Ecology

1.
Ineson, P., Leonard, M. A. & Anderson, J. M. Effect of collembolan grazing upon nitrogen and cation leaching from decomposing leaf litter. Soil Biol. Biochem. 14, 601–605. https://doi.org/10.1016/0038-0717(82)90094-3 (1982).
Article  Google Scholar 
2.
Petersen, H. & Luxton, M. A. comparative analysis of soil fauna populations and their role in decomposition processes. Oikos 39, 288–388. https://doi.org/10.1016/j.pedobi.2006.08.006 (1982).
Article  Google Scholar 

3.
Drake, H. L. & Horn, M. A. As the worm turns: The earthworm gut as a transient habitat for soil microbial biomes. Annu. Rev. Microbiol. 61, 169–189. https://doi.org/10.1146/annurev.micro.61.080706.093139 (2007).
CAS  Article  PubMed  Google Scholar 

4.
Liu, Y. et al. Higher soil fauna abundance accelerates litter carbon release across an alpine forest-tundra ecotone. Sci. Rep. 9, 10562. https://doi.org/10.1038/s41598-019-47072-0 (2019).
CAS  Article  Google Scholar 

5.
Hopkin, S. P. Biology of the Springtails (Insecta: Collembola) (Oxford University Press, Oxford, 1997).
Google Scholar 

6.
Maaß, S., Caruso, T. & Rillig, M. C. Functional role of microarthropods in soil aggregation. Pedobiologia 58, 59–63. https://doi.org/10.1016/j.pedobi.2015.03.001 (2015).
Article  Google Scholar 

7.
Bergstrom, D. M., Convey, P. & Huiskes, A. H. L. Trends in Antarctic Terrestrial and Limnetic Ecosystems: Antarctica as a Global Indicator (Springer, Berlin, 2006). .
Google Scholar 

8.
Convey, P. Antarctic terrestrial biodiversity in a changing world. Polar. Biol. 34(11), 1629–1641. https://doi.org/10.1007/s00300-011-1068-0 (2011).
Article  Google Scholar 

9.
Convey, P. et al. The spatial structure of Antarctic biodiversity. Ecol. Monogr. 84(2), 203–244. https://doi.org/10.1890/12-2216.1 (2014).
Article  Google Scholar 

10.
Wauchope, H. S., Shaw, J. D. & Terauds, A. A snapshot of biodiversity protection in Antarctica. Nat. Commun. 10(1), 946. https://doi.org/10.1038/s41467-019-08915-6 (2019).
ADS  CAS  Article  PubMed  PubMed Central  Google Scholar 

11.
Chown, S. L. et al. The changing form of Antarctic biodiversity. Nature 522(7557), 431–438. https://doi.org/10.1038/nature14505 (2015).
ADS  CAS  Article  PubMed  Google Scholar 

12.
Agamennone, V. et al. The microbiome of Folsomia candida: An assessment of bacterial diversity in a Wolbachia-containing animal. FEMS Microbiol. Ecol. 91(11), 1–10. https://doi.org/10.1093/femsec/fiv128 (2015).
CAS  Article  Google Scholar 

13.
Zhu, D. et al. Exposure of soil collembolans to microplastics perturbs their gut microbiota and alters their isotopic composition. Soil Biol. Biochem. 115, 302–310. https://doi.org/10.1016/j.soilbio.2017.10.027 (2018).
CAS  Article  Google Scholar 

14.
Bahrndorff, S. et al. Diversity and metabolic potential of the microbiota associated with a soil arthropod. Sci. Rep. 8(1), 1–8. https://doi.org/10.1038/s41598-018-20967-0 (2018).
CAS  Article  Google Scholar 

15.
Ding, J. et al. Effects of long-term fertilization on the associated microbiota of soil collembolan. Soil Biol. Biochem. 130, 141–149. https://doi.org/10.1016/j.soilbio.2018.12.015 (2019).
CAS  Article  Google Scholar 

16.
Anslan, S., Bahram, M. & Tedersoo, L. Temporal changes in fungal communities associated with guts and appendages of Collembola as based on culturing and high-throughput sequencing. Soil Biol. Biochem. 96, 152–159. https://doi.org/10.1016/j.soilbio.2016.02.006 (2016).
CAS  Article  Google Scholar 

17.
Terauds, A. et al. Conservation biogeography of the Antarctic. Divers Distrib. 18(7), 726–741. https://doi.org/10.1111/j.1472-4642.2012.00925.x (2012).
Article  Google Scholar 

18.
Terauds, A. & Lee, J. R. Antarctic biogeography revisited: Updating the Antarctic Conservation Biogeographic Regions. Divers Distrib. 22(8), 836–840. https://doi.org/10.1111/ddi.12453 (2016).
Article  Google Scholar 

19.
Greenslade, P. An Antarctic biogeographical anomaly resolved: The true identity of a widespread species of Collembola. Polar Biol. 41(5), 969–981. https://doi.org/10.1007/s00300-018-2261-1 (2018).
Article  Google Scholar 

20.
Carapelli, A. et al. Evidence for cryptic diversity in the “pan-Antarctic” springtail Friesea antarctica and the description of two new species. Insects 11, 141. https://doi.org/10.3390/insects11030141 (2020).
Article  PubMed Central  Google Scholar 

21.
Carapelli, A., Convey, P., Frati, F., Spinsanti, G. & Fanciulli, P. P. Population genetics of three sympatric springtail species (Hexapoda: Collembola) from the South Shetland Islands: Evidence for a common biogeographic pattern. Biol. J. Linn. Soc. 120, 788–803. https://doi.org/10.1093/biolinnean/blw004 (2017).
Article  Google Scholar 

22.
Collins, G. E., Hogg, I. D., Convey, P., Barnes, A. D. & McDonald, I. R. Spatial and temporal scales matter when assessing the species and genetic diversity of springtails (Collembola) in Antarctica. Front. Ecol. Evol. 7, 76. https://doi.org/10.3389/fevo.2019.00076 (2019).
Article  Google Scholar 

23.
Collins, G. E. et al. Genetic diversity of soil invertebrates corroborates timing estimates for past collapses of the West Antarctic Ice Sheet. PNAS 117, 22293–22302. https://doi.org/10.1073/pnas.2007925117 (2020).
ADS  CAS  Article  PubMed  Google Scholar 

24.
Holmes, C. J. et al. The Antarctic mite, Alaskozetes antarcticus, shares bacterial microbiome community membership but not abundance between adults and tritonymphs. Polar Biol. 42, 2075–2085. https://doi.org/10.1007/s00300-019-02582-5 (2019).
Article  Google Scholar 

25.
Vecchi, M., Newton, I. L. G., Cesari, M., Rebecchi, L. & Guidetti, R. The microbial community of tardigrades: Environmental influence and species specificity of microbiome structure and composition. Microb. Ecol. 76(2), 467–481. https://doi.org/10.1007/s00248-017-1134-4 (2018).
CAS  Article  PubMed  Google Scholar 

26.
Delgado-Baquerizo, M. et al. Ecological drivers of soil microbial diversity and soil biological networks in the Southern Hemisphere. Ecology 99(3), 583–596. https://doi.org/10.1002/ecy.2137 (2018).
Article  PubMed  Google Scholar 

27.
Chu, H. et al. Soil bacterial diversity in the Arctic is not fundamentally different from that found in other biomes. Environ. Microbiol. 12(11), 2998–3006. https://doi.org/10.1111/j.1462-2920.2010.02277.x (2010).
CAS  Article  PubMed  Google Scholar 

28.
Siciliano, S. D. et al. Soil fertility is associated with fungal and bacterial richness, whereas pH is associated with community composition in polar soil microbial communities. Soil Biol. Biochem. 78, 10–20. https://doi.org/10.1016/j.soilbio.2014.07.005 (2014).
CAS  Article  Google Scholar 

29.
Zouache, K. et al. Composition of bacterial communities associated with natural and laboratory populations of Asobara tabida infected with Wolbachia. Appl. Environ. Microb. 75, 3755–3764. https://doi.org/10.1128/aem.02964-08 (2009).
CAS  Article  Google Scholar 

30.
Potapov, A. A., Semenina, E. E., Korotkevich, A. Y., Kuznetsova, N. A. & Tiunov, A. V. Connecting taxonomy and ecology: Trophic niches of collembolans as related to taxonomic identity and life forms. Soil Biol. Biochem. 101, 20–31. https://doi.org/10.1016/j.soilbio.2016.07.002 (2016).
CAS  Article  Google Scholar 

31.
De Wever, A. et al. Hidden levels of phylodiversity in Antarctic green algae: Further evidence for the existence of glacial refugia. Proc. R. Soc. B 276, 3591–3599. https://doi.org/10.1098/rspb.2009.0994 (2009).
Article  PubMed  Google Scholar 

32.
Vyverman, W. et al. Evidence for widespread endemism among Antarctic micro-organisms. Polar Sci. 4(2), 103–113. https://doi.org/10.1016/j.polar.2010.03.006 (2010).
ADS  Article  Google Scholar 

33.
Finlay, B. J. & Clarke, K. J. Ubiquitous dispersal of microbial species. Nature 400, 828–828. https://doi.org/10.1038/23616 (1999).
ADS  CAS  Article  Google Scholar 

34.
Chown, S. L. & Convey, P. Structure and temporal variability across life’s hierarchies in the terrestrial Antarctic. Philos. Trans. R. Soc. B 362, 2307–23331. https://doi.org/10.1098/rstb.2006.1949 (2007).
Article  Google Scholar 

35.
Convey, P., Biersma, E. M., Casanova-Katny, A. & Maturana, C. S. Refuges of Antarctic diversity. Chapter 10. In Past Antarctica (eds Oliva, M. & Ruiz-Fernández, J.) 181–200 (Academic Press, Burlington, 2020). https://doi.org/10.1016/B978-0-12-817925-3.00010-0.
Google Scholar 

36.
Bolger, A. M., Lohse, M. & Usadel, B. Trimmomatic: A flexible trimmer for Illumina sequence data. Bioinformatics 30(15), 2114–2120. https://doi.org/10.1093/bioinformatics/btu170 (2014).
CAS  Article  PubMed  PubMed Central  Google Scholar 

37.
Bolyen, E. et al. Reproducible, interactive, scalable and extensible microbiome data science using QIIME 2. Nat. Biotechnol. 37, 852–857. https://doi.org/10.1038/s41587-019-0209-9 (2019).
CAS  Article  PubMed  PubMed Central  Google Scholar 

