929–941 (2013).
PubMed  PubMed Central  Article  Google Scholar 

70.
Bonhomme, M. et al. Detecting selection in population trees: the Lewontin and Krakauer test extended. Genetics 186, 241–262 (2010).
PubMed  PubMed Central  Article  Google Scholar 

71.
Frichot, E. & François, O. LEA: an R package for landscape and ecological association studies. Methods Ecol. Evol. 6, 925–929 (2015).
Article  Google Scholar 

72.
Pan, S. et al. Population transcriptomes reveal synergistic responses of DNA polymorphism and RNA expression to extreme environments on the Qinghai–Tibetan Plateau in a predatory bird. Mol. Ecol. 26, 2993–3010 (2017).
CAS  PubMed  Article  Google Scholar 

73.
Zhang, Y. et al. An RNA-sequencing transcriptome and splicing database of glia, neurons, and vascular cells of the cerebral cortex. J. Neurosci. 34, 11929–11947 (2014).
CAS  PubMed  PubMed Central  Article  Google Scholar 

74.
Yang, L. et al. TFBSshape: a motif database for DNA shape features of transcription factor binding sites. Nucleic Acids Res. 42, D148–D155 (2014).
CAS  PubMed  Article  Google Scholar 

75.
Buenrostro, J. D., Wu, B., Chang, H. Y. & Greenleaf, W. J. ATAC-seq: a method for assaying chromatin accessibility genome-wide. Curr. Protoc. Mol. Biol. 109, 21–29 (2015).
PubMed  PubMed Central  Article  Google Scholar 

76.
Li, R. et al. De novo assembly of human genomes with massively parallel short read sequencing. Genome Res. 20, 265–272 (2010).
CAS  PubMed  PubMed Central  Article  Google Scholar 

77.
Barbato, M., Orozco-terWengel, P., Tapio, M. & Bruford, M. W. SNeP: a tool to estimate trends in recent effective population size trajectories using genome-wide SNP data. Front. Genet. 6, 109 (2015).
PubMed  PubMed Central  Article  CAS  Google Scholar 

78.
Pitt, D. et al. Demography and rapid local adaptation shape Creole cattle genome diversity in the tropics. Evol. Appl. 12, 105–122 (2019).
PubMed  Article  Google Scholar 

79.
Carlzon, L., Karlsson, A., Falk, K., Liess, A. & Møller, S. Extreme weather affects peregrine falcon (Falco peregrinus tundrius) breeding success in South Greenland. Ornis Hungarica 26, 38–50 (2018).
Article  Google Scholar 

80.
Franke, A. et al. Status and trends of circumpolar peregrine falcon and gyrfalcon populations. Ambio 49, 762–783 (2020).
PubMed  Article  PubMed Central  Google Scholar  More

1.
Elias, M., Gompert, Z., Jiggins, C. & Willmott, K. Mutualistic interactions drive ecological niche convergence in a diverse butterfly community. PLoS Biol. 6, e300 (2008).
PubMed Central  Article  CAS  PubMed  Google Scholar 
2.
Newman, E., Manning, J. & Anderson, B. Local adaptation: Mechanical fit between floral ecotypes of Nerine humilis (Amaryllidaceae) and pollinator communities. Evolution 69, 2262–2275 (2015).
PubMed  Article  Google Scholar 

3.
Anderson, B., Ros, P., Wiese, T. J. & Ellis, A. G. Intraspecific divergence and convergence of floral tube length in specialized pollination interactions. Proc. R. Soc. B 281, 20141420 (2014).
PubMed  Article  Google Scholar 

4.
Jordano, P. Angiosperm fleshy fruits and seed dispersers: A comparative analysis of adaptation and constraints in plant-animal interactions. Am. Nat. 145, 163–191 (1995).
Article  Google Scholar 

5.
Müller, F. Ituna and Thyridia: a remarkable case of mimicry in butterflies. Trans. Entomol. Soc. Lond. 1879, 20–29 (1879).
Google Scholar 

6.
Guimarães, P. R. Jr., Jordano, P. & Thompson, J. N. Evolution and coevolution in mutualistic networks. Ecol. Lett. 14, 877–885 (2011).
PubMed  Article  Google Scholar 

7.
Pinheiro, C. E. G, Freitas, A. V. L., Campos, V. C., DeVries, P. J. & Penz, C. M. Both palatable and unpalatable butterflies use bright colors to signal difficulty of capture to predators. Neotrop. Entomol. 45, 107–113 (2016).
CAS  PubMed  Article  Google Scholar 

8.
Kapan, D. D. Three-butterfly system provides a field test of müllerian mimicry. Nature 409, 338–340 (2001).
ADS  CAS  PubMed  Article  Google Scholar 

9.
Meyer, A. Repeating patterns of mimicry. PLoS Biol. 4, e341 (2006).
PubMed  PubMed Central  Article  CAS  Google Scholar 

10.
Rowland, H. M., Ihalainen, E., Lindström, L., Mappes, J. & Speed, M. P. Co-mimics have a mutualistic relationship despite unequal defences. Nature 448, 64–67 (2007).
ADS  CAS  PubMed  Article  Google Scholar 

11.
Joron, M. & Mallet, J. L. B. Diversity in mimicry: Paradox or paradigm?. Trends Ecol. Evol. 13, 461–466 (1998).
CAS  PubMed  Article  Google Scholar 

12.
Mallet, J. Causes and consequences of a lack of coevolution in müllerian mimicry. Evol. Ecol. 13, 777–806 (1999).
Article  Google Scholar 

13.
Kozak, K. M. et al. Multilocus species trees show the recent adaptive radiation of the mimetic Heliconius butterflies. Syst. Biol. 64, 505–524 (2015).
CAS  PubMed  PubMed Central  Article  Google Scholar 

14.
Joshi, J., Prakash, A. & Kunte, K. Evolutionary assembly of communities in butterfly mimicry rings. Am. Nat. 189, E58–E76 (2017).
PubMed  Article  Google Scholar 

15.
Dumbacher, J. P. & Fleischer, R. C. Phylogenetic evidence for colour pattern convergence in toxic pitohuis: Müllerian mimicry in birds?. Proc. R. Soc. B 268, 1971–1976 (2001).
CAS  PubMed  Article  Google Scholar 

16.
Plowright, R. C. & Owen, R. E. The evolutionary significance of bumble bee color patterns: A mimetic interpretation. Evolution 34, 622–637 (1980).
CAS  PubMed  Article  Google Scholar 

17.
Williams, P. The distribution of bumblebee colour patterns worldwide: possible significance for thermoregulation, crypsis, and warning mimicry. Biol. J. Linn. Soc. 92, 97–118 (2007).
Article  Google Scholar 

18.
Langham, G. M. Specialized avian predators repeatedly attack novel color morphs of Heliconius butterflies. Evolution 58, 2783–2787 (2004).
PubMed  Article  Google Scholar 

19.
Randall, J. E. A review of mimicry in marine fishes. Zool. Stud. 44, 299–328 (2005).
Google Scholar 

20.
Symula, R., Schulte, R. & Summers, K. Molecular phylogenetic evidence for a mimetic radiation in Peruvian poison frogs supports a Müllerian mimicry hypothesis. Proc. R. Soc. B 268, 2415–2421 (2001).
CAS  PubMed  Article  Google Scholar 

21.
Stuckert, A. M. M., Venegas, P. J. & Summers, K. Experimental evidence for predator learning and Müllerian mimicry in Peruvian poison frogs (Ranitomeya, Dendrobatidae). Evol. Ecol. 28, 413–426 (2014).
Article  Google Scholar 

22.
Lev-Yadun, S. Müllerian mimicry in aposematic spiny plants. Plandt Signal Behav. 4, 482–483 (2009).
Article  Google Scholar 

23.
Benson, W. W. Natural selection for Miillerian Mimicry in Heliconius erato in Costa Rica. Science 176, 936–939 (1972).
ADS  CAS  PubMed  Article  Google Scholar 

24.
Pinheiro, C. E. G. Does Müllerian mimicry work in nature? Experiments with Butterflies and Birds (Tyrannidae). Biotropica 35, 356–364 (2003).
Google Scholar 

25.
Beccaloni, G. W. Vertical stratification of ithomiine butterfly (Nymphalidae: Ithomiinae) mimicry complexes: The relationship between adult flight height and larval host-plant height. Biol. J. Linn. Soc. 62, 313–341 (1997).
Google Scholar 

26.
Marek, P. E. & Bond, J. E. A Müllerian mimicry ring in Appalachian millipedes. PNAS 106, 9755–9760 (2009).
ADS  CAS  PubMed  Article  Google Scholar 

27.
Mallet, J. & Gilbert, L. E. Why are there so many mimicry rings? Correlations between habitat, behaviour and mimicry in Heliconius butterflies. Biol. J. Linn. Soc. 55, 159–180 (1995).
Google Scholar 

28.
Joron, M. & Iwasa, Y. The evolution of a Müllerian mimic in a spatially distributed community. J. Theor. Biol. 237, 87–103 (2005).
PubMed  MATH  Article  Google Scholar 

29.
Gompert, Z., Willmott, K. & Elias, M. Heterogeneity in predator micro-habitat use and the maintenance of Müllerian mimetic diversity. J. Theor. Biol. 281, 39–46 (2011).
PubMed  Article  Google Scholar 

30.
Aubier, T. G. & Elias, M. Positive and negative interactions jointly determine the structure of Müllerian mimetic communities. Oikos https://doi.org/10.1111/oik.06789 (2020).
Article  Google Scholar 

31.
Endler, J. A. A Predator’s View of Animal Color Patterns. In Evolutionary Biology (eds Hecht, M. K. et al.) 319–364 (Springer, Boston, 1978).
Google Scholar 

32.
Gamberale-Stille, G. Benefit by contrast: an experiment with live aposematic prey. Behav. Ecol. 12, 768–772 (2001).
Article  Google Scholar 

33.
Cazetta, E., Schaefer, H. M. & Galetti, M. Why are fruits colorful? The relative importance of achromatic and chromatic contrasts for detection by birds. Evol. Ecol. 12, 233–244 (2009).
Article  Google Scholar 

34.
Brakefield, P. M. Polymorphic Müllerian mimicry and interactions with thermal melanism in ladybirds and a soldier beetle: a hypothesis. Biol. J. Linn. Soc. 26, 243–267 (1985).
Article  Google Scholar 

35.
Lindstedt, C., Lindström, L. & Mappes, J. Thermoregulation constrains effective warning signal expression. Evolution 63, 469–478 (2009).
PubMed  Article  Google Scholar 

36.
Dalrymple, R. L. et al. Abiotic and biotic predictors of macroecological patterns in bird and butterfly coloration. Ecol. Monogr. 88, 204–224 (2018).
Article  Google Scholar 

37.
Papageorgis, C. Mimicry in neotropical butterflies. Am. Sci. 63, 522–532 (1975).
ADS  Google Scholar 

38.
DeVries, P. J., Lande, R. & Murray, D. Associations of co-mimetic ithomiine butterflies on small spatial and temporal scales in a neotropical rainforest. Biol. J. Linn. Soc. 67, 73–85 (1999).
Article  Google Scholar 

39.
Willmott, K. R. & Mallet, J. Correlations between adult mimicry and larval host plants in ithomiine butterflies. Proc. R. Soc. B 271, S266–S269 (2004).
PubMed  Article  Google Scholar 

40.
Andreazzi, C. S., Thompson, J. N. & Guimarães, P. R. Network structure and selection asymmetry drive coevolution in species-rich antagonistic interactions. Am. Nat. 190, 99–115 (2017).
PubMed  Article  Google Scholar 

41.
Medeiros, L. P., Garcia, G., Thompson, J. N. & Guimarães, P. R. The geographic mosaic of coevolution in mutualistic networks. PNAS 115, 12017–12022 (2018).
CAS  PubMed  Article  Google Scholar 

42.
Lewis, J. J., Belleghem, S. M. V., Papa, R., Danko, C. G. & Reed, R. D. Many functionally connected loci foster adaptive diversification along a neotropical hybrid zone. Sci. Adv. 6, eabb8617 (2020).
ADS  CAS  PubMed  PubMed Central  Article  Google Scholar 

43.
Willmott, K. R., Robinson Willmott, J. C., Elias, M. & Jiggins, C. D. Maintaining mimicry diversity: optimal warning colour patterns differ among microhabitats in Amazonian clearwing butterflies. Proc. Royal Soc. B 284, 20170744 (2017).
Article  Google Scholar 

44.
Chouteau, M. & Angers, B. The role of predators in maintaining the geographic organization of aposematic signals. Am. Nat. 178, 810–817 (2011).
PubMed  Article  Google Scholar 

45.
Abrams, P. A. The evolution of predator-prey interactions: Theory and evidence. Annu. Rev. Ecol. Evol. Syst. 31, 79–105 (2000).
Article  Google Scholar 

46.
Pinheiro, C. E. G. & Cintra, R. Butterfly predators in the neotropics: Which birds are involved?. J. Lepid. Soc. 71, 109–114 (2017).
Google Scholar 

47.
Beatty, C. D., Beirinckx, K. & Sherratt, T. N. The evolution of müllerian mimicry in multispecies communities. Nature 431, 63–66 (2004).
ADS  CAS  PubMed  Article  Google Scholar 

48.
Ramos, R. R. & Freitas, A. V. L. Population biology and wing color variation in Heliconius erato phyllis (Nymphalidae). J. Lepid. Soc. 53, 11–21 (1999).
Google Scholar 

49.
Seixas, R. R., Santos, S. E., Okada, Y. & Freitas, A. V. L. Population Biology of the Sand Forest Specialist Butterfly Heliconius hermathena hermathena (Hewitson) (Nymphalidae: Heliconiinae) in Central Amazonia). J. Lepid. Soc. 71, 133–140 (2017).
Google Scholar 

50.
Turner, J. R. G. The evolutionary dynamics of batesian and muellerian mimicry: Similarities and differences. Ecol. Entomol. 12, 81–95 (1987).
Article  Google Scholar 

51.
R Core Team. A Language and Environment for Statistical Computing 2016 (R Foundation for Statistical Computing, Vienna, 2016).
Google Scholar 

52.
Sheppard, P. M. et al. Genetics and the evolution of muellerian mimicry in heliconius butterflies. Philos. Trans. R. Soc. Lond. B 308, 433–610 (1985).
ADS  Article  Google Scholar 

53.
Beccaloni, G. W. Ecology, natural history and behaviour of Ithomiine butterflies and their mimics in Ecuador (Lepidoptera: Nymphalidae: Ithomiinae). Trop. Lepid. Res. 8, 103–124 (1997).
Google Scholar 

54.
Uehara-Prado, M. & Freitas, A. V. L. The effect of rainforest fragmentation on species diversity and mimicry ring composition of ithomiine butterflies. Insect Conserv. Divers. 2, 23–28 (2009).
Article  Google Scholar 

55.
Brown, K. S. & Benson, W. W. Adaptive polymorphism associated with multiple müllerian mimicry in Heliconius numata (Lepid. Nymph.). Biotropica 6, 205–228 (1974).
Article  Google Scholar 

56.
Jay, P. et al. Supergene evolution triggered by the introgression of a chromosomal inversion. Curr. Biol. 28, 1839-1845.e3 (2018).
CAS  PubMed  Article  Google Scholar 

57.
Holmes, I. A., Grundler, M. R. & Davis Rabosky, A. R. Predator perspective drives geographic variation in frequency-dependent polymorphism. Am. Nat. 190, E78–E93 (2017).
PubMed  Article  Google Scholar 

58.
Thompson, J. N. The Coevolutionary Process (University of Chicago Press, Chicago, 1994).
Google Scholar 

59.
Bronstein, J. L. Mutualism (Oxford University Press, Oxford, 2015).
Google Scholar 

60.
Brown, K. S. Mimicry, aposematism and crypsis in enotropical Lepidoptera: the importance of dual signals. Bull. Soc. Zool. Fr. 113, 83–101 (1988).
Google Scholar 

61.
Chazot, N. et al. Mutualistic mimicry and filtering by altitude shape the structure of Andean butterfly communities. Am. Nat. 183, 26–39 (2014).
PubMed  Article  Google Scholar 

62.
Rossato, D. O., Kaminski, L. A., Iserhard, C. A. & Duarte, L. Chapter two-more than colours: An eco-evolutionary framework for wing shape diversity in butterflies. In Advances in Insect Physiology (ed. Ffrench-Constant, R. H.) (Academic Press, Cambridge, 2018).
Google Scholar 

63.
Kingsolver, J. G. Thermoregulation, flight, and the evolution of wing pattern in pierid butterflies: The topography of adaptive landscapes. Integr. Comp. Biol. 28, 899–912 (1988).
Google Scholar 

64.
Finkbeiner, S. D., Briscoe, A. D. & Reed, R. D. Warning signals are seductive: Relative contributions of color and pattern to predator avoidance and mate attraction in Heliconius butterflies. Evolution 68, 3410–3420 (2014).
PubMed  Article  Google Scholar 

65.
Bergstrom, C. T. & Lachmann, M. The Red King effect: When the slowest runner wins the coevolutionary race. PNAS 100, 593–598 (2003).
ADS  CAS  PubMed  Article  Google Scholar 

66.
Adler, L. S. & Bronstein, J. L. Attracting antagonists: Does floral nectar increase leaf herbivory?. Ecology 85, 1519–1526 (2004).
Article  Google Scholar 

67.
Siepielski, A. M. & Benkman, C. W. Conflicting selection from an antagonist and a mutualist enhances phenotypic variation in a plant. Evolution 64, 1120–1128 (2010).
PubMed  Article  Google Scholar 

68.
Guimarães, P. R., Pires, M. M., Jordano, P., Bascompte, J. & Thompson, J. N. Indirect effects drive coevolution in mutualistic networks. Nature 550, 511–514 (2017).
ADS  PubMed  Article  CAS  Google Scholar 

69.
Raimundo, R. L. G., Gibert, J. P., Hembry, D. H. & Guimarães, P. R. Conflicting selection in the course of adaptive diversification: The interplay between mutualism and intraspecific competition. Am. Nat. 183, 363–375 (2014).
PubMed  Article  Google Scholar 

70.
Benkman, C. W. Biotic interaction strength and the intensity of selection. Ecol. Lett. 16, 1054–1060 (2013).
PubMed  Article  Google Scholar 

71.
Guimarães, P. R. The Structure of Ecological Networks Across Levels of Organization. Annu. Rev. Ecol. Evol. Syst. 51(1), 433–460 (2020).
MathSciNet  Article  Google Scholar 

72.
Watts, D. J. & Strogatz, S. H. Collective dynamics of ‘small-world’ networks. Nature 393, 440–442 (1998).
ADS  CAS  PubMed  MATH  Article  Google Scholar 

73.
Gibert, J. P., Pires, M. M., Thompson, J. N. & Guimarães, P. R. The spatial structure of antagonistic species affects coevolution in predictable ways. Am. Nat. 182, 578–591 (2013).
PubMed  Article  Google Scholar 

74.
Brown, K. S. The biology of Heliconius and related genera. Annu. Rev. Entomol. 26, 427–457 (1981).
Article  Google Scholar 

75.
Bonebrake, T. C., Ponisio, L. C., Boggs, C. L. & Ehrlich, P. R. More than just indicators: A review of tropical butterfly ecology and conservation. Biol. Conserv. 143, 1831–1841 (2010).
Article  Google Scholar 

76.
Twomey, E., Vestergaard, J. S., Venegas, P. J. & Summers, K. Mimetic divergence and the speciation continuum in the mimic poison frog ranitomeya imitator. Am. Nat. 187, 205–224 (2016).
PubMed  Article  Google Scholar 

77.
Greene, H. W. & McDiarmid, R. W. Coral snake mimicry: Does it occur?. Science 213, 1207–1212 (1981).
ADS  CAS  PubMed  Article  Google Scholar 

78.
Wilson, J. S., Williams, K. A., Forister, M. L., von Dohlen, C. D. & Pitts, J. P. Repeated evolution in overlapping mimicry rings among North American velvet ants. Nat. Commun. 3, 1272 (2012).
ADS  PubMed  Article  CAS  Google Scholar  More

Phenology
Ceratina nigrolabiata excavate new nests mainly in May and June, however, some newly excavated nests were also recorded later in the season (Figs. 1, 2). Active brood nests (Table 1) occurred from half of June and appeared in high proportion through whole July. First full brood nests first occurred at the end of June, but the main peak of full brood nests was in July. Full brood nests were also frequent in August. Full-mature and mature brood nests occurred from the end of July, and they were very frequent through August. Other types of nests occurred mainly in the beginning and at the end of season. At the beginning of the season occurred mainly old hibernacula or adults of C. nigrolabiata visiting nests of other Ceratina. In the late phases of season occurred abandoned nests with only parasites and newly excavated burrows for hibernation.
Figure 1

Nesting cycle of C. nigrolabiata. (a) newly excavated nests—burrow which contains only adult(s) and sometimes fillings. (b) discarded nest—burrow where previous nest was discarded, and there are pollen remnants on the walls (c) active brood nest—nest in phase brood cell provisioning (d) large active brood nest, where egg is present at the top, but young adults already developed at the bottom of nest (f) guarded full brood nest—mother guards this nest (f) plugged full brood nest—nest is unguarded and closed by a thick filling plug (g) orphaned full brood nest—last brood cell partition is thin and above it is commonly pollen from incompletely provisioned brood cell (h) full-mature brood nest—this nest contains juveniles, young adults, and sometimes mother (i) mature brood nest—this nest contains young adults and sometimes mother. All these figures are hypothetical examples, they are not based on concrete dissected nests.

Full size image

Figure 2

Phenology of C. nigrolabiata through nesting season.

Full size image

Table 1 Criteria for classification of nest stages.
Full size table

Type of nest founding
We found two types of newly founded nests. Newly excavated nests, which were built by excavating pith from a twig. Discarded nests are the other type. These nests were built from previous nest of Ceratina (probably other C. nigrolabiata in most cases) by discarding a part of or all original offspring (Figs. S1 and S2). We observed nests of C. nigrolabiata, where nest partitions were destroyed and pollen from brood cells was placed on side of the nest. We suppose that original offspring were discarded out of the nest (and on several occasions, we observed discarding of offspring out of the nest). Pollen provisions of the previous nest owner were usually moved to the sides of the nest (Fig. S1). From newly founded nests, 82.69% (86/104) were newly excavated and 17.30% (18/104) were discarded nests. When we counted only nests founded after half of June, the proportion of discarded nests was 22.78% (18/79). From active brood nests, 4.66% (29/622) had apparent relics of usurpation and discarding.
Presence of parents
Newly excavated nests
In newly founded nests, only male was present in 53.48% of nests (46/86, Table 2), only female was present in 10.46% of nests (9/86) and male and female together were present in 36.04% (31/86) nests. Newly founded nests were on average 5.47 cm long (SD = 4.68, range 1–22.1, N = 86). Nests with only male were on average 3.82 cm long (SD = 3.26, range 1.2–16.7, N = 46), nests with only female were on average 5.73 cm long (SD = 4.72, range 1–14.1, N = 9), nests with both male and female were on average 7.85 cm long (SD = 5.49, range 1.9–22.1, N = 31). Nests with both parents were significantly longer than nests with only a male (Tukey HSD test on logarithmic data, difference = 0.6743, p = 0.0003), but not significantly longer than nests with only a female (Tukey HSD test on logarithmic data, difference 0.4427 p = 0.2256).
Table 2 Presence of individuals of parental generation in different nest stages.
Full size table

Discarded nests
In 72.22% (13/18) of discarded nests one male and one female were present. Female and two males were present in two nests, only a male was present in one nest, only a female was present in one nest and no adult was found in one nest.
Active brood nests
We found male–female pair in 84.72% of nests (527/622), female and two males were found in 1.29% of nests (8/622), female and three males were found in 0.16% (1/622) of nests, no adult was present in 1.76% (11/622) of nests, only male was in 5.6% (35/622) and only female in 6.43% (40/622) of nests.
Full brood nests
Most of full brood nests (73.51%, 493/672) were not guarded by any parent (Table 2). When a full brood nest was guarded, then usually by a female (15.18%, 102/672). Only male was present in 4.31% (29/672) and male and female were present in 7.14% (48/672). Males were significantly more often present in nests, where female was also present, than in nests without a female (Chi-square test, Chi = 81.06, df = 1, p  More

1.
Strong, D. R., Lawton, J. H. & Southwood, S. R. Insects on Plants, Community Patterns and Mechanisms (Blackwell Scientific Publicatons, Hoboken, 1984).
Google Scholar 
2.
Daly, H. V., Doyen, J. T. & Purcell, A. H. Introduction to Insect Biology and Diversity (Oxford University Press, Oxford, 1998).
Google Scholar 

3.
Strong, D. R., Lawton, J. H. & Southwood, T. R. E. Insects on Plants: Community Patterns and Mechanisms (Harvard University Press, Cambridge, 1984).
Google Scholar 

4.
Mitter, C., Farrell, B. D. & Wiegmann, B. The phylogenetic study of adaptive zones: Has phytophagy promoted insect diversification?. Am. Nat 132, 107–128 (1988).
Article  Google Scholar 

5.
Farrell, B. D. “Inordinate fondness” explained: Why are there so many beetles?. Science 281, 555–559 (1998).
CAS  PubMed  Article  Google Scholar 

6.
Marvaldi, A. E., Sequeira, A. S., O’Brien, C. W. & Farrell, B. D. Molecular and morphological phylogenetics of weevils (Coleoptera, Curculionoidea): Do niche shifts accompany diversification?. Syst. Biol 51, 761–785 (2002).
PubMed  Article  Google Scholar 

7.
Futuyma, D. J. Evolution of Host Specificity in Herbivorous Insects: Genetic, Ecological, and Phylogenetic Aspects (Wiley, New York, 1991).
Google Scholar 

8.
Via, S., Bouck, A. C. & Skillman, S. Reproductive isolation between divergent races of pea aphids on two hosts. II. Selection against migrants and hybrids in the parental environments. Evolution 54, 1626–1637 (2000).
CAS  PubMed  Article  Google Scholar 

9.
Nosil, P. Transition rates between specialization and generalization in phytophagous insects. Evolution 56, 1701–1706 (2002).
CAS  PubMed  Article  Google Scholar 

10.
Woodward, F. I. Climate and Plant Distribution (Cambridge University Press, Cambridge, 1987).
Google Scholar 

11.
Pearson, R. G. & Dawson, T. P. Predicting the impacts of climate change on the distribution of species: Are bioclimate envelope models useful?. Global. Ecol. Biogeogr 12, 361–371 (2003).
Article  Google Scholar 

12.
Soberón, J. & Nakamura, M. Niches and distributional areas: Concepts, methods, and assumptions. Proc. Natl. Acad. Sci 106, 19644–19650 (2009).
ADS  PubMed  Article  Google Scholar 

13.
Whittaker, R. H. Evolution and measurement of species diversity. Taxon 21, 213–251 (1972).
Article  Google Scholar 

14.
Vellend, M. Conceptual synthesis in community ecology. Q Rev. Biol 85, 183–206 (2010).
PubMed  Article  Google Scholar 

15.
Novotny, V. Beta diversity of plant-insect food webs in tropical forests: A conceptual framework. Insect. Conserv. Diver 2, 5–9 (2009).
Article  Google Scholar 

16.
Legendre, P., Borcard, D. & Peres-Neto, P. R. Analyzing beta diversity: Partitioning the spatial variation of community composition data. Ecol. Monogr 75, 435–450 (2005).
Article  Google Scholar 

17.
Price, P. W. Resource-driven terrestrial interaction webs. Ecol. Res 17, 241–247 (2002).
Article  Google Scholar 

18.
Tscharntke, T. & Hawkins, B. A. Multi-Trophic Level Interactions (Cambridge University Press, Cambridge, 2002).
Google Scholar 

19.
Pereira, M. L., Matos, M. A., Lewinsohn, T. M. & Almeida, N. M. Trophic level and host specialisation affect beta-diversity in plant–herbivore–parasitoid assemblages. Insect. Conserv. Diver 12, 404–413 (2019).
Article  Google Scholar 

20.
Novotny, V. & Weiblen, G. D. From communities to continents: Beta diversity of herbivorous insects. Ann. Zool. Fenn 42, 463–475 (2005).
Google Scholar 

21.
Lewinsohn, T. M. & Roslin, T. Four ways towards tropical herbivore megadiversity. Ecol. Lett 11, 398–416 (2008).
PubMed  Article  Google Scholar 

22.
Ødegaard, F. Host specificity, alpha- and beta-diversity of phytophagous beetles in two tropical forests in Panama. Biodivers. Conserv 15, 83–105 (2006).
Article  Google Scholar 

23.
Hawkins, B. A. & Porter, E. E. Does herbivore diversity depend on plant diversity? The case of California butterflies. Am. Nat. 161, 40–49 (2003).
PubMed  Article  Google Scholar 

24.
Kemp, J. E., Linder, H. P. & Ellis, A. G. Beta diversity of herbivorous insects is coupled to high species and phylogenetic turnover of plant communities across short spatial scales in the Cape Floristic Region. J. Biogeogr. 44, 1813–1823 (2017).
Article  Google Scholar 