38.
Callahan, B. J. et al. DADA2: High-resolution sample inference from Illumina amplicon data. Nat. Methods 13(7), 581–583. https://doi.org/10.1038/nmeth.3869 (2016).
CAS  Article  PubMed  PubMed Central  Google Scholar 

39.
Rognes, T., Flouri, T., Nichols, B., Quince, C. & Mahé, F. VSEARCH: A versatile open source tool for metagenomics. PeerJ 4, e2584. https://doi.org/10.7717/peerj.2584 (2016).
Article  PubMed  PubMed Central  Google Scholar 

40.
Bokulich, N. A. et al. Optimizing taxonomic classification of marker-gene amplicon sequences with qiime 2’s q2-feature-classifier plugin. Microbiome 6(1), 90. https://doi.org/10.1186/s40168-018-0470-z (2018).
MathSciNet  Article  PubMed  PubMed Central  Google Scholar 

41.
Price, M. N., Dehal, P. S. & Arkin, A. P. Fasttree 2-approximately maximum-likelihood trees for large alignments. PLoS One 5(3), e9490. https://doi.org/10.1371/journal.pone.0009490 (2010).
ADS  CAS  Article  PubMed  PubMed Central  Google Scholar 

42.
Lahti, L. & Shetty, S. Microbiome R package. http://microbiome.github.io (2012–2019).

43.
Ssekagiri, A., Sloan, W. T. & Ijaz, U. Z. microbiomeSeq: An R package for analysis of microbial communities in an environmental context. ISCB Africa ASBCB Conference. http://www.github.com/umerijaz/microbiomeSeq (2017).

44.
McMurdie, P. J. & Holmes, S. phyloseq: An R package for reproducible interactive analysis and graphics of microbiome census data. PLoS One 8(4), e61217. https://doi.org/10.1371/journal.pone.0061217 (2014).
ADS  CAS  Article  Google Scholar 

45.
Oksanen, J., et al. Vegan: Community ecology package. R package version 2.5-6. https://github.com/vegandevs/vegan (2019).

46.
Ritchie, M. E. et al. limma powers differential expression analyses for RNA-sequencing and microarray studies. Nucleic Acids Res. 43(7), e47. https://doi.org/10.1093/nar/gkv007 (2015).
CAS  Article  PubMed  PubMed Central  Google Scholar 

47.
Chen, H. & Boutros, P. C. VennDiagram: A package for the generation of highly-customizable Venn and Euler diagrams in R. BMC Bioinf. 12, 35. https://doi.org/10.1186/1471-2105-12-35 (2011).
Article  Google Scholar 

48.
Wickham, H. ggplot2: Elegant Graphics for Data Analysis. Springer, New York. https://ggplot2.tidyverse.org (2016).

49.
Warnes, G. R., et al. gplots: Various R Programming Tools for Plotting Data. R package version 3.0.1.1. https://CRAN.R-project.org/package=gplots (2019). More

1.
Arrigo, K. R. & Dijken, G. L. Secular trends in Arctic Ocean net primary production. J. Geophys. Res. Oceans. 116, C09011 (2011).
ADS  Google Scholar 
2.
Thomas, D. N. Sea Ice Ch 4 (Wiley Blackwell, Oxford, 2017).
Google Scholar 

3.
Arrigo, K. R. et al. Massive phytoplankton blooms under Arctic sea ice. Science 336, 1408–1408 (2012).
ADS  CAS  Article  Google Scholar 

4.
Assmy, P. et al. Leads in Arctic pack ice enable early phytoplankton blooms below snow-covered sea ice. Sci. Rep. 7, 40850 (2016).
ADS  Article  Google Scholar 

5.
Horvat, C. et al. The frequency and extent of sub-ice phytoplankton bloom in the Arctic Ocean. Sci. Adv. 3, e1601191 (2017).
ADS  Article  Google Scholar 

6.
Ardyna, M. et al. Environmental drivers of under-ice phytoplankton bloom dynamics in the Arctic Ocean. Elem. Sci. Anth. 8, 30 (2020).
Article  Google Scholar 

7.
Ardyna, M. et al. Under-ice phytoplankton blooms: Shedding light on the “invisible” part of Arctic primary production. Front. Mar. Sci. 7, 608032 (2020).
Article  Google Scholar 

8.
Rysgaard, S. & Glud, R. N. Carbon cycling in Arctic marine ecosystems: Case study Young Sound (ed. Rysgaard, S. & Glud, R. N.) 62–94 (Meddelelser om Grønland, Bioscience Vol 58, Copenhagen, Denmark, the Commission for Scientific Research in Greenland, 2007).

9.
Meire, L. et al. Marine-terminating glaciers sustain high productivity in Greenland fjords. Glob. Chang. Biol. 23, 5344–5357 (2017).
ADS  Article  Google Scholar 

10.
Randelhoff, A. et al. Pan-Arctic Ocean primary production constrained by turbulent nitrate fluxes. Front. Mar. Sci. 7, 150 (2020).
Article  Google Scholar 

11.
Holding, J. M. et al. Seasonal and spatial patterns of primary production in a high-latitude fjord affected by Greenland Ice Sheet run-off. Biogeosciences 16, 3777–3792 (2019).
ADS  CAS  Article  Google Scholar 

12.
Juul-Pedersen, T. et al. Seasonal and interannual phytoplankton production in a sub-Arctic tidewater outlet glacier fjord, SW Greenland. Mar. Ecol. Prog. Ser. 524, 27–38 (2015).
ADS  Article  Google Scholar 

13.
Sejr, M. K. et al. Evidence of local and regional freshening of Northeast Greenland coastal waters. Sci. Rep. 7, 13183 (2017).
ADS  Article  Google Scholar 

14.
Boone, W. et al. Circulation and fjord-shelf exchange during the ice-covered period in Young Sound-Tyrolerfjord, Northeast Greenland (74°N). Estuar. Coast. Shelf Sci. 194, 205–216 (2017).
ADS  Article  Google Scholar 

15.
Haine, T. W. N. et al. Arctic freshwater export: Status, mechanisms, and prospects. Glob. Planet Change 125, 13–35 (2015).
ADS  Article  Google Scholar 

16.
Carmack, E. C. et al. Freshwater and its role in the Arctic Marine System: Sources, disposition, storage export, and physical and biogeochemical consequences in the Arctic and global ocean. J. Geophys. Res. Biogeosci. 121, 675–717 (2015).
Article  Google Scholar 

17.
Lund-Hansen, L. C. et al. Will low primary production rates in the Amundsen Basin (Arctic Ocean) remain low in a future ice-free setting, and what governs this production?. J. Mar. Syst. 205, 103287 (2020).
Article  Google Scholar 

18.
Dahl, E., Bagøien, E., Edvardsen, B. & Stenseth, N. C. The dynamics of Chrysochromulina species in the Skagerrak in relation to environmental conditions. J. Sea. Res. 54, 15–24 (2005).
ADS  Article  Google Scholar 

19.
Hansen, P. J., Nielsen, T. G. & Kaas, H. Distribution and growth of protists and mesozooplankton during a bloom of Chrysochromulina spp. (Prymnesiophyceae, Prymnesiales). Phycologia 34, 409–416 (1995).
Article  Google Scholar 

20.
Nielsen, T. G., Kiørboe, T. & Bjørnsen, P. K. Effects of a Chrysochromulina polylepis subsurface bloom on the planktonic community. Mar. Ecol. Prog. Ser. 62, 21–35 (1990).
ADS  Article  Google Scholar 

21.
Hällfors, G. & Niemi, Å. A Chrysochromulina (Haptophyceae) bloom under the ice in the Tvärminne Archipelago, southern coast of Finland. Acta Soc. Fauna Flora Fenn. 50, 89–104 (1974).
Google Scholar 

22.
Manton, I. Chrysochromulina tenuispine sp. nov. from arctic Canada. Br. Phycol. J. 13, 227–234 (1978).
Article  Google Scholar 

23.
Green, J. C. & Leadbeater, B. S. C. The Haptophyte Algae ch. 13 (Systematics Association, London, 1994).
Google Scholar 

24.
Hansen, P. J. & Hjorth, M. Growth and grazing responses of Chrysochromulina ericina (Prymnesiophyceae): The role of irradiance, prey concentration and pH. Mar. Biol. 141, 975–983 (2002).
CAS  Article  Google Scholar 

25.
Anderson, R., Charvet, S. & Hansen, P. J. Mixotrophy in chlorophytes and haptophytes—Effect of irradiance, macronutrient micronutrient and vitamin limitation. Front. Microbiol. 9, 1704 (2018).
Article  Google Scholar 

26.
Anderson, R. & Hansen, P. J. Meteorological conditions induce strong shifts in mixotrophic and heterotrophic flagellate bacterivory over small spatio-temporal scales. Limnol. Oceanogr. 9999, 1–11 (2019).
Google Scholar 

27.
McKie-Krisberg, Z. M., Gast, R. J. & Sanders, R. W. Physiological responses of three species of Antarctic mixotrophic phytoflagellates to changes in light and dissolved nutrients. Microbiol. Ecol 70, 21–29 (2015).
CAS  Article  Google Scholar 

28.
McKie-Krisberg, Z. M., Sanders, R. W. & Gast, R. J. Evaluation of mixotrophy-associated gene expression in two species of polar marine algae. Front. Mar. Sci. 5, 273 (2018).
Article  Google Scholar 

29.
Rysgaard, S., Nielsen, T. G. & Hansen, B. W. Seasonal variation in nutrients, pelagic primary production and grazing in a high-Arctic coastal marine ecosystem, Young Sound, Northeast Greenland. Mar. Ecol. Prog. Ser. 179, 13–25 (1999).
ADS  CAS  Article  Google Scholar 

30.
Bendtsen, J., Mortensen, J. & Rysgaard, S. Seasonal surface layer dynamics and sensitivity to runoff in a high Arctic fjord (Young Sound/Tyrolerfjord, 74°N). J. Geophys. Res. Oceans. 119, 1–18 (2014).
Article  Google Scholar 

31.
Krawczyk, D. W. et al. Spatial and temporal distribution of planktonic protists in the East Greenland fjord and offshore waters. Mar. Ecol. Prog. Ser. 538, 99–116 (2015).
ADS  CAS  Article  Google Scholar 