25.
Lawrence, J. F. Synopsis and Classification of Living Organisms, ***Vol 2 (McGraw-Hill, New York, 1982).
Google Scholar 

26.
Hanks, L. M. Influence of the larval host plant on reproductive strategies of cerambycid beetles. Annu. Rev. Entomol. 44, 483–505 (1999).
CAS  PubMed  Article  Google Scholar 

27.
Myers, N., Mittermeier, R. A., Mittermeier, C. G., Fonseca, G. A. B. & Kent, J. Biodiversity hotspots for conservation priorities. Nature 403, 853–857 (2000).
ADS  CAS  PubMed  Article  Google Scholar 

28.
Corff, J. & Marquis, R. Difference between understory and canopy in herbivore community composition and leaf quantity for two oak species in Missouri. Ecol. Entomol. 24, 46–58 (2001).
Article  Google Scholar 

29.
Ulyshen, M. D. & Hanula, J. L. A comparison of the beetle (Coleoptera) fauna captured at two heights above the ground in a North American temperate deciduous forest. Am. Midl. Nat. 158, 260–278 (2007).
Article  Google Scholar 

30.
Groot, P. & Nott, R. Evaluation of traps of six different designs to capture pine sawyer beetles (Coleoptera: Cerambycidae). Agric. Forest. Entomol 3, 107–111 (2002).
Article  Google Scholar 

31.
Miller, D. et al. Ipsenol, ipsdienol, ethanol, and pinene: Trap lure blend for Cerambycidae and Buprestidae (Coleoptera) in Pine Forests of Eastern North America. J. Econ. Entomol. 20, 20 (2015).
Google Scholar 

32.
Simpson, E. H. Measurement of diversity. Nature 163, 688 (1949).
ADS  MATH  Article  Google Scholar 

33.
Baselga, A. Partitioning the turnover and nestedness components of beta diversity. Global. Ecol. Biogeogr 19, 134–143 (2010).
Article  Google Scholar 

34.
Baselga, A. The relationship between species replacement, dissimilarity derived from nestedness, and nestedness. Glob. Ecol. Biogeogr 21, 1223–1232 (2012).
Article  Google Scholar 

35.
Carvalho, J. C. et al. Determining the relative roles of species replacement and species richness differences in generating beta-diversity patterns. Glob. Ecol. Biogeogr. 21, 760–771 (2012).
Article  Google Scholar 

36.
Vavrek, M. J. fossil: Palaeoecological and palaeogeographical analysis tools. Palaeontol. Electron 14, 1 (2011).
Google Scholar 

37.
Zhang, J.-L. Plantlist: Looking Up the Status of Plant Scientific Names based on The Plant List Database (Version 0.3.7) (2018).

38.
Webb, C. O. & Donoghue, M. J. Phylomatic: Tree assembly for applied phylogenetics. Mol. Ecol. Notes 5, 181–183 (2005).
Article  Google Scholar 

39.
Davis, T. J. et al. Darwin’s abominable mystery: Insights from a supertree of the angiosperms. Proc. Natl. Acad. Sci 101, 1904–1909 (2004).
ADS  Article  CAS  Google Scholar 

40.
Kembel, S. W. et al. Picante: R tools for integrating phylogenies and ecology. Bioinformatics 26, 1463–1464 (2010).
CAS  PubMed  Article  Google Scholar 

41.
Whittaker, R. H. Vegetation of the Siskiyou Mountains, Oregon and California. Ecol. Monogr 30, 279–338 (1960).
Article  Google Scholar 

42.
Burnham, K. P. & Anderson, D. R. Model Selection and Multimodel Inference (Springer, Berlin, 2002).
Google Scholar 

43.
R Core Team R: A Language and Environment for Statistical Computing. R Foundation for Statistical Computing, Vienna. https://www.R-project.org (2018).

44.
Chesson, P. Mechanisms of maintenance of species diversity. Annu. Rev. Ecol. Evol. S 31, 343–366 (2000).
Article  Google Scholar 

45.
Whittaker, R. J. Communities and Ecosystems (MacMillan, New York, 1975).
Google Scholar 

46.
Araújo, M. B. & Luoto, M. The importance of biotic interactions for modelling species distributions under climate change. Glob. Ecol. Biogeogr 16, 743–753 (2007).
Article  Google Scholar 

47.
Elton, C. S. Animal Ecology (Sedgwick & Jackson Ltd, London, 1927).
Google Scholar 

48.
Grinnell, J. The niche-relationships of the California Thrasher. Auk 34, 427–433 (1917).
Article  Google Scholar  More

Pot experiment of AG planting
As shown in Fig. 1, compared to CS, the survival rate of 10-year rotation AG decreased, indicating that 2-year-old AG survival rate in RS was lower than that of AG in CS. This confirmed the continued existence of AG continuous cropping obstacles in RS.
The decrease of physicochemical properties and enzyme activity
Plant growth requires water and nutrients. Because soil physicochemical properties influence water and nutrient availability, changes in soil physicochemical properties directly affect AG growth. In the present study, the water content of RS was significantly higher than that of CS under the same management conditions (Table 1). Shu et al.29 found that high soil water content induced root rot disease in AG when sandy loam water content exceeded 30% or that of clay exceeded 50%. Similarly, according to Wang et al.30, the incidence of rust rot positively correlated with soil moisture and rainfall. Therefore, high soil water content, caused by changes in soil physicochemical properties, may negatively affect AG replanting. Furthermore, the pH of RS was significantly lower than that of CS (Table 1). According to Rahman and Punja24, root rot severity at soil pH 5.05 was greater than that at pH 7.0, indicating that acidic conditions can negatively affect AG health. In addition, the available K content in RS was lower than that in CS (Table 1). Sun31 found that AG should be fertilized (N, P, K fertilizer) from emergence to early flowering, when its demand for potassium fertilizer is the highest, suggesting that AG has a high potassium requirement. The levels of ammonium N, nitrate N, available P, and available K, but not of total N and total C, were generally lower in RS than in CS (Table 1), indicating that the cultivation of AG may have long-term negative effects on these soil nutrients. The same trend was observed for soil enzyme activity. Urease, a nickel-containing enzyme, catalyzes the hydrolysis of urea into carbonate and ammonia. Here, urease activity was significantly higher in CS than in RS. Average phosphatase and sucrase activities were also higher in CS than those in RS, although these differences were not significant (Table 2). Yang32 also found that the activities of sucrase, urease, and phosphatase decreased during AG cultivation. In summary, compared to that of CS, RS had lower fertility, but higher soil water content and lower pH, two conditions which are conducive to AG disease, and that may, therefore, present obstacles to AG replanting.
The dual effects of phenolic acids
The results showed that the content of salicylic acid in RS was significantly higher than that in CS. Yang16 found that among the various phenolic acids tested, salicylic acid had the strongest inhibitory effect on AG radicle growth. In our study, higher salicylic acid content in RS may have posed direct autotoxicity to AG. As a major defense hormone, salicylic acid has the function of enhancing immune signals and reprogramming defense transcriptomes33. After planting AG, the soil salicylic acid content increased, which indicated that AG might release more salicylic acid in the growth process to improve immune response to the surrounding environment. Therefore, the role of salicylic acid in the continuous cropping obstacles to AG cultivation deserves further study.
In addition, we found that the content of most phenolic acids, such as p-coumaric, p-hydroxybenzoic, vanillic, caffeic, and cinnamic acid, decreased after AG cultivation, and had not returned to the levels in CS even after 10 years of subsequent crop rotation. AG requires a suitable environment for growth. Before germination in spring, the ginseng farmers’ association uses wheat straw to cover the soil, which not only maintains soil temperature and retains soil moisture, but also improves soil quality and promotes the growth of AG seedlings. Jia et al.34 detected the increase in ferulic, vanillic, cinnamic, and p-hydroxybenzoic acid in a wheat-corn rotation area. In addition, Zheng et al.35 found that straw return, a common method for soil improvement, also increased the concentration of phenolic acids in soil. In our study, the increased phenolic acid content in CS relative to RS may have been beneficial to the growth of AG. Similar to our research results, Jiao et al.36 also found that the content of phenolic acid substances such as syringic, vanillic, p-coumaric, and ferulic acid decreased by 49.1–81% after adding AG root residues (simulating the seasonal AG leaf and fibrous root senescence). Therefore, decreases in the soil contents of some phenolic acids after planting AG may underlie the decline of other soil properties, which is not conducive to the subsequent growth of AG.
As described above, some phenolic acids may be beneficial to the growth of AG; if so, by what mechanism do these beneficial phenolic acids exert their role? Phenolic acids are produced by plants under external stress37,38,39,40. They do have many beneficial functions, such as antibacterial, antioxidant and so on, which can alleviate the stress of plants41. However, with the increase of phenolic acid secretion, some phenolic acids will penetrate into the soil and affect the soil microorganisms. Li et al.42 found that cinnamic acid inhibits Cylindrocarpon destructans (a pathogen of ginseng) growth at high concentrations, while promoting it at low concentrations. Yang et al.43 found that vanillic acid promoted the growth of the pathogens Rhizoctonia solani and Fusarium solani at low concentrations, but inhibited it at high concentrations; many phenolic acid compounds can inhibit the proliferation of Phytophthora cactorum (a pathogenic bacterium that causes AG phytophthora disease) at high concentrations. In addition, Yuan et al.44 found that p-coumaric acid strongly suppressed the in vitro growth of fungi, significantly reducing the decay caused by Alternaria alternata. Therefore, it can be seen that phenolic acids have inhibitory effects on pathogens at higher concentrations. With a decrease in soil phenolic acid content, this inhibitory effect on pathogenic bacteria will be weakened, resulting in an imbalance in the soil microbial composition that affects AG growth performance. Overall, soil phenolic acid content may indirectly affect AG growth performance by affecting soil microorganisms.
The change in the relative abundance of key bacteria
Our results showed that there was no significant difference in bacterial α-diversity between 10-year post-ginseng RS and CS, but there were differences in β-diversity, which reflects community composition and structure, between CS and RS. In other words, there were significant differences in the relative abundance of key bacteria in the bacterial community, such as Chlamydiae (phylum level, RS: 0.28%, CS: 0.10%, P = 0.035), within this phylum, the c_Chlamydiae, o_Chlamydiales, f_Simkaniaceae, and g_uncultured; Acidothermus (genus level, RS: 2.40%, CS: 5.40%, P = 0.030); Sphingomonadales (order level, CS: 2.98%, RS: 1.68%, P = 0.002), Sphingomonadaceae (family level, CS: 2.88%, RS: 1.48%, P = 0.004), genera Novosphingobium (CS: 0.03%, RS: 0.20%, P = 0.035) and Sphingomonas (CS: 2.83%, RS: 1.10%, P = 0.000); Rhodanobacter (CS: 0.38%, RS: 3.45%, P = 0.050); Arthrobacter (CS: 0.03%, RS: 0.43%, P = 0.001); Mizugakiibacter (CS: 0.63%, RS: 2.28%, P = 0.048); Jatrophihabitans (CS: 1.15%, RS: 0.75%, P = 0.048); Pseudomonas (RS: 0.15%, CS: 0.03%, P = 0.029) among others (Fig. 4, see Supplementary Table S2).
There was no difference in soil bacterial α-diversity between RS and CS, which may be due to the recovery of soil bacterial diversity after 10 years of rotation. However, the results of the pot experiment showed that RS still presented continuous cropping obstacles, which indicated that restoring soil microbial α-diversity does not alleviate continuous cropping obstacles for AG. Instead, differences in microbial community composition (i.e., β-diversity), particularly the abundances of bacterial taxa that play key roles, may explain the persistence of AG continuous cropping obstacles in RS after 10 years.
Among the differences in microbial community composition, CS had higher relative abundances of some bacterial genera that may be beneficial bacteria. The genus Acidothermus had the highest abundance, and it contained a single species, A. cellulolyticus, which is thermophilic, acidophilic, and has the ability to produce many thermostable cellulose-degrading enzymes45. Therefore, higher cellulose-degrading capacity might exist in CS than that in RS. Sphingomonas, a bacterium with the ability to decompose mono- and polycyclic aromatic compounds, as well as heterocyclic compounds, was more abundant in CS than RS, suggesting that bacterial decay of recalcitrant plant compounds was also higher in CS than RS. In addition, Sphingomonas not only decomposes monoaromatic phenolic acids but also improves plant stress resistance, and it is considered a plant probiotic46. Similar to our results, Li and Jiang23 found that Jatrophihabitans relative abundance in soil used for AG for 4 years was significantly (P  root rot group  > control group; in addition, compared with CS, there was a higher abundance of Rhodanobacter in the soil in which Korean ginseng (Panax ginseng) was grown49. We also found that this genus might be increased by the influence of Panax plants, which warrants further study. Our results showed that Arthrobacter was higher in the RS group, and Jiang et al.48 also found that the relative abundance of Arthrobacter in the root rot group was higher than that in the healthy root group; therefore, we speculate that Arthrobacter might be a factor causing root rot of P. quinquefolius, leading to a continuous cropping obstacle to AG growth. Our results showed that the abundance of Pseudomonas sp. in RS was higher than that in CS (RS: 0.15%, CS: 0.03%, P = 0.029, see Supplementary Table S2). Tan et al.50 showed that Pseudomonas sp. was the main pathogen causing root rot disease in P. notoginseng. In addition, Jiang et al.48 also found that Pseudomonas is abundant in the rhizosphere soils of diseased ginseng roots. Therefore, it is necessary to further study the effects of Pseudomonas species on AG growth. To sum up, the relative abundances of a large number of bacteria that are either confirmed or potentially harmful to other plants increased in RS, which may be an important factor leading to the occurrence of continuous cropping obstacles in the 10-year post-ginseng rotation soil.
As shown in Fig. 6, there are many correlations among the three factors. The abundances of Acidothermus, Sphingomonas, Jatrophihabitans, and Actinospica were each positively correlated with that of available K, caffeic acid, and cinnamic acid, but negatively correlated with that of salicylic acid. Therefore, the interactions among phenolic acids, microorganisms, and soil nutrients evidenced possible “synergistic” or “antagonistic” effects within the microecosystem. Overall, these complex relationships are the main reason for AG continuous cropping obstacles, but it is still unknown which of these factors plays the primary role. Finally, Nitrobacter, Actinospica, Clostridium sensu stricto 1, Thermosporothrix, Holophaga, and Peptoclostridium, also showed significant differences in abundance between RS and CS (Fig. 4), which also should receive more attention. More

No statistical methods were used to predetermine sample size. The experiments were not randomized, and investigators were not blinded to allocation during experiments and outcome assessment.
Etymology
The designation ‘Azoamicus’ combines the prefix azo- (New Latin, pertaining to nitrogen) with amicus (Latin, masculine noun, friend); thus giving azoamicus (‘friend that pertains to nitrogen’), alluding to its role as denitrifying endosymbiont. ‘Ciliaticola’ combines ciliate (referring to a group of ciliated protozoa) with the suffix -cola (derived from the Latin masculine noun incola, dweller, inhabitant), thus meaning ‘dwelling within a ciliate’.
Geochemical profiling
We carried out sampling for geochemical profiling in September 2016 and October 2018 at a single station located in the deep, southern lake basin of Lake Zug (about 197-m water depth) (47° 06′ 00.8′′ N, 8° 29′ 35.0′′ E). In September 2016, we used a multi-parameter probe to measure conductivity, turbidity, depth (pressure), temperature and pH (XRX 620, RBR). Dissolved oxygen was monitored online with normal and trace micro-optodes (types PSt1 and TOS7, Presens) with detection limits of 125 and 20 nM, respectively, and a response time of 7 s. In October 2018, we used a CTD (CTD60, Sea&Sun Technology) equipped with a Clark-type oxygen sensor (accuracy ± 3%, resolution 0.1%) to record oxygen profiles.
Sample collection
Water for bulk DNA and RNA analyses was collected in September 2016 and October 2018. Sample collection for DNA and RNA extraction in September 2016 has previously been described46. In October 2018, water was sampled with a Niskin bottle (Hydro-Bios) from 160 m, 170 m and 180 m. For each depth, 2 l of lake water was directly filtered on board our boat onto 0.22-μm Sterivex filter cartridges (Merck Millipore) using a peristaltic pump, subsequently purged with RNAlater preservation solution (Life Technologies) and stored at −20 °C until further processing. For fluorescence in situ hybridization (FISH) analyses, water from the same depths was fixed on board the boat with formaldehyde (1.5% final concentration; Electron Microscopy Sciences) and incubated in a chilled cool box for about 6 h before filtration onto 3-μm polycarbonate filters (Merck Millipore). Additional FISH samples using the same approach were collected in May 2019 from 189 m water depth.
Water for incubation experiments and single-ciliate PCR was sampled in May 2019 from 189-m water depth using a 10 l Go-Flo bottle (General Oceanics), filled into 2.5-l glass bottles without headspace, closed with butyl rubber stoppers and kept cold (at about 4 °C) and dark until further handling. During sampling, oxygen contamination was minimized by overflowing the bottle with anoxic lake water.
For combined FISH and differential interference contrast microscopy analyses, individual live ciliates were picked from lake water (from 186-m depth, collected February 2020) and directly fixed on microscope slides. In brief, microscope slides were treated with 0.1 mg ml−1 poly-l-lysine for 10 min at room temperature, washed with MilliQ water and dried. Ciliates were pre-enriched by gravity flow of bulk lake water through a 5-μm membrane filter and picked using a glass capillary under a binocular microscope. Picked ciliates were transferred into a droplet of formaldehyde (2% in 0.1× PBS, pH 7.6) on poly-l-lysine-coated microscope slides, incubated (for 1 h at room temperature) and washed with MilliQ water. FISH was performed as described in ‘Double-labelled oligonucleotide probe fluorescence in situ hybridization and microscopy’.
Nutrient measurements
Water samples for measurements of nutrients (ammonium, NOx and nitrite) were retrieved with a syringe sampler from 15 discrete depths at and below the base of the oxycline. Forty ml of water was directly injected into a 50-ml Falcon tube containing 10 ml of OPA reagent for fluorometric ammonium quantification47. In 2018, ammonium concentration was determined using the same method, except that the lake water was immediately sterile-filtered after sampling and frozen at −20 °C until further processing. For NOx quantification, 10 ml of water was sterile-filtered into a 15-ml Falcon tube and combined nitrate and nitrite concentration was determined by a commercial QuAAtro Segmented Flow Analyzer (SEAL Analytical).
Clone library construction and Sanger sequencing
‘Candidatus A. ciliaticola’-specific 16S rRNA gene primers were designed on the basis of the ‘Ca. A. ciliaticola’ circular metagenome-assembled genome sequence. Primers targeted the intergenic spacer regions about 50 bp up- and downstream of the 16S rRNA gene, resulting in a 1,568-bp-long PCR product. For clone library construction, the ‘Ca. A. ciliaticola’ 16S rRNA gene was amplified by a nested PCR approach from the same DNA extract used for metagenome sequencing obtained in September 2016 from 160-m water depth using the newly designed ‘Ca. A. ciliaticola’-specific primers (eub62A3_29F and eub62A3_1547R) followed by PCR amplification with general bacterial 16S rRNA gene primers (8F and 1492R) (Supplementary Table 2). Cloning and construction of the clone library is described in more detail in Supplementary Methods. Inserts of purified plasmids from five clones were sequenced by Sanger sequencing using the BigDye Terminator v.3.1 sequencing kit (Thermo Fisher Scientific) and primers M13f or M13r. The sequencing PCR contained 3 μl purified plasmid, 0.5 μl 10× sequencing buffer, 0.5 μl primer (10 μM) and 1 μl BigDye reagent. The PCR reactions were performed as follows: 99 cycles (1 °C s−1 ramp) of denaturation (10 s at 96 °C), annealing (5 s at 60 °C) and elongation (4 min at 60 °C). The PCR products were purified using gel filtration (Sephadex G-50 Superfine, Amersham Bioscience) followed by Sanger sequencing (3130xl genetic analyser, Applied Biosystems). The Sanger sequences were quality-trimmed and assembled using Sequencher v.5.4.6 and standard settings before trimming vector and primer sequences.
Probe design for fluorescence in situ hybridization
To visualize ‘Ca. A. ciliaticola’ cells in the environment, we designed a specific FISH probe on the basis of the ‘Ca. A. ciliaticola’ circular metagenome-assembled genome 16S rRNA gene sequence and closely related sequences within the clade eub62A3. The ‘Ca. A. ciliaticola’ 16S rRNA gene sequence was imported into Arb48 v.6.1 and aligned to the SILVA SSU Ref NR 99 132 database using the SINA-Aligner49. A FISH probe specific for ‘Ca. A. ciliaticola’ and most members of clade eub62A3 was designed (probe eub62A3_813 5′ CTAACAGCAAGTTTTCATCGTTTA 3′) (Supplementary Table 3) using the probe design tool implemented in Arb, and further manually refined and evaluated in silico using MathFISH50. The newly designed probe eub62A3_813 targets ‘Ca. A. ciliaticola’ and 78% of the ‘Ca. Azoamicus’ subgroup A and B sequences included in SILVA SSU Ref NR 99 138 (7 out of 9; the 2 sequences that are not targeted belong to ‘Ca. Azoamicus’ subgroup B), and shows no nontarget hits. Some sequences in the database had only 1 or 2 weak mismatches (98% identity with the 16S rRNA gene sequence of ‘Ca. A. ciliaticola’. The remaining reads shared >94% identity with ‘Ca. A. ciliaticola’ and all shared as top hits sequences belonging to ‘Ca. Azoamicus’ subgroup A when blasted against the NCBI nr database (accessed June 2020).
Clone-FISH
Clone-FISH was performed as previously described51. In brief, a purified plasmid containing the ‘Ca. A. ciliaticola’ 16S rRNA gene sequence (as described in ‘Clone library construction and Sanger sequencing’) in the correct orientation (confirmed by PCR using M13F and 1492R primers) was transformed into electrocompetent Escherichia coli JM109(DE3) cells (Promega) by electroporation using the Cell Porator and Voltage Booster System (Gibco) with settings 350 V, 330 μF capacitance, low ohm impedance, fast charge rate and 4 kΩ resistance (Voltage Booster). After electroporation, cells were transferred into SOC medium (Sigma Aldrich), incubated for 1 h at 37 °C and plated onto LB plates containing 100 mg l−1 kanamycin. After incubation overnight at 37 °C, 4 clones were picked and the presence of the insert was checked with PCR (primers M13F and 1492R) as described in ‘Clone library construction and Sanger sequencing’, followed by gel electrophoresis. An insert-positive clone was selected at random and grown in LB medium containing 100 mg l−1 kanamycin at 37 °C until optical density at 600 nm reached 0.37. Transcription of the plasmid insert was induced using isopropyl β-d-1-thiogalactopyranoside (IPTG) (1 mM final concentration). After addition of IPTG, cells were incubated for 1 h at 37 °C followed by addition of 170 mg l−1 chloramphenicol and subsequent incubation for 4 h. Cells were collected by centrifugation, fixed in 2% formaldehyde solution for 1 h at room temperature, washed and stored at 4 °C in phosphate-buffered saline (pH 7.4) containing 50% ethanol until further processing. Formamide melting curves52 were carried out using a ‘Ca. Azoamicus’-specific, HRP-labelled probe (eub62A3_813) (Extended Data Fig. 5). In brief, cells were applied to glass slides. Permeabilization with lysozyme, peroxidase inactivation, hybridization (10%, 30%, 35%, 40%, 45% and 50% formamide) and tyramide signal amplification (Oregon Green 488) was performed as previously described53. For each formamide concentration, images of three fields of view were recorded using the same exposure time for all formamide concentrations, which was optimized at 10% formamide all same settings using an Axio Imager 2 microscope (Zeiss) and analysed using Daime 2.154.
Double-labelled oligonucleotide probe fluorescence in situ hybridization and microscopy
Hybridization with double-labelled oligonucleotide probes (terminally double-labelled with Atto488 dye; details of the probe are in Supplementary Table 3) (Biomers) and counterstaining with DAPI was performed as previously described55. In brief, samples (either cut filter sections or ciliates picked and fixed on a glass slide, as described in ‘Sample collection’) were incubated in hybridization buffer containing 25% formamide and 5 ng DNA μl−1 probe (the same concentration was used for eub62A3_813 competitor 1 and 2) for 3 h at 46 °C, and subsequently washed in prewarmed washing buffer (5 mM EDTA, 159 mM NaCl) for 30 min at 48 °C. After a brief MilliQ water wash, samples were incubated for 5 min at room temperature in DAPI solution (1 μg ml−1), briefly washed in MilliQ water and air-dried. Filter sections were mounted onto glass slides. Samples were embedded in Prolong Gold Antifade Mountant (Thermo Fisher Scientific), and left to cure for 24 h. Confocal laser scanning and differential interference contrast microscopy were performed on a Zeiss LSM 780 (Zeiss, 63× oil objective, 1.4 numerical aperture, with differential interference contrast prism). Z-stack images were obtained to capture entire ciliate cells and fluorescence images of FISH probe and DAPI signals were projected into 2D for visualization. Cell counting was performed using a Axio Imager 2 microscope (Zeiss) in randomly selected fields of view (40× objective, grid length = 312.5 μm) on polycarbonate filters (3-μm pore size, 32-mm effective filter diameter; Merck Millipore) onto which 0.5 l PFA-fixed lake water was filtered.
For light microscopy, live ciliates were picked from Lake Zug water (May 2019, 189 m) and prepared for live ciliate imaging using light microscopy as previously described56 on an Axio Imager 2 microscope (Zeiss). For image acquisition and processing, Zeiss ZEN (blue edition) 2.3 was used.
Scanning electron microscopy
Ciliates sampled in February 2020 were individually picked under a binocular microscope, and washed in droplets of sterile-filtered lake water. Washed ciliates were then transferred into approximately 200 μl fixative on top of a polylysine-coated silicon wafer (0.1 mg ml−1 poly-l-lysine for 10 min at room temperature) and fixed for 1 h at room temperature. The fixative contained 2.5% glutaraldehyde (v/v, electron microscopy grade) in PHEM buffer57 (pH 7.4). Fixed ciliates attached to the silicon wafer were dehydrated in an ethanol series (30%, 50%, 70%, 80%, 96% and 100%) before automated critical-point drying (EM CPD300, Leica). Scanning electron microscopy was performed on a Quanta FEG 250 (FEI). Images were obtained using FEI xTM v.6.3.6.3123 at an acceleration voltage of 2 kV under high vacuum conditions and were captured using an Everhart–Thornley secondary electron detector. The image represents an integrated and drift corrected array of 128 images captured with a dwell time of 50 ns.
Single-ciliate PCR
Ciliates were picked from Lake Zug water (189 m, 2019) under the binocular microscope with a glass micropipette, and subsequently washed twice in drops of sterile nuclease-free water (Ambion) before being transferred into lysis buffer. DNA was extracted using MasterPure DNA purification kit (Ambion) following the manufacturer’s instructions with a final elution in 1× TE buffer (25 μl). Overall, DNA was extracted from four individual ciliates (S1–S4), five (C5) and ten pooled ciliates (C10) as well as no ciliate (control). 16S (‘Ca. A. ciliaticola’) and 18S rRNA gene sequences (ciliate host) were then separately amplified by PCR using primer pairs eub62A3_29F/_1547R and cil_384F/_1147R. PCR reactions (20 μl) with ‘Ca. A. ciliaticola’-specific primers (eub62A3_29F/_1547R) were performed as described in ‘Clone library construction and Sanger sequencing’ with following modifications: 5 μl template, 58 °C annealing temperature and 40 amplification cycles. PCR with ciliate-specific primers (cil_384F/_1147R) was performed analogously with following modifications: 55 °C annealing temperature, 50 s elongation and 35 amplification cycles. The PCR reactions with primer pairs eub62A3_29F/_1547R were further amplified in a second round of PCR (under the same conditions) using 2 μl PCR reaction from the first round. For each PCR step, successful amplification of products was checked using gel electrophoresis as described in Supplementary Methods. PCR reactions were subsequently purified using QIAquick PCR purification Kit (Qiagen) according to the manufacturer’s instructions with a final elution in sterile nuclease-free water (25 μl) (Ambion). Purified PCR reactions were subsequently sequenced using Sanger sequencing and processed as described in ‘Clone library construction and Sanger sequencing’ with the following modifications: primers eub62A3_29F, eub62A3_1547R (annealing temperature 58 °C) or cil_384F, cil_1147R (annealing temperature 55 °C). Two of the single-ciliate DNA extracts amplified with the endosymbiont-specific primers either showed a faint (S4) or no (S2) band and were not sequenced.
Nucleic acid extraction from lake water
Bulk DNA and RNA extraction as well as metagenome and metatranscriptome sequencing of lake water samples collected in 2016 have previously been described46. For samples from 2018, filters were purged of RNAlater, briefly rinsed with nuclease-free water and removed from the Sterivex cartridge. RNA and DNA was then extracted from separate filters using PowerWater RNA or DNA isolation kits (MoBio Laboratories) according to the manufacturer’s instructions.
Metagenome and bulk metatranscriptome sequencing
For metagenomic sequencing, DNA libraries were prepared as recommended by the NEBNext Ultra II FS DNA Library Prep Kit for Illumina (New England Biolabs). Sequencing-by-synthesis was performed on the Illumina HiSeq2500 sequencer (Illumina Inc.) with the 2 × 250-bp read mode. For metatranscriptomic sequencing, rRNA was removed (Ribo-Zero rRNA Removal Kit for bacteria (Illumina)) and an RNA-sequencing library was prepared according to the NEBNext Ultra II Directional RNA Library Prep Kit for Illumina (New England Biolabs). Sequencing-by-synthesis was performed on the Illumina HiSeq3000 sequencer (Illumina) with the 1 × 150-bp read mode. Library preparation and sequencing was performed by the Max Planck Genome Centre Cologne (http://mpgc.mpipz.mpg.de/home/). Detailed information for each metagenomic and metatranscriptomic dataset can be found in Supplementary Table 4.
Genome assembly and finishing
The genome of ‘Ca. A. ciliaticola’ was reconstructed from a metagenomic dataset sampled in 2018, as follows: metagenomic reads (MG_18_C) were trimmed using Trimmomatic58 v.0.32 as previously described46 and assembled using metaSPAdes59 v.3.13.0 and k-mer lengths of 21, 33, 55, 77, 99 and 127. From this assembly, contigs with high similarity to the previously reconstructed ‘Ca. A. ciliaticola’ genome from the metagenome samples from 2016 (further details are provided in Supplementary Methods) were identified by blastn (identity >95%) and metagenomic reads were mapped back to the contigs using BBmap60 v.35.43 (minid = 0.98). Mapped reads were subsequently reassembled using SPAdes v.3.13.0 with mismatch corrector and coverage threshold enabled (–careful –cov-cutoff 60), resulting in the assembly of a single contig (292,647 bp) that was circularized by trimming the identical overlapping ends (127 bp) giving rise to the closed genome (292,520 bp). The start position was set in an intergenic spacer region near the maximum of the GC disparity curve generated with oriFinder61 v.1.0. The two independently assembled circular metagenome-assembled genomes (from 2016 and 2018 metagenomes (Supplementary Methods)) shared 99.99% identity. For all subsequent analyses, the genome reconstructed from the 2018 dataset was used owing to higher coverage.
Genome annotation and comparative analyses
Genome annotation was performed using a modified version of Prokka62 v.1.13.3 to allow annotation of genes that overlap with tRNA genes. The annotation of key metabolic genes was manually inspected and refined using searches against NCBI non-redundant protein or conserved domain database63. Transmembrane transporters were predicted and classified using the Transporter Automatic Annotation Pipeline web service64 and the Transporter Classification Database65. Pseudogenes were predicted using pseudo finder66 v.0.11 and standard settings. Circular genome maps were created using DNAplotter67 v.18.1.0.
For comparative analyses, protein-coding CDS encoded in the genomes of insect endosymbionts (C. ruddii PV, AP009180.1 and B. aphidicola BCc, CP000263.1), mitochondrion of J. libera (NC_021127) and a free-living relative of ‘Ca. A. ciliaticola’ (L. clemsonensis, CP016397.1) were downloaded from NCBI GenBank. Additionally, protein-coding CDS of the ciliate endosymbiont C. taeniospiralis (PGGB00000000.1) were obtained using Prokka annotation. Classification of functional categories was performed using the eggNOG-mapper v.1 web service68 with mapping mode DIAMOND and standard settings. The classification of the functional category C (energy production and conversion) for ‘Ca. A. ciliaticola’ was modified to also include norB and nirK, which were grouped by eggNOG into different categories (Q and P, respectively).
Multiple sequence alignment of ‘Ca. A. ciliaticola’ and other plastidic and bacterial ATP/ADP translocases was generated using MuscleWS69 v.3.8.31 with default settings implemented in Jalview70 v.2.11.1.0.
Metatranscriptomic analyses of bulk water samples
Raw metatranscriptomic Illumina reads trimming and removal of rRNA sequences was performed as previously described46. Non-rRNA reads were then mapped to the genome of ‘Ca. A. ciliaticola’ using Bowtie271 v.2.2.1.0 and standard parameters. Sorted and indexed BAM files were generated using samtools72 v.0.1.19 and transcripts per feature (based on the Prokka annotation) were quantified using EDGE-pro73 v.1.3.1 and standard settings. Gene transcription was subsequently quantified as transcripts per million74 (TPM) using the formula:

$${{rm{T}}{rm{P}}{rm{M}}}_{{rm{i}}}=frac{{c}_{i}}{{l}_{i}}times frac{1}{{sum }_{j}frac{{c}_{i}}{{l}_{i}}}times {10}^{6}$$

to assign each feature (i) a TPM value, in which c = feature count, l = length (in kb) and j = all features.
Read coverage visualization and plotting was performed using pyGenomeTracks75 (average coverage over 100-bp bins) implemented in deepTools276 v.3.2.0.
Phylogenetic analyses
The full-length 16S rRNA gene sequence was retrieved from the circular metagenome-assembled genome of ‘Ca. A. ciliaticola’ using RNAmmer77 v.1.5, aligned using the SILVA incremental aligner49 (SINA 1.2.11) and imported to the SILVA SSU NR99 database45 (release 132) using ARB48 v.6.1. Additional closely related 16S rRNA gene sequences were identified by BLASTN in the NCBI non-redundant nucleotide database and JGI IMG/M 16S rRNA Public Assembled Metagenomes (retrieved July 2018) and also imported into ARB. A maximum-likelihood phylogenetic tree of 16S rRNA gene sequences was calculated using RAxML78 v.8.2.8 integrated in ARB with the GAMMA model of rate heterogeneity and the GTR substitution model with 100 bootstraps. The alignment was not constrained by a weighting mask or filter. For the complete tree shown in Extended Data Fig. 4, additional ‘Ca. A. ciliaticola’ sequences obtained from a clone library and individual single ciliates were added to the tree using the Parsimony ‘Quick add’ algorithm implemented in ARB.
For the ciliate phylogeny, sequences obtained from Sanger sequencing of picked ciliates were added to the EukRef-Ciliphora30 Plagiopylea subgroup alignment using MAFFT79 online service version 7 (argument:–addfragments). An additional metagenome-assembled full-length 18S rRNA gene sequence assigned to Plagiopylea was obtained using phyloFlash80 v.3.0 from one of the Lake Zug metagenomes (MG_18_C) and also added to the alignment (argument:–add). A phylogenetic tree was calculated using IQ-TREE webserver (http://iqtree.cibiv.univie.ac.at) running IQ-TREE81 1.6.11 with default settings and automatic substitution model selection (best-fit model: TIM2+F+I+G4). Phylogenetic trees were visualized using the Interactive Tree of Life v.4 web service82.
For the ATP/ADP translocase phylogenetic tree, amino acid sequences were retrieved from NCBI RefSeq (250 top hits) and NCBI nr (15 top hits) (both accessed June 2019) using NCBI blastp web-service83 with the amino acid sequence of ATP/ADP translocase sequence of ‘Ca. A. ciliaticola’ (ESZ_00147) as query. Additional amino acid sequences of characterized nucleotide transporters listed in Supplementary Table 8 were also added. After removal of duplicates, sequences were clustered at 95% identity using usearch84 v.8.0.1623 and aligned using MUSCLE69 v.3.8.31. Phylogenetic tree construction using IQ-TREE (best-fit model: LG+F+I+G4) and visualization was performed as described for the 18S rRNA gene phylogenetic tree.
Incubation experiments
Incubation experiments were performed to provide experimental evidence for the denitrifying activity linked to the ciliate host. Three incubations were set up that contained (a) no ciliates (filtered fraction), (b) lake water that was enriched in ciliates (enriched fraction) and (c) bulk lake water. For these experiments, lake water was size-fractionated using a 10-μm polycarbonate filter (Merck Millipore) under N2 atmosphere in a glove bag at 12 °C. Enriched and filtered fractions were obtained by gravity filtration of 0.5 l water until 0.25 l supernatant was left. Thus, in the enriched fraction, ciliates from 0.5 l lake water were concentrated in 0.25 l lake water. In the filtered fraction, organisms larger than 10 μm (including ciliates) were filtered out. Both the enriched water (plus filter) and the filtered water were transferred to separate serum bottles (no headspace) and closed with butyl rubber stoppers. For bulk incubations, unfiltered water was directly filled into 250 ml serum bottles under N2 atmosphere.
Denitrification potential was assessed by measuring the production of 30N2 over time in 15N-nitrite and 15N-nitrate amended incubations by isotope ratio mass spectrometry (Isoprime Precision running ionOS v.4.04, Elementar). A 30 ml helium headspace was set for the 250 ml serum bottles and the water was degassed with helium for 10 min to ensure anoxic conditions and reduce N2 background. A 15N-labelled mixture of nitrate and nitrite (20 μM and 5 μM final concentration, respectively; Sigma Aldrich) was supplied at a 99% labelling percentage and the water was incubated for a total of 30 h at 4 °C in the dark. Subsamples of the headspace were taken at regular time intervals during the incubation by withdrawing 3 ml of the gaseous headspace and simultaneously replacing the removed volume by helium. This gas sample was transferred into 12 ml Exetainers (LabCo) that were pre-filled with helium-degassed water and stored until analysis. 30N2 in the gas samples was measured on an isotope ratio mass spectrometer, and denitrification rates were calculated from the slope of the linear increase of 30N2 in the headspace over the time course of the experiments. The rate of 30N2 production was corrected for dilution of the headspace introduced by subsampling and by the measured total 15N labelling percentage. ‘Ca. A. ciliaticola’-containing ciliate abundance in the different incubation bottles was assessed by microscopic counts after cell fixation, FISH hybridization (eub62A3_813) and DAPI staining as described in ‘Double-labelled oligonucleotide probe fluorescence in situ hybridization and microscopy’.
Statistics and reproducibility
No statistical methods were used to predetermine sample size and experiments were not randomized. The investigators were not blinded to allocation during experiments and outcome assessment.
In Fig. 1a, the scanning electron microscopy image is a representative of n = 6 recorded images that were obtained from 1 experiment. In Fig 1b, the differential interference contrast image is a representative of n = 6 recorded images that were obtained from 1 experiment.
In Fig. 2c, Extended Data Fig. 2i. FISH fluorescence images (eub62A3_813 probe) are representative of n = 33 recorded images that were obtained from 5 independent experiments of 3 biological replicate samples.
In Fig. 2c, Extended Data Fig. 2f, h, j. DAPI fluorescence images are representative of n = 21 recorded images that were obtained from 5 independent experiments of 3 biological replicate samples.
In Extended Data Fig. 2a, the FISH fluorescence image (Arch915 probe) is representative of n = 6 images that were obtained from 1 experiment. In Extended Data Fig. 2b, d, F420 autofluorescence images are representative of n = 11 recorded images that were obtained from 3 independent experiments of 1 sample. In Extended Data Fig. 2c, g, FISH fluorescence images (EUB-I probe) are representative of n = 15 images obtained from 3 independent experiments of 2 biological replicate samples. In Extended Data Fig. 2e, the FISH fluorescence image (NON338 probe) is representative of n = 15 recorded images that were obtained from 3 independent experiments of 2 biological replicate samples.
In Extended Data Fig. 5a, each of the 6 FISH fluorescence images (eub62A3_813 probe) is representative of n = 3 images from 1 experiment.
For the fluorescence images shown, the number of analysed cells was typically much higher (n  > 100) than the ones that were eventually recorded.
Reporting summary
Further information on research design is available in the Nature Research Reporting Summary linked to this paper. More

1.
de Lattin, G. Grundriss der Zoogeographie (Gustav Fischer Verlag, 1976).
2.
Hewitt, G. M. Post-glacial re-colonization of European biota. Biol. J. Linn. Soc. Lond. 68, 87–112. https://doi.org/10.1006/bijl.1999.0332 (1999).
Article  Google Scholar 

3.
Wallace, A. R. The geographical distribution of animals; with a study of the relations of living and extinct faunas as elucidating the past changes of the Earth’s surface (Harper & Brothers, 1876).

4.
Mittermeier, R. A., Myers, N., Mittermeier, C. G. & Robles Gil, P. Hotspots: Earth’s biologically richest and most endangered terrestrial ecoregions (CEMEX, 1999).
Google Scholar 

5.
Médail, F. & Quézel, P. Biodiversity hotspots in the Mediterranean Basin: setting global conservation priorities. Conserv. Biol. 13(6), 1510–1513 (1999).
Article  Google Scholar 

6.
Temple, H. J. & Cuttelod, A. (Compilers). The Status and Distribution of Mediterranean Mammals. Gland, Switzerland and Cambridge (UK: IUCN, vii+32pp, 2009).

7.
Blondel, J. The nature and origin of the vertebrate fauna. pp. 139–163 In: Woodward, C. J. (ed.) The Physical Geography of the Mediterranean (Oxford University Press, Oxford, 2009).

8.
Aulagnier, S., Hafner, P., Mitchell-Jones, A. J., Moutou, F. & Zima, J. Mammals of Europe, North Africa and the Middle East (A&C Black Publishers, 2009).
Google Scholar 

9.
Horáček, I., Hanák, V. & Gaisler, J. Bats of the Palearctic region: a taxonomic and biogeographic review. In Proceedings of the VIIIth European bat research symposium (Vol. 1, pp. 11–157) (Kraków, CIC ISEZ PAN, 2000).

10.
Smith, C. H. A system of world mammal faunal regions. I. Logical and statistical derivation of the regions. J. Biogeogr. 10, 455–466. https://doi.org/10.2307/2844752 (1983).

11.
Dobson, M. Mammal distributions in the western Mediterranean: the role of human intervention. Mammal Rev. 28(2), 77–88 (1998).
Article  Google Scholar 

12.
Sans-Fuentes, M. A. & Ventura, J. Distribution patterns of the small mammals (Insectivora and Rodentia) in a transitional zone between the Eurosiberian and the Mediterranean regions. J. Biogeogr. 27(3), 755–764 (2000).
Article  Google Scholar 

13.
Kryštufek, B. & Vohralík, V. Mammals of Turkey and Cyprus: introduction, checklist, Insectivora (Zgodovinsko društvo za južno Primorsko, 2001).

14.
Kryštufek, B. A quantitative assessment of Balkan mammal diversity. In Balkan Biodiversity (pp. 79–108) (Springer, Dordrecht, 2004).