32.
Søgaard, D. H., Deming, J. W., Meire, L. & Rysgaard, S. Effects of microbial processes and CaCO3 dynamics on inorganic carbon cycling in snow-covered Arctic winter sea ice. Mar. Ecol. Prog. Ser. 611, 31–44 (2019).
ADS  Article  Google Scholar 

33.
Rysgaard, S. et al. Ikaite crystal distribution in winter sea ice and implications for CO2 system dynamics. Cryosphere 7, 707–718 (2013).
ADS  Article  Google Scholar 

34.
Søgaard, D. H. et al. Autotrophic and heterotrophic activity in Arctic first-year sea ice: Seasonal study from Malene Bight, SW Greenland. Mar. Ecol. Prog. Ser. 419, 31–45 (2010).
ADS  Article  Google Scholar 

35.
Grasshoff, K., Kremling, K. & Ehrhardt, M. Methods of Seawater Analysis (WILEY-VCH Verlag GmbH, Weinheim, 1999).
Google Scholar 

36.
Steemann-Nielsen, E. The use of radio-active carbon (C14) for measuring organic production in the sea. ICES J. Mar. Sci. 18, 117–140 (1952).
Article  Google Scholar 

37.
Søgaard, D. H. et al. The relative contributions of biological and abiotic processes to carbon dynamics in subarctic sea ice. Polar Biol. 36, 1761–1777 (2013).
Article  Google Scholar 

38.
Platt, T., Gallegos, C. L. & Harrison, W. G. Photoinhibition of photosynthesis in natural assemblages of marine phytoplankton. J. Mar. Res. 38, 687–701 (1980).
Google Scholar 

39.
Jespersen, A. M. & Christoffersen, K. Measurements of chlorophyll-a from phytoplankton using ethanol as extraction solvent. Arch. Hydrobiol. 109, 445–454 (1987).
CAS  Google Scholar 

40.
Ralph, P. J. & Gademann, R. Rapid light curves: A powerful tool to assess photosynthetic activity. Aquat. Bot. 82, 222–237 (2005).
CAS  Article  Google Scholar 

41.
Jassby, A. D. & Platt, T. Mathematical formulation of the relationship between photosynthesis and light for phytoplankton. Limnol. Oceanogr. 21, 540–547 (1976).
ADS  CAS  Article  Google Scholar  More

1.
Vitousek, P. M. et al. The Nitrogen Cycle at Regional to Global Scales 1–45 (Springer, New York, 2002).
Google Scholar 
2.
Stein, L. Y. & Klotz, M. G. The nitrogen cycle. Curr. Biol. CB 26, R94–R98 (2016).
CAS  PubMed  Article  Google Scholar 

3.
Kuypers, M. M. M., Marchant, H. K. & Kartal, B. The microbial nitrogen-cycling network. Nat. Rev. Microbiol. 16, 263–276 (2018).
CAS  PubMed  Article  Google Scholar 

4.
Winogradsky, S. On the nitrifying organisms. Sciences 110, 1013–1016 (1890).
Google Scholar 

5.
Könneke, M. et al. Isolation of an autotrophic ammonia-oxidizing marine archaeon. Nature 437, 543–546 (2005).
ADS  PubMed  Article  CAS  Google Scholar 

6.
Hatzenpichler, R. Diversity, physiology, and niche differentiation of ammonia-oxidizing archaea. Appl. Environ. Microbiol. 78, 7501–7510 (2012).
CAS  PubMed  PubMed Central  Article  Google Scholar 

7.
Daims, H. et al. Complete nitrification by Nitrospira bacteria. Nature 528, 504–509 (2015).
ADS  CAS  PubMed  PubMed Central  Article  Google Scholar 

8.
van Kessel, M. A. H. J. et al. Complete nitrification by a single microorganism. Nature 528, 555–559 (2015).
ADS  PubMed  PubMed Central  Article  CAS  Google Scholar 

9.
Pester, M. et al. NxrB encoding the beta subunit of nitrite oxidoreductase as functional and phylogenetic marker for nitrite-oxidizing Nitrospira. Environ. Microbiol. 16, 3055–3071 (2014).
CAS  PubMed  Article  Google Scholar 

10.
Gruber-Dorninger, C. et al. Functionally relevant diversity of closely related Nitrospira in activated sludge. ISME J. 9, 643–655 (2015).
CAS  PubMed  Article  Google Scholar 

11.
Pjevac, P. et al. AmoA-targeted polymerase chain reaction primers for the specific detection and quantification of comammox Nitrospira in the environment. Front. Microbiol. 8, 1508 (2017).
PubMed  PubMed Central  Article  Google Scholar 

12.
Bartelme, R. P., McLellan, S. L. & Newton, R. J. Freshwater recirculating aquaculture system operations drive biofilter bacterial community shifts around a stable nitrifying consortium of ammonia-oxidizing archaea and Comammox Nitrospira. Front. Microbiol. 8, 101 (2017).
PubMed  PubMed Central  Article  Google Scholar 

13.
Wang, Y. et al. Comammox in drinking water systems. Water Res. 116, 332–341 (2017).
CAS  PubMed  Article  Google Scholar 

14.
Pinto, A. J. et al. Metagenomic evidence for the presence of Comammox Nitrospira-like bacteria in a drinking water system. mSphere 1 (2016).

15.
Fowler, S. J., Palomo, A., Dechesne, A., Mines, P. D. & Smets, B. F. Comammox Nitrospira are abundant ammonia oxidizers in diverse groundwater-fed rapid sand filter communities. Environ. Microbiol. 20, 1002–1015 (2018).
CAS  PubMed  Article  Google Scholar 

16.
Beach, N. K. & Noguera, D. R. Design and assessment of species-level qPCR primers targeting Comammox. Front. Microbiol. 10, 36 (2019).
PubMed  PubMed Central  Article  Google Scholar 

17.
Hu, H.-W. & He, J.-Z. Comammox—a newly discovered nitrification process in the terrestrial nitrogen cycle. J. Soils Sediments 17, 2709–2717 (2017).
CAS  Article  Google Scholar 

18.
Xia, F. et al. Ubiquity and diversity of complete ammonia oxidizers (Comammox). Appl. Environ. Microbiol. 84, e01390-18 (2018).

19.
Jiang, Q., Xia, F., Zhu, T., Wang, D. & Quan, Z. Distribution of comammox and canonical ammonia-oxidizing bacteria in tidal flat sediments of the Yangtze River estuary at different depths over four seasons. J. Appl. Microbiol. 127, 533–543 (2019).
CAS  PubMed  Article  Google Scholar 

20.
Liu, S. et al. Comammox Nitrospira within the Yangtze River continuum: Community, biogeography, and ecological drivers. ISME J. 14, 2488–2504 (2020).
CAS  PubMed  PubMed Central  Article  Google Scholar 

21.
Xu, Y. et al. Diversity and abundance of comammox bacteria in the sediments of an urban lake. J. Appl. Microbiol. 128, 1647–1657 (2020).
CAS  PubMed  Article  Google Scholar 

22.
Lu, S., Sun, Y., Lu, B., Zheng, D. & Xu, S. Change of abundance and correlation of Nitrospira inopinata-like comammox and populations in nitrogen cycle during different seasons. Chemosphere 241, 125098 (2020).
ADS  CAS  PubMed  Article  Google Scholar 

23.
Boehrer, B. & Schultze, M. Stratification of lakes. Rev. Geophys. 46, RG2005 (2008).

24.
Hou, J., Song, C., Cao, X. & Zhou, Y. Shifts between ammonia-oxidizing bacteria and archaea in relation to nitrification potential across trophic gradients in two large Chinese lakes (Lake Taihu and Lake Chaohu). Water Res. 47, 2285–2296 (2013).
CAS  PubMed  Article  Google Scholar 

25.
Alfreider, A. et al. CO2 assimilation strategies in stratified lakes: Diversity and distribution patterns of chemolithoautotrophs. Environ. Microbiol. 19, 2754–2768 (2017).
CAS  PubMed  PubMed Central  Article  Google Scholar 

26.
Alfreider, A. et al. Autotrophic carbon fixation strategies used by nitrifying prokaryotes in freshwater lakes. FEMS Microbiol. Ecol. 94, fiy163 (2018).

27.
Herber, J. et al. A single Thaumarchaeon drives nitrification in deep oligotrophic Lake Constance. Environ. Microbiol. 22, 212–228 (2020).
CAS  PubMed  Article  Google Scholar 

28.
Rotthauwe, J.-H., Witzel, K.-P. & Liesack, W. The ammonia monooxygenase structural gene amoA as a functional marker: Molecular fine-scale analysis of natural ammonia-oxidizing populations. Appl. Environ. Microbiol. 63, 4704–4712 (1997).
CAS  PubMed  PubMed Central  Article  Google Scholar 

29.
Junier, P. et al. Phylogenetic and functional marker genes to study ammonia-oxidizing microorganisms (AOM) in the environment. Appl. Microbiol. Biotechnol. 85, 425–440 (2010).
CAS  PubMed  Article  Google Scholar 

30.
Kowalchuk, G. A. & Stephen, J. R. Ammonia-oxidizing bacteria: A model for molecular microbial ecology. Annu. Rev. Microbiol. 55, 485–529 (2001).
CAS  PubMed  Article  Google Scholar 

31.
Alves, R. J. E., Minh, B. Q., Urich, T., von Haeseler, A. & Schleper, C. Unifying the global phylogeny and environmental distribution of ammonia-oxidising archaea based on amoA genes. Nat. Commun. 9, 1517 (2018).
ADS  PubMed  PubMed Central  Article  CAS  Google Scholar 

32.
Linhart, C. & Shamir, R. The degenerate primer design problem: Theory and applications. J. Comput Biol. 12, 431–456 (2005).

33.
Alfreider, A. & Tartarotti, B. Spatiotemporal dynamics of different CO2 fixation strategies used by prokaryotes in a dimictic lake. Sci. Rep. 9, 15068 (2019).
ADS  PubMed  PubMed Central  Article  CAS  Google Scholar 

34.
Luesken, F. A. et al. Diversity and enrichment of nitrite-dependent anaerobic methane oxidizing bacteria from wastewater sludge. Appl. Microbiol. Biotechnol. 92, 845–854 (2011).
CAS  PubMed  PubMed Central  Article  Google Scholar 

35.
Wu, D. Y., Ugozzoli, L., Pal, B. K., Qian, J. I. N. & Wallace, R. B. The effect of temperature and oligonucleotide primer length on the specificity and efficiency of amplification by the polymerase chain reaction. DNA Cell Biol. 10, 233–238 (1991).
CAS  PubMed  Article  Google Scholar 

36.
Kits, K. D. et al. Kinetic analysis of a complete nitrifier reveals an oligotrophic lifestyle. Nature 549, 269–272 (2017).
ADS  CAS  PubMed  PubMed Central  Article  Google Scholar 

37.
Daims, H., Lücker, S. & Wagner, M. A new perspective on microbes formerly known as nitrite-oxidizing bacteria. Trends Microbiol. 24, 699–712 (2016).
CAS  PubMed  PubMed Central  Article  Google Scholar 

38.
Berg, I. A. Ecological aspects of the distribution of different autotrophic CO2 fixation pathways. Appl. Environ. Microbiol. 77, 1925–1936 (2011).
CAS  PubMed  PubMed Central  Article  Google Scholar 

39.
Callieri, C., Hernández-Avilés, S., Salcher, M. M., Fontaneto, D. & Bertoni, R. Distribution patterns and environmental correlates of Thaumarchaeota abundance in six deep subalpine lakes. Aquat. Sci. 78, 215–225 (2016).
CAS  Article  Google Scholar 

40.
Coci, M., Odermatt, N., Salcher, M. M., Pernthaler, J. & Corno, G. Ecology and distribution of Thaumarchaea in the deep hypolimnion of Lake Maggiore. Archaea 2015, 1–11 (2015).
Article  Google Scholar 

41.
Auguet, J.-C., Triadó-Margarit, X., Nomokonova, N., Camarero, L. & Casamayor, E. O. Vertical segregation and phylogenetic characterization of ammonia-oxidizing archaea in a deep oligotrophic lake. ISME J. 6, 1786–1797 (2012).
CAS  PubMed  PubMed Central  Article  Google Scholar 

42.
Vissers, E. W. et al. Seasonal and vertical distribution of putative ammonia-oxidizing thaumarchaeotal communities in an oligotrophic lake. FEMS Microbiol. Ecol. 83, 515–526 (2013).
CAS  PubMed  Article  Google Scholar 

43.
Vissers, E. W. Spatial and Temporal Dynamics of Thaumarchaeota in Deep European Lakes (Netherlands Institute of Ecology, 2012).

44.
Small, G. E. et al. Rates and controls of nitrification in a large oligotrophic lake. Limnol. Oceanogr. 58, 276–286 (2013).
ADS  CAS  Article  Google Scholar 

45.
Lavrentyev, P. J., Gardner, W. S. & Johnson, J. R. Cascading trophic effects on aquatic nitrification: Experimental evidence and potential implications. Aquat. Microb. Ecol. 13, 161–175 (1997).
Article  Google Scholar 

46.
Costa, E., Pérez, J. & Kreft, J.-U. Why is metabolic labour divided in nitrification?. Trends Microbiol. 14, 213–219 (2006).
CAS  PubMed  Article  Google Scholar 

47.
Koch, H., van Kessel, M. A. H. J. & Lücker, S. Complete nitrification: Insights into the ecophysiology of comammox Nitrospira. Appl. Microbiol. Biotechnol. 103, 177–189 (2019).
CAS  PubMed  Article  Google Scholar 

48.
Schramm, A., de Beer, D., Gieseke, A. & Amann, R. Microenvironments and distribution of nitrifying bacteria in a membrane-bound biofilm. Environ. Microbiol. 2, 680–686 (2000).
CAS  PubMed  Article  Google Scholar 

49.
Nowka, B., Off, S., Daims, H. & Spieck, E. Improved isolation strategies allowed the phenotypic differentiation of two Nitrospira strains from widespread phylogenetic lineages. FEMS Microbiol. Ecol. 91, fiu031 (2015).

50.
Ushiki, N., Fujitani, H., Aoi, Y. & Tsuneda, S. Isolation of Nitrospira belonging to sublineage II from a wastewater treatment plant. Microbes Environ. ME13042 (2013).

51.
Cotto, I. et al. Long solids retention times and attached growth phase favor prevalence of comammox bacteria in nitrogen removal systems. Water Res. 169, 115268 (2020).
CAS  PubMed  Article  Google Scholar 

52.
Koch, H. et al. Expanded metabolic versatility of ubiquitous nitrite-oxidizing bacteria from the genus Nitrospira. Proc. Natl. Acad. Sci. U.S.A. 112, 11371–11376 (2015).
ADS  CAS  PubMed  PubMed Central  Article  Google Scholar 

53.
Kalvelage, T. et al. Nitrogen cycling driven by organic matter export in the South Pacific oxygen minimum zone. Nat. Geosci. 6, 228–234 (2013).
ADS  CAS  Article  Google Scholar 

54.
Bristow, L. A. et al. Ammonium and nitrite oxidation at nanomolar oxygen concentrations in oxygen minimum zone waters. Proc. Natl. Acad. Sci. U.S.A. 113, 10601–10606 (2016).
ADS  CAS  PubMed  PubMed Central  Article  Google Scholar 

55.
Madeira, F. et al. The EMBL-EBI search and sequence analysis tools APIs in 2019. Nucleic Acids Res. 47, W636–W641 (2019).
CAS  PubMed  PubMed Central  Article  Google Scholar 

56.
Altschul, S. F., Gish, W., Miller, W., Myers, E. W. & Lipman, D. J. Basic local alignment search tool. J. Mol. Biol. 215, 403–410 (1990).
CAS  PubMed  Article  Google Scholar 

57.
Tamura, K., Stecher, G., Peterson, D., Filipski, A. & Kumar, S. MEGA6: Molecular evolutionary genetics analysis version 6.0. Mol. Biol. Evol. 30, 2725–2729 (2013).
CAS  PubMed  PubMed Central  Article  Google Scholar 

58.
Ye, J. et al. Primer-BLAST: A tool to design target-specific primers for polymerase chain reaction. BMC Bioinform. 13, 134 (2012).
CAS  Article  Google Scholar 

59.
Waterhouse, A. M., Procter, J. B., Martin, D. M. A., Clamp, M. & Barton, G. J. Jalview Version 2-a multiple sequence alignment editor and analysis workbench. Bioinformatics (Oxford, England) 25, 1189–1191 (2009).
CAS  Article  Google Scholar  More

We selected 10 permanent plots with long-term observations from CERN to include typical forest types of various ages in the East China monsoon forest region, including tropical rainforests, subtropical evergreen coniferous and broad-leaved mixed forests, warm temperate deciduous broad-leaved forests and temperate coniferous and broad-leaved forests, with evident precipitation and temperature gradients from south to north (Fig. 1). The spatial representativeness of the selected 10 sites across the Chinese forest region was evaluated by calculating the Euclidean distance based on various environmental factors. The 10 sites performed well and represented more than 80% of the Chinese forest region (Fig. S1). Of these forests, the Xishuangbanna tropical seasonal rainforest (BNF), Dinghu Mountain subtropical evergreen coniferous and broad-leaved mixed forest (DHF), Ailao Mountain subtropical evergreen broad-leaved forest (ALF), and Changbai Mountain temperate deciduous coniferous and broad-leaved mixed forest (CBF) are mature natural forests; the Shennongjia subtropical evergreen deciduous broad-leaved mixed forest (SNF) and Huitong subtropical evergreen broad-leaved forest (HTF) are natural secondary forests; and the other sites, i.e., the Beijing warm temperate deciduous broad-leaved mixed forest (BJF), Maoxian warm temperate deciduous coniferous mixed forest (MXF), Qianyanzhou subtropical evergreen artificial coniferous mixed forest (QYF), and Heshan subtropical evergreen broad-leaved forest (HSF), are plantations or middle- and young-age forests. All 10 sites are well protected and subject to minimal human activities, thus reflecting the C cycle dynamics under global environmental change, e.g., climate change, increasing CO2 and nitrogen deposition. The detailed characteristics of each plot can be found in their profiles in the CFCCD database.
There are three main steps to create the observation-based basic dataset and assimilated dataset of typical Chinese forests C cycle dynamics:
1.
Observation-based basic data acquisition. An ensemble of daily atmospheric and water data at ten CERN sites were used as forcing datasets for MDF and future scientific analysis; biological and soil data were also collected from CERN and processed by quality control and statistical calculation as benchmark to constrain the model.

2.
Implementation of a multiple data-model fusion framework. The Markov Chain Monte Carlo (MCMC) that integrated the Data Assimilation Linked Ecosystem Carbon (DALEC) model with multiple and dynamic observational data was used to retrieve C-cycle process parameters in a realistic disequilibrium state.

3.
Key process parameters and C function data assimilation. The key parameters of the process-based C cycle model (DALEC) were determined via the model-data fusion method; then the ecosystem C sequestration datasets were simulated by forward running the DALEC model with optimized parameters and then validated based on observational data and other previous studies.