15.
Kryštufek, B., Vohralík, V. & Janžekovič, F. Mammals of Turkey and Cyprus: Rodentia I: Sciuridae, Dipodidae, Gliridae (Arvicolinae, 2005).
Google Scholar 

16.
Kryštufek, B. & Vohralík, V. Mammals of Turkey and Cyprus, Rodentia II: Cricetinae, Murridae, Spalacidae, Calomyscidae, Capromyidae, Hystricidae Castoridae. J. Mammal. 96, 1–373 (2010).
Google Scholar 

17.
Kryštufek, B., Donev, N. R. & Skok, J. Species richness and distribution of non-volant small mammals along an elevational gradient on a Mediterranean mountain. Mammalia 75(1), 3–11 (2011).
Article  Google Scholar 

18.
Svenning, J. C., Fløjgaard, C. & Baselga, A. Climate, history and neutrality as drivers of mammal beta diversity in Europe: Insights from multiscale deconstruction. J. Anim. Ecol. 80(2), 393–402 (2011).
Article  Google Scholar 

19.
Gaston, K., & Blackburn, T. Pattern and process in macroecology (John Wiley & Sons, 2008).

20.
Darwin, C. On the Origin of Species by Means of Natural Selection (J. Murray, 1859).

21.
Wallace, A. R. Tropical Nature and Other Essays (Macmillan, 1878).

22.
Hawkins, B. A. et al. Energy, water and broad-scale geographic patterns of species richness. Ecology 84, 3105–3117. https://doi.org/10.1890/03-8006 (2002).
Article  Google Scholar 

23.
Hillebrand, H. On the generality of the latitudinal diversity gradient. Am. Nat. 163(2), 192–211 (2004).
Article  Google Scholar 

24.
Kindlmann P, Schödelbauerová I, Dixon AF.G. Inverse latitudinal gradients in species diversity. pp. 246–257 in Storch D. et al. (eds.) Scaling Biodiversity (Cambridge University Press, 2007).

25.
Boone, R. B. & Krohn, W. B. Relationship between avian range limits and plant transition zones in Maine. J. Biogeogr. 27, 471–482 (2000).
Article  Google Scholar 

26.
Storch, D., Evans, K. L. & Gaston, K. J. The species-area-energy relationship in orchids. Ecol. Lett. 8, 487–492. https://doi.org/10.15517/lank.v7i1-2.19504 (2005).
Article  PubMed  Google Scholar 

27.
Valladares, F. et al. Global change and Mediterranean forests: current impacts and potential responses in Forests and Global Change (eds. Burslem, D. F. R. & Simonson, W. D.), 47–75 (Cambridge University Press, 2014).

28.
MacArthur, R. H. Patterns of Species Diversity. Geographical Ecology: Patterns in the Distributions of Species (Harper & Row, 1972).

29.
Whittaker, R. J. & Fernández-Palacios, J. M. Island biogeography: ecology, evolution, and conservation. Oxford University Press (2007).

30.
Sólymos, P. & Lele, S. R. Global pattern and local variation in species-area relationships. Glob. Ecol. Biogeogr. 21, 109–120. https://doi.org/10.1111/j.1466-8238.2011.00655.x (2012).
Article  Google Scholar 

31.
Willig, M. R., Kaufman, D. M. & Stevens, R. D. Latitudinal gradients of biodiversity: patterns, scale, and synthesis. Annu. Rev. Ecol. Evol. Syst. 34, 273–309. https://doi.org/10.1146/annurev.ecolsys.34.012103.144032 (2003).
Article  Google Scholar 

32.
Prevedello, J., Gotelli, N. J. & Metzger, J. A stochastic model for landscape patterns of biodiversity. Ecol. Monogr. 86, 462–479. https://doi.org/10.1002/ecm.1223 (2016).
Article  Google Scholar 

33.
Blondel, J., Aronson, J., Bodiou, J. Y. & Boeuf, G. The Mediteranean region. Biological diversity in space and time (Oxford University Press, 2010).

34.
Vigne, J. D. The large “true” Mediterranean islands as a model for the Holocene human impact on the European vertebrate fauna? Recent data and new reflections. The Holocene history of the European vertebrate fauna. Modern aspects of research, 295–322 (1999).

35.
Harding, A.F., Palutikof, J. & Holt, T. The climate system. pp. 69–88 In: Woodward, C.J. (ed.) The Physical Geography of the Mediterranean (Oxford University Press, Oxford, 2009).

36.
Zdruli, P. Desertification in the Mediterranean Region. Mediterranean year book 2011 (European Institute of the Mediterranean, 2012).

37.
Bilton, D. T. et al. Mediterranean Europe as an area of endemism for small mammals rather than a source for northwards postglacial colonization. Proc. Royal Soc. B 265(1402), 1219–1226 (1998).
CAS  Article  Google Scholar 

38.
Hewitt, G. M. Mediterranean peninsulas: The evolution of hotspots. In Biodiversity hotspots (pp. 123–147) (Springer, Berlin, Heidelberg, 2011).

39.
Bilgin, R. Back to the suture: the distribution of intraspecific genetic diversity in and around Anatolia. Int. J. Mol. Sci. 12, 4080–4103. https://doi.org/10.3390/ijms12064080 (2011).
Article  PubMed  PubMed Central  Google Scholar 

40.
Vigne, J. D. The origins of mammals on the Mediterranean islands as an indicator of early voyaging. Euras. Prehistory 10(1–2), 45–56 (2014).
Google Scholar 

41.
Masseti, M. Mammals of the Mediterranean islands: Homogenisation and the loss of biodiversity. Mammalia 73, 169–202. https://doi.org/10.1515/MAMM.2009.029 (2009).
Article  Google Scholar 

42.
Angelici, F. M., Laurenti, A. & Nappi, A. A. checklist of the mammals of small Italian islands. Hystrix 20, 3–27. https://doi.org/10.4404/hystrix-20.1-4429 (2009).
Article  Google Scholar 

43.
Cunningham, P. L. & Aspinall, S. The diet of Little Owl Athene noctua in the UAE, with notes on Barn Owl Tyto alba and Desert Eagle Owl Bubo (b.) ascalaphus. Tribulus 11, 13–15 (2001).

44.
Taylor, I. R. How owls select their prey: A study of Barn owls Tyto alba and their small mammal prey. Ardea 97, 635–644. https://doi.org/10.5253/078.097.0433 (2009).
Article  Google Scholar 

45.
Yom-Tov, Y. & Wool, D. Do the contents of barn owl pellets accurately represent the proportion of prey species in the field?. Condor 99, 972–976. https://doi.org/10.2307/1370149 (1997).
Article  Google Scholar 

46.
Dodson, P. & Wexlar, D. Taphonomic investigations of owl pellets. Paleobiology 5, 275–284 (1979).
Article  Google Scholar 

47.
Heisler, L., Somers, C. & Poulin, R. Owl pellets: A more effective alternative to conventional trapping for broad-scale studies of small mammal communities. Methods Ecol. Evol. 7, 96–103. https://doi.org/10.1111/2041-210X.12454 (2015).
Article  Google Scholar 

48.
Torre, I., Arrizabalaga, A. & Flaquer, C. Three methods for assessing richness and composition of small mammal communities. J. Mammal. 85, 524–530. https://doi.org/10.1644/BJK-112 (2004).
Article  Google Scholar 

49.
Yalden, D. W. & Morris, P. A. The analysis of owl pellet (Occasional publications)(The Mammal Society, 1990).

50.
Williams, D. F. & Braun, S. E. Comparison of pitfall and conventional traps for sampling small mammal populations. J. Wildl. Manage. 47, 841–845 (1983).
Article  Google Scholar 

51.
Glennon, M. J., Porter, W. F. & Demers, C. L. An alternative field technique for estimating diversity of small-mammal populations. J. Mammal. 83, 734–742. https://doi.org/10.1644/1545-1542 (2002).
Article  Google Scholar 

52.
Morris, P. A., Burgis, M. J., Morris, P. A. & Holloway, R. A method for estimating total body weight of avian prey items in the diet of owls. J. Zool. 210, 642–644 (1986).
Article  Google Scholar 

53.
Vukićević Radić, O., Jovanović, T. B., Matić, R. & Katarinovski, D. Age structure of yellow-necked mouse (Apodemus flavicollis Melchior 1834) in two samples obtained from live traps and owl pellets. Arch. Biol. Sci. 57, 53–56 (2005).

54.
Coda, J., Gomez, D., Steinmann, A. R. & Priotto, J. Small mammals in farmlands of Argentina: Responses to organic and conventional farming. Agric. Ecosyst. Environ. 211, 17–23 (2015).
Article  Google Scholar 

55.
Andrade, A., de Menezes, J. F. S. & Monjeau, A. Are owl pellets good estimators of prey abundance?. J. King Saud Univ. Sci. 28, 239–244. https://doi.org/10.1016/j.jksus.2015.10.007 (2016).
Article  Google Scholar 

56.
Moysi, M., Christou, M., Goutner, V., Kassinis, N. & Iezekiel, S. Spatial and temporal patterns in the diet of barn owl (Tyto alba) in Cyprus. J. Biol. Res-Thessalon. 25(1), 9 (2018).
Article  Google Scholar 

57.
Romano, A., Séchaud, R. & Roulin, A. Global biogeographical patterns in the diet of a cosmopolitan predator. J. Biogeogr. 47, 1467–1481. https://doi.org/10.1111/jbi.13829 (2020).
Article  Google Scholar 

58.
Baquero, R. A. & Tellería, J. L. Species richness, rarity and endemicity of European mammals: A biogeographical approach. Biodivers. Conserv. 10(1), 29–44 (2001).
Article  Google Scholar 

59.
Mitchell-Jones, A. J. et al. The Atlas of European Mammals (T & AD Poyser, 1999).

60.
Kross, S. M., Bourbour, R. P. & Martinico, B. L. Agricultural land use, arn owl diet, and vertebrate pest control implications. Agric. Ecosyst. Environ. 223, 167–174. https://doi.org/10.1016/j.agee.2016.03.002 (2016).
Article  Google Scholar 

61.
Krishnapriya, T. & Ramakrishnan, U. Higher speciation and lower extinction rates influence mammal diversity gradients in Asia. BMC Evol. Biol. 15, 11. https://doi.org/10.1186/s12862-015-0289-1 (2015).
Article  Google Scholar 

62.
Kouki, J., Niemela, P. & Viitasaari, M. Reversed latitudinal gradient in species richness of sawflies (Hymenoptera, Symphyta). Ann. Zool. Fenn. 31, 83–88 (1994).
Google Scholar 

63.
Rabenold, K. N. A reversed latitudinal diversity gradient in avian communities of eastern deciduous forests. Am. Nat. 114, 275–286. https://doi.org/10.1086/283474 (1979).
Article  Google Scholar 

64.
Ruffino, L. & Vidal, E. Early colonization of Mediterranean islands by Rattus rattus: A review of zooarcheological data. Biol. Invasions 12(8), 2389–2394 (2010).
Article  Google Scholar 

65.
Thomes, J. B. Land degradation. pp. 563–581. In: Woodward, C.J. (ed.) The Physical Geography of the Mediterranean (Oxford University Press, Oxford, 2009).

66.
Allen, H. D. Vegetation and ecosystem dynamics. pp. 203–227. In: Woodward, C.J. (ed.) The Physical Geography of the Mediterranean (Oxford University Press, Oxford, 2009).

67.
Dov Por, F. & Dimentman, C. Mare Nostrum. Neogene and anthropic natural history of the Mediterranean basin, with emphasis on the Levant (Pensoft, Sofia-Moscow, 2006).

68.
Zohary, D., Hopi, M. & Weiss, E. Domestication of Plants in the Old World 4th edn. (Oxford University Press, 2012).
Google Scholar 

69.
Roulin, A. Spatial variation in the decline of European birds as shown by the Barn Owl Tyto alba diet. Bird Study 62, 271–275. https://doi.org/10.1080/00063657.2015.1012043 (2015).
Article  Google Scholar 

70.
Pezzo, F. & Morimando, F. Food habits of the barn owl, Tyto alba, in a mediterranean rural area: Comparison with the diet of two sympatric carnivores. Boll. Zool. 62, 369–373. https://doi.org/10.1080/11250009509356091 (1995).
Article  Google Scholar 

71.
Soranzo, N., Alia, R., Provan, J. & Powell, W. Patterns of variation at a mitochondrial sequence-tagged-site locus provides new insights into the postglacial history of European Pinus sylvestris populations. Mol. Ecol. 9, 1205–1211. https://doi.org/10.1046/j.1365-294x.2000.00994.x (2000).
CAS  Article  PubMed  Google Scholar 

72.
van Andel, T. H. The climate and landscape of the middle part of the Weichselian Glaciation in Europe: The stage 3 project. Q. Res. 57, 2–8. https://doi.org/10.1006/qres.2001.2294 (2002).
ADS  Article  Google Scholar 

73.
Johnston, D. W. & Hill, J. M. Prey selection of Common Barn-owls on islands and mainland sites. J. Raptor. Res. 21(1), 3–7 (1987).
Google Scholar 

74.
Sommer, R., Zoller, H., Kock, D., Böhme, W. & Griesau, A. Feeding of the barn owl, Tyto alba with first record of the European free-tailed bat, Tadarida teniotis on the island of Ibiza (Spain, Balearics). Fol. Zool. 54, 364–370 (2005).
Google Scholar 

75.
Kryštufek, B., Reed, J. Pattern and process in Balkan biodiversity – an overview in A quantitative assesment of Balkan mammal diversity (eds. Griffiths, H. I., Kryštufek, B. & Reed, J. M.) 79–108 (Kluwer Academic, 2004).

76.
Ricklefs, R. E. & Lovette, I. J. The roles of island area per se and habitat diversity in the species-area relationships of four Lesser Antillean faunal groups. J. Anim. Ecol. 68, 1142–1160 (1999).
Article  Google Scholar 

77.
Heaney, L. R. Mammalian species richness on islands on the Sunda Shelf Southeast Asia. Oecologia 61, 11–17 (1984).
ADS  Article  Google Scholar 

78.
Carvajal, A. & Adler, G. H. Biogeography of mammals on tropical Pacific islands. J. Biogeogr. 32, 1561–1569. https://doi.org/10.1111/j.1365-2699.2005.01302.x (2005).
Article  Google Scholar 

79.
Millien-Parra, V. & Jaeger, J. J. Island biogeography of the Japanese terrestrial mammal assemblages: An example of a relict fauna. J. Biogeogr. 26, 959–972. https://doi.org/10.1046/j.1365-2699.1999.00346.x (1999).
Article  Google Scholar 

80.
Amori, G., Rizzo Pinna, V., Sammuri, G. & Luiselli, L. Diversity of small mammal communities of the tuscan archipelago: Testing the effects of island size, distance from mainland and human density. Fol. Zool. 64, 161–166. https://doi.org/10.25225/fozo.v64.i2.a9.2015 (2015).