Each step is explained in more detail below.
Observation-based basic data acquisition
Atmospheric and water data
In situ observations of daily air temperature (Ta), photosynthetically active radiation (PAR), relative humidity (RH), precipitation (Precip), and soil moisture (Sw) at the 10 sites from 2005 to 2015 were obtained from the CERN scientific and technological resources service system (http://www.cnern.org.cn/). These atmospheric and water data were mostly observed by an automatic meteorological station at each site. Among them, the PAR was estimated by a LI-COR LI-190SZ Quantum Sensor; Ta and RH were measured by a QMT110 sensor; Sw was estimated by a soil moisture neutron probe or the Time-Domain Reflectometry (TDR) soil moisture probe; the associated saturated soil water capacity (Sc) was measured by the cutting ring method to sample soil in each field campaign and the oven-drying method to measure saturated moisture content after the soil was soaked in water for 48 h; and Precip was artificially observed by CERN staff using an SM1-1 rain gauge. These monitoring data were collected in keeping with CERN’s protocols of observation and quality control29,30.
There were occasional missing data in time-continuous meteorological observations; therefore, the data were processed by standardized gap filling31. Specifically, for Ta, PAR, and RH, which were applied as model driver, we used a linear interpolation method to interpolate continuous missing data with less than three observations; otherwise, we established a regression model using the CERN observations and other observations from adjacent stations of the China Meteorology Administration (756 meteorological stations; http://data.cma.cn/en) to interpolate continuous missing data with more than three observations.
Biological data
Biomass.
At each site, the diameters at breast height (DBHs) and tree heights were measured for each tree in a regular inventory performed at least once every five years. The allometric equations of the DBH and/or tree heights with the biomasses of different plant tissues (i.e., leaves, branches, stems and roots) were developed at each site for various species based on the felled standard trees in the destructive plot. Then, we calculated the biomasses for the ten ecosystems using these allometric equations (FA02 table downloaded from http://www.cnern.org.cn/), which all passed the significance test (0.01 level) and have the R2 most above 0.9 when its estimation compare to observations from standard trees. For some unfelled species under protection, the allometric equations were obtained from Luo et al.32, which were developed based on national inventories and meta analyses from the published literature.
Litterfall.
The aboveground litterfall biomass was measured monthly by ten replicates with 1 m × 1 m baskets during the growing season or once during the nongrowing season. All collected litter was dried at 70 °C for 24 h in the laboratory and then weighed. To avoid the effects of wind on the measurement of litterfall biomass within an individual month, annual litterfall biomass data were finally adopted for each site.
LAI.
The leaf area index (LAI) at each site was measured optically with an LAI-2000 plant canopy analyzer (LI-COR, Lincoln, NE, USA) at least quarterly every year.
Soil data
Soil organic matter (SOM) was measured by the potassium dichromate oxidation titrimetric method. Soil bulk density (SBD) was measured by the cutting ring method in each field campaign at 10 forest sites. Soil particle size (i.e., soil mechanical composition) was measure by the laser particle analyzer. At least three samples were collected from each of the five soil layers (0–10, 10–20, 20–40, 40–60, and 60–100 cm) once every five years.
SOC.
The soil organic C (SOC) content was calculated from SOM, SBD, and volume percentage of gravel with particle size >2 mm at 10 forest sites as follows33:

$$SOC={sum }_{i=1}^{n}0.58times {H}_{i}times {B}_{i}times {O}_{i}times (1-{rm{theta }})times 100$$
(1)

where SOC is the soil organic C density (g C/m2) of all n layers, Hi is the soil thickness (cm), Bi is the soil bulk density (g/cm3), Oi is the SOM content of the ith layer (%), and θ is the volume percentage (%) of gravel with particle size >2 mm. In the absence of soil bulk density or soil organic matter content measurements in some layers, the missing soil measurements corresponding to specific soil depths of theses forest ecosystems were supplemented according to the empirical formulas of the relationships between SOM/soil bulk density and soil depth in different layers, which were developed based on the long-term and across-site CERN soil observations34.
All these raw atmospheric, biological, and soil data mentioned above can be directly download from CERN scientific and technological resources service system (http://www.cnern.org.cn/data/initDRsearch) or obtained after online application via protocol sharing.
Auxiliary flux data
Net ecosystem exchange (NEE).
These data were obtained from ChinaFLUX (http://www.chinaflux.org/), covering CBF, QYF, and BNF. The data were aggregated to the daily time step from half-hourly CO2 flux data measured by the eddy covariance technique and processed with quality control and gap filling procedures35.
Implementation of MDF method
The assimilated data were retrieved from a multiple data-model fusion method (Fig. 2). Specifically, the long-term and dynamic observations of biomass, litterfall, LAI and SOC were used as the model constraint data; Ta, PAR, and RH were used as the meteorological driving data; and the metropolis simulated annealing algorithm, a variation in the MCMC technique36,37, was applied to retrieve the C cycle parameters (e.g., C allocation and C turnover times) against the observations and prior knowledge. Then, we forward-simulated the model to produce the dynamic and time-continuous changes in ecosystem C sequestration function.
Fig. 2

Flowchart of the generation of assimilated datasets in a multiple- and long-term data assimilation framework.

Full size image

Since the dynamic C cycle observations provided an effective solution to constrain the C cycle states without the steady state assumption (SSA), the novelty of our MDF framework involves estimating these C cycle dynamics in better agreement with the actual dynamic disequilibrium state38. Therefore, the uncertainty in allocation and turnover parameters and in C pool states have largely been reduced based on the time-series observations under the non-SSA (NSSA)21,39,40, thereby significantly enhancing the model’s ability to predict the C sequestration function19,41,42.
Carbon cycle process model description
DALEC is a box model of C pools connected via fluxes running at a daily time step and has been applied extensively to the MDF research21,43. Its main structure (i.e., C cycle, C allocation, and turnover process) is generally consistent with state-of-the-art process-based models (Fig. S2; Table S1), with five pools (i.e., foliage (Cf), fine root (Cr), woody (Cw, including branches, stems, and coarse roots), litter (Clit) and SOM (Csom)) for evergreen forests and an additional labile pool (Clab) of stored C that supports leaf flushing for deciduous forests. The C cycle was initiated with the canopy C influx: gross primary productivity (GPP), which was predicted by the Aggregated Canopy Model (ACM)44 (Appendix S1). After GPP is consumed by autotrophic respiration (Ra), the remaining photosynthate (NPP) is allocated to plant tissue pools (Cf, Cr, or Cw). The C exiting from all C reservoirs was based on a first order differential equation with various turnover rates, with temperature and moisture dependency on the turnover from the litter and soil pools. In contrast to the original DALEC model only with temperature scalar fTa, here we added a new function fSw to express soil moisture pressure on litter and soil decomposition processes (Appendix S1). In general, the C pools and fluxes in DALEC were iteratively calculated at a daily time step and determined as a function of the key turnover and allocation parameters. A detailed model description can be found in Williams et al.45 and Fox et al.46.
Multiple data-model fusion at the nonsteady state
In a realistic disequilibrium state, C pools are time-variant, i.e., the C efflux is not equal to the C influx (left(frac{dC}{dt}ne 0right)); thus, the MDF was run via the dynamic and long-term CERN observations to constrain the DALEC model at the non-steady state (Eq. 2). Here, to avoid the uncertainty arising from the spin-up process under SSA, we determined the initial state of the C pools by the initial observations of C stocks or by optimization (i.e., Clab, which cannot be directly observed). Then, the turnover and allocation parameters were retrieved under the disequilibrium state with dynamic environmental forcing. This method avoids the considerable uncertainties when invoking the SSA to estimate the initial state of C pools and the C cycle parameters(e.g., allocation coefficients and turnover rates)39,40,47, which could lead to obvious biases in C sequestration19.

$$left{begin{array}{l}Delta {C}_{i}ne 0\ {C}_{i}left({rm{t}}+1right)={C}_{i}left({rm{t}}right)+{I}_{i}left({rm{t}}right)-{k}_{i}{C}_{i}left({rm{t}}right),{rm{i}}=1,2ldots n\ {C}_{i}left({rm{t}}=0right)={C}_{i}0end{array}right.$$
(2)

where Ci, Ii, and ki represent the size, input and turnover rate of the ith C reservoir, respectively; Ci0 represents the initial state of the ith C reservoir; t represents the specific model-running time step (daily step); and ΔCi represents the ith C pool change between t day and t +1 day when applicable into actual calculation. According to the Bayesian theory, the posterior distributions of the parameters are calculated by maximizing the likelihood function (Eq. 3).

$$L={prod }_{j=1}^{m}{prod }_{i=1}^{{n}_{j}}frac{1}{sqrt{2pi }{sigma }_{j}}{e}^{-{left({x}_{j,i}-{mu }_{j,i}left({boldsymbol{P}}right)right)}^{2}/2{sigma }_{j}^{2}}$$
(3)

where L is the integrated likelihood function; m is the number of multiple data types; n is the number of data points categorized by the jth data type; xj,i is the measured value composed of dynamic C cycle observations; μj,i(P) represents the modeled fluxes and stocks based on parameters under the NSSA (P); and σj is the standard deviation of each data point classified by the jth data type. Moreover, we imposed a sequence of ecological and dynamic constraints on the model parameter inter-relationships and pool dynamics (Appendix S2), which can significantly reduce uncertainty in model parameters and simulations48. The more detailed disequilibrium method can be found in our latest study19.
Key C-cycle process parameters and C sequestration data assimilation
Key process parameter estimation
Here, we mainly focus on how the C input (i.e., the net primary productivity) partitioned into various plant pools (i.e., foliar, wood, and fine roots), i.e., allocation coefficients, which could be directly determined from the optimized parameters (Fig. S3) of the DALEC model after the step 2: MDF method. Another key process parameter, C turnover time, needs further simple statistical calculation based on the model simulations with optimized parameters. Turnover time is commonly estimated by the equation “τ = stock/flux”20,49. Since the C influx is not equal to the C efflux in the realistic dynamic disequilibrium state, the turnover time should be defined as the ratio between the mass of a C pool and its outgoing flux50. Note that with few natural and anthropogenic disturbances in these well-protected CERN sites12,18, the C efflux is approximately equivalent to the Rh from soil and litterfall (mortality) and Ra (growth) from vegetation. Hence, the turnover time for vegetation, soil, and whole ecosystem can be derived as follows:

$${tau }_{veg}=frac{{C}_{live}}{{I}_{live}-Delta {C}_{live}}=frac{{C}_{live}}{litterfall+{R}_{a}}$$
(4)

$${tau }_{soil}=frac{{C}_{dead}}{{I}_{dead}-Delta {C}_{dead}}=frac{{C}_{dead}}{{R}_{h}}$$
(5)

$${tau }_{eco}=frac{{C}_{eco}}{{I}_{eco}-Delta {C}_{eco}}=frac{{C}_{live+}{C}_{dead}}{{R}_{a}+{R}_{h}}$$
(6)

where τveɡ, τsoil, and τeco refer to the biomass, soil and whole-ecosystem turnover times, respectively; Clive, Cdead and Ceco refer to the live biomass C pool size (Cf, Cr, and Cw,), dead organic C pool size (Csoil and Clitter), and the whole-ecosystem C pool size, respectively; Ilive, Idead and Ieco refer to the C input into the live biomass C pool, dead organic C pool, and whole ecosystem C pool, respectively; ΔClive, ΔCdead and ΔCeco refer to the changes in the live biomass C pool, dead organic C pool size, and whole-ecosystem C pool size, respectively; and Ra, Rh and litterfall refer to the autotrophic and heterotrophic respiration, and turnover from all live C pools (i.e., foliage, fine root and woody pools),respectively, as calculated from the DALEC output driven with the estimated parameters during 2005–2015. Since the C reservoirs, fluxes, and turnover times are instantaneous values, we used the averages of the fluxes and reservoirs over multiple years to reflect the average turnover time during a specific period (i.e., 2005–2015).
Time-continuous C sequestration estimation
The optimized parameter values under the NSSA along with the initial observations of the corresponding C pool sizes were used in forward modeling driven by dynamic environmental variables from 2005 to 2015 to obtain the time-continuous soil and vegetation C storage51. The difference between the ecosystem C influx (GPP) and ecosystem respiration (Ra+Rh) is used to examine the ecosystem C sequestration, i.e., net ecosystem productivity (NEP). Similarly, the difference between the ecosystem C influx (GPP) and ecosystem autotrophic respiration (Ra) is used to examine the net primary ecosystem productivity (NPP). More