81.
Audoin-Rouzeau, F. & La Vigne, J. D. colonisation de l’Europe par le rat noir (Rattus rattus). Rev. de Paléobiologie 13, 125–145. https://doi.org/10.1134/S1062359011020130 (1994).
Article  Google Scholar 

82.
Towns, D. R., Atkinson, I. A. E. & Daugherty, Ch. H. Have the harmful effects of introduced rats on islands been exaggerated?. Biol. Invasions 8, 863–891. https://doi.org/10.1007/s10530-005-0421-z (2006).
Article  Google Scholar 

83.
Martin, J. L., Thibault, J. C. & Bretagnolle, V. Black rats, island characteristics, and colonial nesting birds in the Mediterranean: Consequences of an ancient introduction. Conserv. Biol. 14, 1452–1466. https://doi.org/10.1046/j.1523-1739.2000.99190.x (2000).
Article  Google Scholar 

84.
Landová, E., Horáček, I. & Frynta, D. Have black rats evolved a culturally-transmitted technique of pinecone opening independently in Cyprus and Israel?. Isr. J. Ecol. Evol. 52(2), 151–158 (2006).
Article  Google Scholar 

85.
Sarà, M. & Morand, S. Island incidence and mainland population density: Mammals from Mediterranean islands. Divers. Distrib. 8, 1–9 (2002).
Article  Google Scholar 

86.
Libois, M. R., Fons, R., Saint Girons, M. C. Le régime alimentaire de la chouette effraie Tyto alba, dans les Pyrénées-orientales. Etude des variations ecogéographiques. Rev. Ecol.-Terre Vie 37, 187–217 (1983).

87.
Di Russo, C. Dati sui micromammiferi da borre di barbacianni, Tyto alba, di un Sito della Sardegna Centro-orientale. Hystrix 2, 57–62. https://doi.org/10.4404/hystrix-2.1-3885 (1987).
Article  Google Scholar 

88.
Guerra, C., García, D. & Alcover, J. A. Unusual foraging patterns of the barn owl, Tyto alba (Strigiformes: Tytonidae), on small islets from the Pityusic archipelago (Western Mediterranean Sea). Fol. Zool. 63, 180–187. https://doi.org/10.25225/fozo.v63.i3.a5.2014 (2014).

89.
Patterson, B. D. & Atmar, W. Nested subsets and the structure of insular mammalian faunas and archipelagos. Biol. J. Linn. Soc. Lond. 28, 65–82. https://doi.org/10.1111/j.1095-8312.1986.tb01749.x (1986).
Article  Google Scholar 

90.
Kutiel, P., Peled, Y. & Geffen, E. The effect of removing shrub cover on annual plants and small mammals in a coastal sand dune ecosystem. Biol. Conserv. 94, 235–242. https://doi.org/10.1016/S0006-3207(99)00172-X (2000).
Article  Google Scholar 

91.
Tores, M., Motro, Y., Motro, U. & Yom-Tov, Y. The barn owl-a selective opportunist predator. Israel J. Zool. 51, 349–360. https://doi.org/10.1560/7862-9E5G-RQJJ-15BE (2005).
Article  Google Scholar 

92.
Obuch, J. & Benda, P. Food of the Barn Owl (Tyto alba) in the Eastern Mediterranean. Slovak Raptor J. 3, 41–50. https://doi.org/10.2478/v10262-012-0032-4 (2009).
Article  Google Scholar 

93.
Anděra, M. & Horáček, I. Determining our mammals (Sobotáles, 2005).

94.
Dor, M. Observations sur les Micromammiferes trouves dans les Pelotes de la Chouette effraye (Tyto alba) en Palestine. Mammalia 11, 50–54 (1947).
Article  Google Scholar 

95.
De Pablo, F. Alimentación de la Lechuza Común (Tyto alba) en Menorca. Bolleti Soc. Hist. Nat. Balear. 43, 15–26 (2000).
Google Scholar 

96.
Rihane, A. Contribution to the study of the diet of Barn Owl Tyto alba in the semi-arid plains of Atlantic Morocco. Alauda 71, 363–369 (2003).
Google Scholar 

97.
Kennedy, C. M., J. R. Oakleaf, D. M. Theobald, Baruch-Mordo, S. & Kiesecker, J. Managing the middle: A shift in conservation priorities based on the global human modification gradient. Global Change Biol. 25(3), 811–826. https://doi.org/10.1111/gcb.14549 (2019).

98.
Kennedy, C. M., Oakleaf, J. R., Theobald, D. M., Baruch-Mordo, S. & Kiesecker, J. Global Human Modification of Terrestrial Systems. Palisades, NY: NASA Socioeconomic Data and Applications Center (SEDAC). https://doi.org/10.7927/edbc-3z60. Accessed DAY MONTH YEAR (2020).

99.
Shannon, C. & Weaver, W. The Mathematical Theory of Communication (The University of Illinois Press, 1964).

100.
R Development Core Team. R: A language and environment for statistical computing. Vienna, Austria: R Found Stat Comp (2011).

101.
Anderson, D. R. & Burnham, K. P. Avoiding pitfalls when using information-theoretic methods. J. Wildl. Manag. 66, 912–918 (2002).
Article  Google Scholar 

102.
Whittingham, M. J., Stephens, P. A., Bradbury, R. B. & Freckleton, R. P. Why do we still use stepwise modelling in ecology and behaviour?. J. Anim. Ecol. 75, 1182–1189. https://doi.org/10.1111/j.1365-2656.2006.01141.x (2006).
Article  PubMed  Google Scholar 

103.
Burnham, K. P., Anderson, D. R. & Huyvaert, K. P. AIC model selection and multimodel inference in behavioral ecology: Some background, observations, and comparisons. Behav. Ecol. Sociobiol. 65, 23–35. https://doi.org/10.1007/s00265-010-1039-4 (2011).
Article  Google Scholar 

104.
ter Braak, C. & Šmilauer, P. Canoco reference manual and user’s quide: software for ordination, version 5.0 (Microcomputer Power, 2012).

105.
StatSoft Inc. Statistica (data analysis software system), version 12. http://www.statsoft.com (2013). More

1.
Jost C, Lawrence CA, Campolongo F, Van De Bund W, Hill S, DeAngelis DL. The effects of mixotrophy on the stability and dynamics of a simple planktonic food web model. Theor Popul Biol. 2004;66:37–51.
PubMed  Article  Google Scholar 
2.
Tittel J, Bissinger V, Zippel B, Gaedke U, Bell E, Lorke A, et al. Mixotrophs combine resource use to outcompete specialists: Implications for aquatic food webs. Proc Natl Acad Sci. 2011;100:12776–81.
Article  CAS  Google Scholar 

3.
Ward BA, Follows MJ. Marine mixotrophy increases trophic transfer efficiency, mean organism size, and vertical carbon flux. Proc Natl Acad Sci. 2016;113:2958–63.
CAS  PubMed  Article  Google Scholar 

4.
Hansen PJ, Tillmann U. Mixotrophy among dinoflagellates—prey selection, physiology and ecological imporance. In: Subba Rao DV, editor. Dinoflagellates: classification, evolution, physiology and ecological significance. Hauppauge, NY, USA: Nova; 2020;201–60.

5.
Unrein F, Gasol JM, Not F, Forn I, Massana R. Mixotrophic haptophytes are key bacterial grazers in oligotrophic coastal waters. ISME J. 2014;8:164–76.
CAS  PubMed  Article  Google Scholar 

6.
Anderson R, Charvet S, Hansen P. Mixotrophy in chlorophytes and haptophytes – effect of irradiance, macronutrient, micronutrient and vitamin limitation. Front Microbiol. 2018;9:1704.
PubMed  PubMed Central  Article  Google Scholar 

7.
Lewitus AJ, Caron DA, Miller KR. Effect of light and glycerol on the organization of the photosynthetic apparatus in the facultative heterotroph Pyrenomonas salina (cryptophyceae). J Phycol. 1991;27:578–87.
Article  Google Scholar 

8.
Du YooY, Seong KA, Jeong HJ, Yih W, Rho J-R, Nam SW, et al. Mixotrophy in the marine red-tide cryptophyte Teleaulax amphioxeia and ingestion and grazing impact of cryptophytes on natural populations of bacteria in Korean coastal waters. Harmful Algae. 2017;68:105–17.
Article  Google Scholar 

9.
Caron DA, Porter KG, Sanders RW. Carbon, nitrogen, and phosphorus budgets for the mixotrophic phytoflagellate Poterioochromonas malhamensis (Chrysophyceae) during bacterial ingestion. Limnol Oceanogr. 1990;35:433–43.
CAS  Article  Google Scholar 

10.
Holen DA, Boraas ME. Mixotrophy in chrysophytes. Chrysophyte algae. Cambridge, UK: Cambridge University Press; 1995;119–40.

11.
Fenchel T. Ecology of heterotrophic microflagellates. II. Bioenerg growth Mar Ecol Prog Ser. 1982;8:225–31.
Article  Google Scholar 

12.
Rottberger J, Gruber A, Boenigk J, Kroth P. Influence of nutrients and light on autotrophic, mixotrophic and heterotrophic freshwater chrysophytes. Aquat Micro Ecol. 2013;71:179–91.
Article  Google Scholar 

13.
Bell EM, Laybourn-Parry J. Mixotrophy in the antarctic phytoflagellate, Pyramimonas gelidicola (Chlorophyta: Prasinophyceae). J Phycol. 2003;39:644–9.
Article  Google Scholar 

14.
McKie-Krisberg ZM, Sanders RW. Phagotrophy by the picoeukaryotic green alga Micromonas: implications for Arctic Oceans. ISME J. 2014;8:1953–61.
CAS  PubMed  PubMed Central  Article  Google Scholar 

15.
McKie-Krisberg ZM, Gast RJ, Sanders RW. Physiological responses of three species of Antarctic mxotrophic phytoflagellates to changes in light and dissolved nutrients. Micro Ecol. 2015;70:21–29.
CAS  Article  Google Scholar 

16.
Paasch A. Physiological and genomic characterization of phagocytosis in green algae. New York, NY, USA: American Museum of Natural History; 2017.

17.
Not F, Latasa M, Scharek R, Viprey M, Karleskind P, Balagué V, et al. Protistan assemblages across the Indian Ocean, with a specific emphasis on the picoeukaryotes. Deep Res Part I Oceanogr Res Pap. 2008;55:1456–73.
Article  Google Scholar 

18.
Shi XL, Marie D, Jardillier L, Scanlan DJ, Vaulot D. Groups without cultured representatives dominate eukaryotic picophytoplankton in the oligotrophic South East Pacific Ocean. PLoS ONE. 2009;4:e7657.
PubMed  PubMed Central  Article  CAS  Google Scholar 

19.
Rii YM, Duhamel S, Bidigare RR, Karl DM, Repeta DJ, Church MJ. Diversity and productivity of photosynthetic picoeukaryotes in biogeochemically distinct regions of the South East Pacific Ocean. Limnol Oceanogr. 2016;61:806–24.
Article  Google Scholar 

20.
Maruyama S, Kim E. A modern descendant of early green algal phagotrophs. Curr Biol. 2013;23:1081–4.
CAS  PubMed  Article  Google Scholar 

21.
O’Kelly C. Flagellar apparatus architecture and the phylogeny of ‘green’ algae: Chlorophytes, Euglenoids, Glaucophytes. In: Menzel D, editor. The cytoskeleton of the algae. Boca Raton: CRC Press; 1992. p. 315–41.
Google Scholar 

22.
Burns JA, Pittis AA, Kim E. Gene-based predictive models of trophic modes suggest Asgard archaea are not phagocytotic. Nat Ecol Evol. 2018;2:697–704.
PubMed  Article  Google Scholar 

23.
Wilken S, Yung CCM, Hamilton M, Hoadley K, Nzongo J, Eckmann C, et al. The need to account for cell biology in characterizing predatory mixotrophs in aquatic environments. Philos Trans R Soc B Biol Sci. 2019;374:20190090.
CAS  Article  Google Scholar 

24.
Inouye I, Hori T, Chihara M. Absolute configuration analysis of the flagellar apparatus of Pterosperma Cristatum (Prasinophyceae) and consideration of Its phylogenetic position. J Phycol. 1990;26:329–44.
Article  Google Scholar 

25.
Bhuiyan MAH, Faria DG, Horiguchi T, Sym SD, Suda S. Taxonomy and phylogeny of Pyramimonas vacuolata sp. nov. (Pyramimonadales, Chlorophyta). Phycologia. 2015;54:323–32.
CAS  Article  Google Scholar 

26.
Adl SM, Bass D, Lane CE, Lukeš J, Schoch CL, Smirnov A, et al. Revisions to the classification, nomenclature, and diversity of eukaryotes. J Eukaryot Microbiol. 2019;66:4–119.
PubMed  PubMed Central  Article  Google Scholar 

27.
Burns JA, Paasch A, Narechania A, Kim E. Comparative genomics of a bacterivorous green alga reveals evolutionary causalities and consequences of phago-mixotrophic mode of nutrition. Genome Biol Evol. 2015;7:3047–61.
CAS  PubMed  PubMed Central  Article  Google Scholar 

28.
Guillard R. Culture of phytoplankton for feeding marine invertebrates. In: Smith WL, Chanley WH, editors. Culture of marine invertebrate animals. 1975. New York: Plenum Press; 1975. p. 22–60.