1.
Alerstam, T., Hedenström, A. & Åkesson, S. Long-distance migration: evolution and determinants. Oikos 103, 247–260 (2003).
Article  Google Scholar 
2.
Gilroy, J. J., Gill, J. A., Butchart, S. H. M., Jones, V. R. & Franco, A. M. A. Migratory diversity predicts population declines in birds. Ecol. Lett. 19, 308–317 (2016).
Article  Google Scholar 

3.
Gill, J. A., Alves, J. A. & Gunnarsson, T. G. Mechanisms driving phenological and range change in migratory species. Philos. Trans. R. Soc. B 374, 20180047 (2019).
Article  Google Scholar 

4.
Méndez, V., Gill, J. A., Alves, J. A., Burton, N. H. K. & Davies, R. G. Consequences of population change for local abundance and site occupancy of wintering waterbirds. Divers. Distrib. 24, 24–35 (2018).
Article  Google Scholar 

5.
Finch, T., Butler, S. J., Franco, A. M. A. & Cresswell, W. Low migratory connectivity is common in long-distance migrant birds. J. Anim. Ecol. 86, 662–673 (2017).
Article  Google Scholar 

6.
Phillips, R. A., Silk, J. R. D., Croxall, J. P., Afanasyev, V. & Bennett, V. J. Summer distribution and migration of nonbreeding albatrosses: Individual consistencies and implications for conservation. Ecology 86, 2386–2396 (2005).
Article  Google Scholar 

7.
Newton, I. The Migration Ecology of Birds (Academic Press, New York, 2008).
Google Scholar 

8.
Grist, H. et al. Site fidelity and individual variation in winter location in partially migratory European shags. PLoS ONE 9, e98562 (2014).
ADS  Article  Google Scholar 

9.
Alves, J. A. et al. Costs, benefits, and fitness consequences of different migratory strategies. Ecology 94, 11–17 (2013).
ADS  Article  Google Scholar 

10.
Evans, D. R. et al. Individual condition, but not fledging phenology, carries over to affect post-fledging survival in a neotropical migratory songbird. Ibis (Lond. 1859) 162, 331–344 (2020).
Article  Google Scholar 

11.
Gill, J. A. et al. Why is timing of bird migration advancing when individuals are not?. Proc. Biol. Sci. 281, 20132161 (2014).
PubMed  PubMed Central  Google Scholar 

12.
Meyburg, B.-U. et al. Orientation of native versus translocated juvenile lesser spotted eagles (Clanga pomarina) on the first autumn migration. J. Exp. Biol. 220, 2765–2776 (2017).
Article  Google Scholar 

13.
Gill, J. A. Does competition really drive population distributions?. Wader Study 126, 166–168 (2019).
Article  Google Scholar 

14.
Berthold, P. Control of Bird Migration (Chapman & Hall, Boca Raton, 1996).
Google Scholar 

15.
Sutherland, W. J. Evidence for flexibility and constraint in migration systems. J. Avian Biol. 29, 441 (1998).
Article  Google Scholar 

16.
Harrison, X. A. et al. Cultural inheritance drives site fidelity and migratory connectivity in a long-distance migrant. Mol. Ecol. 19, 5484–5496 (2010).
Article  Google Scholar 

17.
Piersma, T., Loonstra, A. H. J., Verhoeven, M. A. & Oudman, T. Rethinking classic starling displacement experiments: evidence for innate or for learned migratory directions?. J. Avian Biol. 51, jav.02337 (2020).
Article  Google Scholar 

18.
Þórisson, B. et al. Population size of oystercatchers Haematopus ostralegus wintering in Iceland. Bird Study 65, 274–278 (2018).
Article  Google Scholar 

19.
Méndez, V. et al. Individual variation in migratory behaviour in a sub-Arctic partial migrant shorebird. Behav. Ecol. https://doi.org/10.1093/beheco/araa010 (2020).
Article  Google Scholar 

20.
van de Pol, M. et al. A global assessment of the conservation status of the nominate subspecies of Eurasian oystercatcher Haematopus ostralegus ostralegus. Int. Wader Stud. 20, 47–61 (2014).
Google Scholar 

21.
Méndez, V. et al. Effects of migratory behaviour on breeding phenology and success in a sub-arctic partially migratory shorebird. J. Anim. Ecol. (under review).

22.
Ens, B. J., Safriel, U. N. & Harris, M. P. Divorce in the long-lived and monogamous oystercatcher, Haematopus ostralegus: incompatibility or choosing the better option?. Anim. Behav. 45, 1199–1217 (1993).
Article  Google Scholar 

23.
Ens, B. J., Choudhury, S. & Black, J. M. Mate fidelity and divorce in monogamous birds. In Partnerships in Birds: The Study of Monogamy (ed. Black, J. M.) 344–401 (Oxford University Press, Oxford, 1996).
Google Scholar 

24.
Winger, B. M., Auteri, G. G., Pegan, T. M. & Weeks, B. C. A long winter for the Red Queen: rethinking the evolution of seasonal migration. Biol. Rev. 94, 737–752 (2019).
Article  Google Scholar 

25.
Bulla, M. et al. Unexpected diversity in socially synchronized rhythms of shorebirds. Nature 540, 109–113 (2016).
ADS  CAS  Article  Google Scholar 

26.
Nol, E. Sex roles in the American oystercatcher. Behaviour 95, 232–260 (1985).
Article  Google Scholar 

27.
Reynolds, J. D. & Székely, T. The evolution of parental care in shorebirds: life histories, ecology, and sexual selection. Behav. Ecol. 8, 126–134 (1997).
Article  Google Scholar 

28.
Lazarus, J. The logic of mate desertion. Anim. Behav. 39, 672–684 (1990).
Article  Google Scholar 

29.
Safriel, U. N., Ens, B. J., Kaiser, A. & Goss-Custard, J. D. Rearing to independence. In The Oystercatcher: From Individuals to Populations (ed. Goss-Custard, J. D.) 219–250 (Oxford University Press, Oxford, 1996).
Google Scholar 

30.
Gunnarsson, T. G., Gill, J. A., Sigurbjörnsson, T. & Sutherland, W. J. Pair bonds: arrival synchrony in migratory birds. Nature 431, 646 (2004).
ADS  CAS  Article  Google Scholar 

31.
Gunnarsson, T. G., Gill, J. A., Newton, J., Potts, P. M. & Sutherland, W. J. Seasonal matching of habitat quality and fitness in a migratory bird. Proc. R. Soc. B 272, 2319–2323 (2005).
Article  Google Scholar 

32.
Gunnarsson, T. G. Monitoring wader productivity during autumn passage in Iceland. Wader Study Gr. Bull. 110, 21–29 (2006).
Google Scholar 

33.
Cramp, S. & Simmons, K. E. L. Birds of the Western Palearctic (Oxford University Press, Oxford, 1983).
Google Scholar 

34.
Alves, J. A., Gunnarsson, T. G., Sutherland, W. J., Potts, P. M. & Gill, J. A. Linking warming effects on phenology, demography, and range expansion in a migratory bird population. Ecol. Evol. 9, 2365–2375 (2019).
Article  Google Scholar 

35.
Liebezeit, J. R. et al. Assessing the development of shorebird eggs using the flotation method: species-specific and generalized regression models. Condor 109, 32–47 (2007).
Article  Google Scholar 

36.
Burnham, K. & Anderson, D. Model Selection and Multi-model Inference: A Practical Information-Theoretic Approach (Springer, Berlin, 2002).
Google Scholar 

37.
R Core Team. A Language and Environment for Statistical Computing (R Foundation for Statistical Computing, Vienna, 2020).
Google Scholar  More

1.
Litchman, E. et al. Global biogeochemical impacts of phytoplankton: a trait-based perspective. J. Ecol. 103, 1384–1396 (2015).
CAS  Article  Google Scholar 
2.
De Senerpont Domis, L. N. et al. Plankton dynamics under different climatic conditions in space and time. Freshw. Biol. 58, 463–482 (2013).
Article  Google Scholar 

3.
Reynolds, C. Ecology of Phytoplankton. (Cambridge University Press, 2006).

4.
Phillips, G. et al. Water Framework Directive Intercalibration: Central Baltic Lake Phytoplankton Ecological Assessment Methods. 189 (Join Research Center, 2014).

5.
Ptacnik, R., Solimini, A. & Brettum, P. Performance of a new phytoplankton composition metric along a eutrophication gradient in Nordic lakes. Hydrobiologia 633, 75–82 (2009).
CAS  Article  Google Scholar 

6.
Pollard, A. I., Hampton, S. E. & Leech, D. M. The promise and potential of continental-scale limnology using the U.S. Environmental Protection Agency’s National Lakes. Assessment. Limnol. Oceanogr. Bull. 27, 36–41 (2018).
Article  Google Scholar 

7.
de Hoyos, C. et al. Water Framework Directive Intercalibration: Mediterranean Lake Phytoplankton Ecological Assessment Methods. 189 (Join Research Center, 2014).

8.
Mischke, U., Riedmüller, U., Hoehn, E., Schönfelder, I. & Nixdorf, B. Description of the German System for Phytoplankton-Based Assessment of Lakes for Implementation of the EU Water Framework Directive (WFD). 31 (Univ. Cottbus, 2008).

9.
Laplace-Treyture, C. & Feret, T. Performance of the Phytoplankton Index for Lakes (IPLAC): A multimetric phytoplankton index to assess the ecological status of water bodies in France. Ecol. Indic. 69, 686–698 (2016).
CAS  Article  Google Scholar 

10.
Xue, Y. et al. Distinct patterns and processes of abundant and rare eukaryotic plankton communities following a reservoir cyanobacterial bloom. ISME J. 12, 2263–2277 (2018).
CAS  Article  Google Scholar 

11.
Barbe, J. et al. Actualisation de la Méthode de Diagnose Rapide des Plans d’Eau: Analyse Critique des Indices de Qualité des Lacs et Propositions d’Indices de Fonctionnement de l’Écosystème Lacustre. 107 (Cemagref, 2003).

12.
Marchetto, A., Padedda, B., Mariani, M., Luglie, A. & Sechi, N. A numerical index for evaluating phytoplankton response to changes in nutrient levels in deep mediterranean reservoirs. J. Limnol. 68, 106–121 (2009).
Article  Google Scholar 

13.
Kruk, C., Mazzeo, N., Lacerot, G. & Reynolds, C. S. Classification schemes for phytoplankton: A local validation of a functional approach to the analysis of species temporal replacement. J. Plankton Res. 24, 901–912 (2002).
Article  Google Scholar 

14.
Reynolds, C. S. Phytoplankton designer – or how to predict compositional responses to trophic-state change. Hydrobiologia 424, 123–132 (2000).
Article  Google Scholar 

15.
Reynolds, C. S., Huszar, V., Kruk, C., Naselli-Flores, L. & Melo, S. Towards a functional classification of the freshwater phytoplankton. J. Plankton Res. 24, 417–428 (2002).
Article  Google Scholar 

16.
Mieleitner, J. & Reichert, P. Modelling functional groups of phytoplankton in three lakes of different trophic state. Ecol. Model. 211, 279–291 (2008).
Article  Google Scholar 

17.
Rangel, L. M., Soares, M. C. S., Paiva, R. & Silva, L. H. S. Morphology-based functional groups as effective indicators of phytoplankton dynamics in a tropical cyanobacteria-dominated transitional river–reservoir system. Ecol. Indic. 64, 217–227 (2016).
Article  Google Scholar 

18.
Salmaso, N., Naselli-Flores, L. & Padisák, J. Functional classifications and their application in phytoplankton ecology. Freshw. Biol. 60, 603–619 (2015).
Article  Google Scholar 

19.
Padisák, J., Borics, G., Grigorszky, I. & Soróczki-Pintér, É. Use of phytoplankton assemblages for monitoring ecological status of lakes within the water framework directive: The assemblage index. Hydrobiologia 553, 1–14 (2006).
Article  Google Scholar 

20.
Borics, G. et al. A new evaluation technique of potamo-plankton for the assessment of the ecological status of rivers. Large Rivers 17, 465–486 (2007).
Google Scholar 

21.
European Parliament. Directive 2000/60/CE du Parlement Européen et du Conseil du 23 Octobre 2000 Établissant un Cadre pour une Politique Communautaire dans le Domaine de l’Eau. 72 (Communauté Européenne, 2000).

22.
Padisák, J., Crossetti, L. O. & Naselli-Flores, L. Use and misuse in the application of the phytoplankton functional classification: A critical review with updates. Hydrobiologia 621, 1–19 (2009).
Article  Google Scholar 

23.
Kruk, C. et al. Classification of Reynolds phytoplankton functional groups using individual traits and machine learning techniques. Freshw. Biol. 62, 1681–1692 (2017).
CAS  Article  Google Scholar 

24.
Wentzky, V. C., Tittel, J., Jäger, C. G., Bruggeman, J. & Rinke, K. Seasonal succession of functional traits in phytoplankton communities and their interaction with trophic state. J. Ecol. 108, 1649–1663 (2020).
CAS  Article  Google Scholar 

25.
Olenina, I. et al. Biovolumes and Size-Classes of Phytoplankton in the Baltic Sea. 144 (Baltic Marine Environnment Protection Commission, 2006).

26.
Kremer, C. T., Gillette, J. P., Rudstam, L. G., Brettum, P. & Ptacnik, R. A compendium of cell and natural unit biovolumes for >1200 freshwater phytoplankton species. Ecology 95, 2984–2984 (2014).
Article  Google Scholar 

27.
Druart, J. C. & Rimet, F. Protocole d’Analyse du Phytoplancton de l’INRA: Prélèvement, Dénombrement et Biovolume. 96 (INRA, 2008).

28.
Rimet, F. & Druart, J.-C. A trait database for phytoplankton of temperate lakes. Ann. Limnol. – Int. J. Limnol. 54, 18 (2018).
Article  Google Scholar 

29.
John, D. M., Whitton, B. A. & Brook, A. J. The Freshwater Algal Flora of the British Isles: an Identification Guide to Freshwater and Terrestrial Algae. Second Edition. (Cambridge University Press, 2011).

30.
Wehr, J. D., Sheath, R. G. & Kociolek, P. Freshwater Algae of North America: Ecology and Classification. (Academic press, 2015).

31.
Laplace-Treyture, C., Hadoux, E., Plaire, M., Dubertrand, A. & Esmieu, P. PHYTOBS v3.0: Outil de Comptage du Phytoplancton en Laboratoire et de Calcul de l’IPLAC. Version 3.0. Application JAVA. https://hydrobio-dce.inrae.fr/phytobs-software/ (2017).

32.
Hillebrand, H., Dürselen, C. D., Kirschtel, D., Pollinger, U. & Zohary, T. Biovolume calculation for pelagic and benthic microalgae. J. Phycol. 35, 403–424 (1999).
Article  Google Scholar 

33.
Hutorowicz, A. Opracowanie Standardowych Objętości Komórek do Szacowania Biomasy w Wybranych Taksonów Glonów Planktonowych Wraz z Określeniem Sposobu Pomiarów i Szacowania. 42 (Instytutu Rybactwa Śródlądowego, 2005).

34.
Padisak, J. & Adrian, R. In Methoden der Biologischen Wasseruntersuchung 2. Biologische Gewässeruntersuchung (ed. Friedrich, W. und G.) Biovolumen und Biomasse (Gustav Fischer Verlag, 1999).

35.
NF EN 16695. Qualité de l’eau – Lignes Directrices pour l’Estimation du Biovolume des Microalgues. 106 (2015).

36.
Menden-Deuer, S. & Lessard, E. J. Carbon to volume relationships for dinoflagellates, diatoms, and other protist plankton. Limnol. Oceanogr. 45, 569–579 (2000).
ADS  CAS  Article  Google Scholar 

37.
Sieburth, J. M., Smetacek, V. & Lenz, J. Pelagic ecosystem structure: Heterotrophic compartments of the plankton and their relationship to plankton size fractions. Limnol. Oceanogr. 23, 1256–1263 (1978).
ADS  Article  Google Scholar 

38.
Ignatiades, L. Redefinition of cell size classification of phytoplankton – a potential tool for improving the quality and assurance of data interpretation. Mediterr. Mar. Sci. 17, 56 (2015).
Article  Google Scholar 

39.
Whitton, B. A. Ecology of Cyanobacteria II. Their Diversity in Space and Time. (Springer Verlag, 2012).

40.
Dittmann, E., Gugger, M., Sivonen, K. & Fewer, D. P. Natural product biosynthetic diversity and comparative genomics of the Cyanobacteria. Trends Microbiol. 23, 642–652 (2015).
CAS  Article  Google Scholar 

41.
Sanseverino, I., Conduto, D., Pozzoli, L., Dobricic, S. & Lettieri, T. Algal Bloom and its Economic Impact. 48 (Join Research Center, 2016).

42.
Sanseverino, I., Conduto Antonio, D., Loos, R. & Lettieri, T. Cyanotoxins: Methods and Approaches for their Analysis and Detection. 64 (Join Research Center, 2017).

43.
Meriluoto, J., Spoof, L. & Codd, G. A. Handbook of Cyanobacterial Monitoring and Cyanotoxin Analysis. (John Wiley & Sons, 2017).

44.
Lwoff, A., Van Niel, C. B., Ryan, P. J. & Tatum, E. L. Nomenclature of Nutritional Types of Microorganisms. In Cold Spring Harbor Symposia on Quantitative Biology. XI (5th ed.) 302–303 (1946).

45.
Morris, J. Biology: How Life Works. (W. H. Freeman/Macmillan Learning, 2018).

46.
Laplace-Treyture, C. et al. Phytoplankton morpho-functional trait dataset from French water-bodies. Portail Data INRAE https://doi.org/10.15454/GJGIAH (2020).

47.
Morabito, G., Oggioni, A., Caravati, E. & Panzani, P. Seasonal morphological plasticity of phytoplankton in Lago Maggiore (N. Italy). Hydrobiologia 578, 47–57 (2007).
Article  Google Scholar 

48.
Naselli-Flores, L., Padisák, J. & Albay, M. Shape and size in phytoplankton ecology: Do they matter? Hydrobiologia 578, 157–161 (2007).
Article  Google Scholar 

49.
Strathmann, R. R. Estimating the organic carbon content of phytoplankton from cell volume or plasma volume. Limnol. Oceanogr. 12, 411–418 (1967).
ADS  CAS  Article  Google Scholar 

50.
Chaffin, J. D., Stanislawczyk, K., Kane, D. D. & Lambrix, M. M. Nutrient addition effects on chlorophyll a, phytoplankton biomass, and heterocyte formation in Lake Erie’s central basin during 2014–2017: Insights into diazotrophic blooms in high nitrogen water. Freshw. Biol. 00, 1–15 (2020).
Google Scholar 

51.
Hadoux, E. & Laplace-Treyture, C. PHYTOBS: Phytoplankton Counting Tool in Laboratory. Version 1.0. JAVA Application. https://hydrobio-dce.inrae.fr/phytobs-software/ (2009).

52.
Huber Pestalozzi, G. & Thienemann, A. Das Phytoplankton des Susswassers Systematik und Biologie: 5 Teil Chlorophyceae (Grünalgen) Ordnung: Volvocales. (E. Schweizerbart’sche verlagsbuchhandlung, 1974).

53.
Komarek, J., Fott, B. & Huber Pestalozzi, G. Das Phytoplankton des Susswassers Systematik und Biologie: 7 Teil 1 Halfte Chlorophyceae (Grunalgen) Ordnung: Chlorococcales. (E. Schweizerbart’sche verlagsbuchhandlung, 1983).

54.
Coesel, P. F. M. & Meesters, K. J. Desmids of the Lowlands: Mesotaeniaceae and Desmidiaceae of the European Lowlands. (KNNV Publishing, 2007).