29.
Cho J-C, Giovannoni SJ. Pelagibaca bermudensis gen. nov., sp. nov., a novel marine bacterium within the Roseobacter clade in the order Rhodobacterales. Int J Syst Evol Microbiol. 2006;56:855–9.
CAS  PubMed  Article  Google Scholar 

30.
Thrash JC, Cho J-C, Ferriera S, Johnson J, Vergin KL, Giovannoni SJ. Genome sequences of Pelagibaca bermudensis HTCC2601T and Maritimibacter alkaliphilus HTCC2654T, the type strains of two marine Roseobacter genera. J Bacteriol. 2010;192:5552–3.
CAS  PubMed  PubMed Central  Article  Google Scholar 

31.
First MR, Park NY, Berrang ME, Meinersmann RJ, Bernhard JM, Gast RJ, et al. Ciliate ingestion and digestion: Flow cytometric measurements and regrowth of a digestion-resistant Campylobacter jejuni. J Eukaryot Microbiol. 2012;59:12–19.
PubMed  Article  Google Scholar 

32.
Sherr BF, Sherr EB, Fallon RD. Use of monodispersed, fluorescently labeled bacteria to estimate in situ protozoan bacterivory. Appl Environ Microbiol. 1987;53:958–65.
CAS  PubMed  PubMed Central  Article  Google Scholar 

33.
Vazquez-Dominguez E, Peters F, Gasol JM, Vaqué D. Measuring the grazing losses of picoplankton: methodological improvements in the use of fluorescently labeled tracers combined with flow cytometry. Aquat Micro Ecol. 1999;20:119–28.
Article  Google Scholar 

34.
Leebens-Mack J, Barker M, Carpenter EJ. One thousand plant transcriptomes and the phylogenomics of green plants. Nature. 2019;574:679–85.
Article  CAS  Google Scholar 

35.
Wincker P. A thousand plants’ phylogeny. Nat Plants. 2019;5:1106–7.
PubMed  Article  PubMed Central  Google Scholar 

36.
Keeling PJ, Burki F, Wilcox HM, Allam B, Allen EE, Amaral-Zettler LA, et al. The marine microbial eukaryote transcriptome sequencing project (MMETSP): illuminating the functional diversity of eukaryotic life in the oceans through transcriptome sequencing. PLOS Biol. 2014;12:e1001889.
PubMed  PubMed Central  Article  CAS  Google Scholar 

37.
Johnson LK, Alexander H, Brown CT. Re-assembly, quality evaluation, and annotation of 678 microbial eukaryotic reference transcriptomes. Gigascience. 2019;8:1–12.
Google Scholar 

38.
Besemer J. GeneMarkS: a self-training method for prediction of gene starts in microbial genomes. Implications for finding sequence motifs in regulatory regions. Nucleic Acids Res. 2001;29:2607–18.
CAS  PubMed  PubMed Central  Article  Google Scholar 

39.
Simão FA, Waterhouse RM, Ioannidis P, Kriventseva EV, Zdobnov EM. BUSCO: assessing genome assembly and annotation completeness with single-copy orthologs. Bioinformatics. 2015;31:3210–2.
PubMed  PubMed Central  Google Scholar 

40.
Gawryluk RMR, Tikhonenkov DV, Hehenberger E, Husnik F, Mylnikov AP, Keeling PJ. Non-photosynthetic predators are sister to red algae. Nature. 2019;572:240–3.
CAS  PubMed  Article  Google Scholar 

41.
Newcombe RG. Interval estimation for the difference between independent proportions: comparison of eleven methods. Stat Med. 1998;17:873–90.
CAS  PubMed  Article  Google Scholar 

42.
Kursa M, Rudnicki W. Feature selection with the Boruta package. J Stat Softw. 2010;36:1–13.
Article  Google Scholar 

43.
Chasset PO. Probabilistic neural network for the R statistical language. https://github.com/chasset/pnn. Github. 2013.

44.
Maia R, Eliason CM, Bitton P-P, Doucet SM, Shawkey MD. pavo: an R package for the analysis, visualization and organization of spectral data. Methods Ecol Evol. 2013;4:906–13.
Google Scholar 

45.
Jimenez V, Burns J, Le Gall F, Not F, Vaulot D. No evidence of phago-mixotropy in Micromonas polaris, the dominant picophytoplankton species in the Arctic. J Phycol. 2021. https://doi.org/10.1111/jpy.13125.

46.
R Core Team. R development core team. R A Lang Environ Stat Comput. Vienna: R Foundation for Statistical Computing; 2016.

47.
Figueroa-Martinez F, Nedelcu AM, Smith DR, Reyes-Prieto A. When the lights go out: the evolutionary fate of free-living colorless green algae. N. Phytol. 2015;206:972–82.
Article  Google Scholar 

48.
Nakada T, Misawa K, Nozaki H. Molecular systematics of Volvocales (Chlorophyceae, Chlorophyta) based on exhaustive 18S rRNA phylogenetic analyses. Mol Phylogenet Evol. 2008;48:281–91.
CAS  PubMed  Article  Google Scholar 

49.
Johnson I. The molecular probes handbook: a guide to fluorescent probes and labeling technologies. 11th ed. Waltham, MA, USA: Life Technologies Corporation; 2010.

50.
Leliaert F, Smith DR, Moreau H, Herron MD, Verbruggen H, Delwiche CF, et al. Phylogeny and molecular evolution of the green algae. CRC Crit Rev Plant Sci. 2012;31:1–46.
Article  Google Scholar 

51.
Leliaert F. Green algae: chlorophyta and streptophyta. Reference module in life sciences. Amsterdam, DK: Elsevier; 2019.

52.
Parke M, Adams I. The Pyramimonas-like motile stage of Halosphaera viridis Schmitz. Bull Res Counc Isr. 1961.

53.
Thorndsen J. Cymbomonas Schiller (Prasinophyceae) reinvestigated by light and electron microscopy. Arch fur Protistenkd. 1988;136:327–36.
Article  Google Scholar 

54.
González JM, Sherr BF, Sherr EB. Digestive enzyme activity as a quantitative measure of protistan grazing: the acid lysozyme assay for bacterivory. Mar Ecol Prog Ser. 1993;100:197–206.
Article  Google Scholar 

55.
Moestrup Ø, Inouye I, Hori T. Ultrastructural studies on Cymbomonas tetramitiformis (Prasinophyceae). I. General structure, scale microstructure, and ontogeny. Can J Bot. 2003;81:657–71.
Article  Google Scholar 

56.
Turmel M, Lopes dos Santos A, Otis C, Sergerie R, Lemieux C. Tracing the evolution of the plastome and mitogenome in the Chloropicophyceae uncovered convergent tRNA gene losses and a variant plastid genetic code. Genome Biol Evol. 2019;11:1275–92.
CAS  PubMed  PubMed Central  Article  Google Scholar 

57.
Lopes dos Santos A, Gourvil P, Tragin M, Noël M, Decelle J, Romac S, et al. Diversity and oceanic distribution of prasinophytes clade VII, the dominant group of green algae in oceanic waters. ISME J. 2017;11:512–28.
PubMed  Article  Google Scholar 

58.
Lemieux C, Turmel M, Otis C, Pombert J-F. A streamlined and predominantly diploid genome in the tiny marine green alga Chloropicon primus. Nat Commun. 2019;10:4061.
PubMed  PubMed Central  Article  Google Scholar 

59.
Zingone A, Borra M, Brunet C, Forlani G. Kooistra WHCF, Procaccini G. Phylogenetic position of Crustomatix stigmatica sp. nov. and Dolichomastix tenuilepis in relation to the mamiellales (Prasinophyceae, Chlorophyta). J Phycol. 2002;38:1024–39.
CAS  Article  Google Scholar 

60.
Liang Z, Geng Y, Ji C, Du H, Wong CE, Zhang Q, et al. Mesostigma viride genome and transcriptome provide insights into the origin and evolution of Streptophyta. Adv Sci. 2020;7:1901850.
CAS  Article  Google Scholar 

61.
Buckley CM, Gopaldass N, Bosmani C, Johnston SA, Soldati T, Insall RH, et al. WASH drives early recycling from macropinosomes and phagosomes to maintain surface phagocytic receptors. Proc Natl Acad Sci. 2016;113:E5906–15.
CAS  PubMed  Article  Google Scholar 

62.
Shpak M, Kugelman JR, Varela-Ramirez A, Aguilera RJ. The phylogeny and evolution of deoxyribonuclease II: An enzyme essential for lysosomal DNA degradation. Mol Phylogenet Evol. 2008;47:841–54.
CAS  PubMed  Article  Google Scholar 

63.
Gast RJ, McKie-Krisberg ZM, Fay SA, Rose JM, Sanders RW. Antarctic mixotrophic protist abundances by microscopy and molecular methods. FEMS Microbiol Ecol. 2014;89:388–401.
CAS  PubMed  Article  Google Scholar 

64.
Mitra A, Flynn KJ, Tillmann U, Raven JA, Caron D, Stoecker DK, et al. Defining planktonic protist functional groups on mechanisms for energy and nutrient acquisition: Incorporation of diverse mixotrophic strategies. Protist. 2016;167:106–20.
CAS  Article  Google Scholar 

65.
Kirkham AR, Lepère C, Jardillier LE, Not F, Bouman H, Mead A, et al. A global perspective on marine photosynthetic picoeukaryote community structure. ISME J. 2013;7:922–36.
CAS  PubMed  PubMed Central  Article  Google Scholar 

66.
Moon-van Der Staay SY, Wachter R De, Vaulot D. Oceanic 18S rDNA sequences from picoplankton reveal unsuspected eukaryotic diversity. Nature. 2001;409:607–10.
CAS  PubMed  Article  Google Scholar 

67.
Worden A. Picoeukaryote diversity in coastal waters of the Pacific Ocean. Aquat Micro Ecol. 2006;43:165–75.
Article  Google Scholar 

68.
Van Hannen EJ, Veninga M, Bloem J, Gons HJ, Laanbroek HJ. Genetic changes in the bacterial community structure associated with protistan grazers. Fundam Appl Limnol. 1999;145:25–38.
Article  Google Scholar 

69.
Jürgens K, Güde H. The potential importance of grazing-resistant bacteria in planktonic systems. Mar Ecol Prog Ser. 1994;112:169–88.
Article  Google Scholar 

70.
Jürgens K, Pernthaler J, Schalla S, Amann R. Morphological and compositional changes in a planktonic bacterial community in response to enhanced protozoan grazing. Appl Environ Microbiol. 1999;65:1241–50.
PubMed  PubMed Central  Article  Google Scholar 

71.
Suzuki M. Effect of protistan bacterivory on coastal bacterioplankton diversity. Aquat Micro Ecol. 1999;20:261–72.
Article  Google Scholar 

72.
Sherr EB, Sherr BF. Significance of predation by protists in aquatic microbial food webs. Antonie van Leeuwenhoek2. 2002;81:293–308.
CAS  Article  Google Scholar 

73.
González J, Sherr EB, Sherr BF. Size-selective grazing on bacteria by natural assemblages of estuarine flagellates and ciliates. Appl Environ Microbiol. 1990;56:583–9.
PubMed  PubMed Central  Article  Google Scholar 

74.
Sherr BF, Sherr EB, McDaniel J. Effect of protistan grazing on the frequency of dividing cells in bacterioplankton assemblages. Appl Environ Microbiol. 1992;58:2381–5.
CAS  PubMed  PubMed Central  Article  Google Scholar 

75.
González J, Sherr EB, Sherr BF. Differential feeding by marine flagellates on growing vs starving bacteria, and on motile vs non-motile bacteria. Mar Ecol Prog Ser. 1993;102:257–67.
Article  Google Scholar 

76.
del Giorgio PA, Gasol JM, Vaqué D, Mura P, Agustí S, Duarte CM. Bacterioplankton community structure: Protists control net production and the proportion of active bacteria in a coastal marine community. Limnol Oceanogr. 1996;41:1169–79.
Article  Google Scholar 

77.
Andersen OK, Goldman JC, Caron DA, Dennett MR. Nutrient cycling in a microflagellate food chain: III. Phosphorus dynamics. Mar Ecol Prog Ser. 1986;31:47–55.
CAS  Article  Google Scholar 

78.
Fenchel T. Protistan filter feeding. Prog Protistol. 1986;1:65–113.
Google Scholar 

79.
Epstein S, Shiaris M. Size selective grazing of coastal bacterioplankton by natural assemblages of pigmented flagellates, colourless flagellates and ciliates. Micro Ecol. 1992;23:211–25.
CAS  Article  Google Scholar 

80.
Montagnes D, Barbosa A, Boenigk J, Davidson K, Jurgens K, Macek M, et al. Selective feeding behaviour of key free-living protists: avenues for continued study. Aquat Micro Ecol. 2008;53:83–98.
Article  Google Scholar 

81.
Pfister G, Arndt H. Food selectivity and feeding behaviour in omnivorous filter-feeding ciliates: a case study for Stylonychia. Eur J Protistol. 1998;34:446–57.
Article  Google Scholar 

82.
Boenigk J, Arndt H. Bacterivory by heterotrophic flagellates: community structure and feeding strategies. Antonie Van Leeuwenhoek. 2002;81:465–80.
PubMed  Article  Google Scholar 

83.
Pickup ZL, Pickup R, Parry JD. Growth of Acanthamoeba castellanii and Hartmannella vermiformis on live, heat-killed and DTAF-stained bacterial prey. FEMS Microbiol Ecol. 2007;61:264–72.
CAS  PubMed  Article  Google Scholar 

84.
Legrand C, Johansson N, Johnsen G, Borsheim K, Graneli E. Phagotrophy and toxicity variation in mixotrophic Prymnesium patelliferum (Haptophyceae). Limnol Oceanogr. 2001;46:1208–14.
Article  Google Scholar 

85.
Caron DA, Sanders RW, Lim EL, Marrasé C, Amaral LA, Whitney S, et al. Light-depend phagotrophy freshwater mixotrophic chrysophyte Dinobryon cylindricum. Micro. Ecol. 1993;25:93–111.
CAS  Article  Google Scholar 

86.
Fenchel T. The microbial loop – 25 years later. J Exp Mar Bio Ecol. 2008;366:99–103.
Article  Google Scholar 

87.
Tittel J, Bissinger V, Zippel B, Gaedke U, Bell E, Lorke A, et al. Mixotrophs combine resource use to outcompete specialists: Implications for aquatic food webs. Proc Natl Acad Sci. 2003;100:12776–81.
CAS  PubMed  Article  Google Scholar 

88.
Moorthi S, Ptacnik R, Sanders R, Fischer R, Busch M, Hillebrand H. The functional role of planktonic mixotrophs in altering seston stoichiometry. Aquat Micro Ecol. 2017;79:235–45.
Article  Google Scholar 

89.
Katechakis A, Haseneder T, Kling R, Stibor H. Mixotrophic versus photoautotrophic specialist algae as food for zooplankton: The light: nutrient hypothesis might not hold for mixotrophs. Limnol Oceanogr. 2005;50:1290–9.
CAS  Article  Google Scholar 

90.
Weisse T, Anderson R, Arndt H, Calbet A, Hansen PJ, Montagnes D. Functional ecology of aquatic phagotrophic protists – concepts, limitations, and perspectives. Eur J Protistol. 2016;55:50–74.
PubMed  Article  Google Scholar 

91.
Graham LE, Graham JM, Wilcox WL, Cook ME. Algae. 3rd ed. Madison, WI, USA: LJLM Press; 2016.

92.
Guillou L, Eikrem W, Chrétiennot-Dinet M-J, Le Gall F, Massana R, Romari K, et al. Diversity of picoplanktonic prasinophytes assessed by direct nuclear SSU rDNA sequencing of environmental samples and novel isolates retrieved from oceanic and coastal marine ecosystems. Protist. 2004;155:193–214.
CAS  PubMed  Article  Google Scholar 

93.
Lemieux C, Otis C, Turmel M. Six newly sequenced chloroplast genomes from prasinophyte green algae provide insights into the relationships among prasinophyte lineages and the diversity of streamlined genome architecture in picoplanktonic species. BMC Genom. 2014;15:857.
Article  CAS  Google Scholar  More