55.
Coesel, P. F. M. & Meesters, K. European Flora of the Desmid Genera Staurastrum and Staurodesmus. (KNNV Publishing, 2013).

56.
Starmach, K. Chrysophyceae und Haptophyceae. (VEB Gustav Fischer Verlag, 1985).

57.
Komarek, J. & Anagnostidis, K. Cyanoprokaryota 1.Teil: Chroococcales. (Gustav Fischer, 1999).

58.
Komarek, J. & Anagnostidis, K. Cyanoprokaryota 2.Teil: Oscillatoriales. (Elsevier, 2005).

59.
Komarek, J. Cyanoprokaryota: 3. Teil/Part 3: Heterocytous Genera. (Springer Spektrum Verlag, 2013).

60.
Anses. Evaluation des Risques Liés aux Cyanobactéries et leurs Toxines dans les Eaux Douces. Avis de l’Anses. 438 (Anses, 2020).

61.
Bey, M.-Y. & Ector, L. Atlas des Diatomées des Cours d’Eau de la Région Rhône-Alpes. (DREAL Rhône-Alpes, 2013).

62.
Cox, E. J. Identification of Freshwater Diatoms from Live Material. (Chapman & Hall, 1996).

63.
Druart, J. C. & Straub, F. Description de deux nouvelles Cyclotelles (Bacillariophyceae) de milieux alcalins et eutrophes: Cyclotella costei nov. sp. et Cyclotella wuethrichiana nov. sp. Swiss J. Hydrol. 50, 182–188 (1988).
Article  Google Scholar 

64.
Houk, V. Atlas of Freshwater Centric Diatoms with a Brief Key and Descriptions Part I Melosiraceae, Orthoseiraceae, Paraliaceae and Aulacoseiraceae. vol. 1 (Czech Phycological Society, Prague & Palacký University Olomouc, 2003).

65.
Houk, V. & Klee, R. Atlas of freshwater centric diatoms with a brief key and descriptions Part II Melosiraceae and Aulacoseiraceae (Supplement to Part I). Fottea J. Czech Phycol. Soc. 7, 85–255 (2007).
Google Scholar 

66.
Houk, V., Klee, R. & Tanaka, H. Atlas of Freshwater Centric Diatoms with a Brief Key and Descriptions Part IV Stephanodiscaceae B. vol. 14 (Czech Phycological Society, Prague & Palacký University Olomouc, 2014).

67.
Houk, V., Klee, R. & Tanaka, H. Atlas of Freshwater Centric Diatoms with a Brief Key and Descriptions Part III Steogabiduscaceae A Cyclotella, Tertiarius, Discostella. vol. 10 (Czech Phycological Society, Prague & Palacký University Olomouc, 2010).

68.
Houk, V., Klee, R. & Tanaka, H. Atlas of freshwater centric diatoms with a brief key and descriptions: second emended edition of Part I and II Melosiraceae, Orthoseiraceae, Paraliaceae and Aulacoseiraceae. Fottea J. Czech Phycol. Soc. 17, 1–615 (2017).
Google Scholar 

69.
Krammer, K. & Lange Bertalot, H. Bacillariophyceae. 1. Teil: Naviculaceae. (Specktrum Akademischer Verlag GmbH Heidelberg, 1999).

70.
Krammer, K. & Lange Bertalot, H. Bacillariophyceae. 4. Teil: Achnanthaceae Kritische Ergänzungen zu Achnanthes s.l., Bavicula s. str., Gomphonema. (Spektrum, 2004).

71.
Krammer, K. & Lange Bertalot, H. Bacillariophyceae. 2. Teil: Bacillariaceae, Epithemiaceae, Surirellaceae. (Elsevier, 2007).

72.
Krammer, K. & Lange-Bertalot, H. Bacillariophyceae. 3. Teil: Centrales, Fragilariaceae, Eunotiaceae. (Gustav Fischer Verlag, 2004).

73.
Lange-Bertalot, H., Hofmann, G., Werum, M. & Cantonati, M. Freshwater Benthic Diatoms of Central Europe: Over 800 Common Species Used in Ecological Assessment. (Koeltz Botanical Books, 2017).

74.
Siver, P. A. et al. Observations on Fragilaria longifusiformis comb. nov. et nom. nov. (Bacillariophyceae), a widespread planktic diatom documented from North America and Europe. Phycol. Res. 54, 183–192 (2006).
Article  Google Scholar 

75.
Popovsky, J. & Pfiester, L. A. Dinophyceae (Dinoflagellida). (Gustav Fischer Verlag, 1990).

76.
Moestrup, Ø. & Calado, A. J. Dinophyceae. vol. 6 (Spektrum Akademischer Verlag, 2018).

77.
Huber Pestalozzi, G. Das Phytoplankton des Susswassers Systematik und Biologie: 4 Teil Euglenophyceen. (E. Schweizerbart’sche verlagsbuchhandlung, 1969).

78.
Ettl, H. Xanthophyceae: 1. Teil. (Gustav Fischer Verlag, 1978).

79.
Rieth, A. Xanthophyceae: 2. Teil. (Gustav Fischer Verlag, 1980). More

1.
Sender, R., Fuchs, S. & Milo, R. Revised estimates for the number of human and bacteria cells in the body. PLOS Biol. 14, e1002533 (2016).
PubMed  PubMed Central  Article  CAS  Google Scholar 
2.
Kho, Z. Y. & Lal, S. K. The human gut microbiome—A potential controller of wellness and disease. Front. Microbiol. 9, 1835 (2018).
PubMed  PubMed Central  Article  Google Scholar 

3.
Kostic, A. D., Xavier, R. J. & Gevers, D. The microbiome in inflammatory bowel disease: Current status and the future ahead. Gastroenterology 146, 1489–1499 (2014).
CAS  PubMed  PubMed Central  Article  Google Scholar 

4.
Thaiss, C. A., Zmora, N., Levy, M. & Elinav, E. The microbiome and innate immunity. Nature 535, 65–74 (2016).
CAS  PubMed  Article  ADS  PubMed Central  Google Scholar 

5.
Das, B. & Nair, G. B. Homeostasis and dysbiosis of the gut microbiome in health and disease. J. Biosci. 44, 117 (2019).
PubMed  Article  PubMed Central  Google Scholar 

6.
Shreiner, A. B., Kao, J. Y. & Young, V. B. The gut microbiome in health and in disease. Curr. Opin. Gastroenterol. 31, 69–75 (2015).
CAS  PubMed  PubMed Central  Article  Google Scholar 

7.
Petersen, C. & Round, J. L. Defining dysbiosis and its influence on host immunity and disease: How changes in microbiota structure influence health. Cell. Microbiol. 16, 1024–1033 (2014).
CAS  PubMed  PubMed Central  Article  Google Scholar 

8.
Karlsson, F. H. et al. Gut metagenome in European women with normal, impaired and diabetic glucose control. Nature 498, 99–103 (2013).
CAS  PubMed  PubMed Central  Article  ADS  Google Scholar 

9.
Koren, O. et al. Human oral, gut, and plaque microbiota in patients with atherosclerosis. Proc. Natl. Acad. Sci. 108, 4592–4598 (2011).
CAS  PubMed  Article  ADS  PubMed Central  Google Scholar 

10.
Karlsson, F. H. et al. Symptomatic atherosclerosis is associated with an altered gut metagenome. Nat. Commun. 3, 1245 (2012).
PubMed  PubMed Central  Article  ADS  CAS  Google Scholar 

11.
Chatelier, L. M. et al. Richness of human gut microbiome correlates with metabolic markers. Nature 500, 541–546 (2013).
PubMed  Article  CAS  PubMed Central  Google Scholar 

12.
Franzosa, E. A. et al. Gut microbiome structure and metabolic activity in inflammatory bowel disease. Nat. Microbiol. 4, 293–305 (2019).
CAS  PubMed  Article  PubMed Central  Google Scholar 

13.
Becker, C., Neurath, M. F. & Wirtz, S. The intestinal microbiota in inflammatory bowel disease. ILAR J. 56, 192–204 (2015).
CAS  PubMed  Article  PubMed Central  Google Scholar 

14.
Kostic, A. D. et al. Fusobacterium nucleatum potentiates intestinal tumorigenesis and modulates the tumor-immune microenvironment. Cell Host Microbe 14, 207–215 (2013).
CAS  PubMed  PubMed Central  Article  Google Scholar 

15.
The Human Microbiome Project Consortium. Structure, function and diversity of the healthy human microbiome. Nature 486, 207–214 (2012).
PubMed Central  Article  ADS  CAS  Google Scholar 

16.
Johnson, A. J. et al. Daily sampling reveals personalized diet–microbiome associations in humans. Cell Host Microbe 25, 789-802.e5 (2019).
CAS  PubMed  Article  PubMed Central  Google Scholar 

17.
Villmones, H. C. et al. Species level description of the human ileal bacterial microbiota. Sci. Rep. 8, 1–9 (2018).
CAS  Article  Google Scholar 

18.
Gevers, D. et al. The treatment-naive microbiome in new-onset Crohn’s disease. Cell Host Microbe 15, 382–392 (2014).
CAS  PubMed  PubMed Central  Article  Google Scholar 

19.
David, L. A. et al. Diet rapidly and reproducibly alters the human gut microbiome. Nature 505, 559–563 (2014).
CAS  PubMed  Article  ADS  PubMed Central  Google Scholar 

20.
Zhou, J., Deng, Y., Luo, F., He, Z. & Yang, Y. Phylogenetic molecular ecological network of soil microbial communities in response to elevated CO2. MBio 2, e00122-e211 (2011).
PubMed  PubMed Central  Article  Google Scholar 

21.
Lupatini, M. et al. Network topology reveals high connectance levels and few key microbial genera within soils. Front. Environ. Sci. 2, 10 (2014).
Article  Google Scholar 

22.
Eiler, A., Heinrich, F. & Bertilsson, S. Coherent dynamics and association networks among lake bacterioplankton taxa. ISME J. 6, 330–342 (2012).
CAS  PubMed  Article  Google Scholar 

23.
Kara, E. L., Hanson, P. C., Hu, Y. H., Winslow, L. & McMahon, K. D. A decade of seasonal dynamics and co-occurrences within freshwater bacterioplankton communities from eutrophic Lake Mendota, WI, USA. ISME J. 7, 680–684 (2013).
PubMed  Article  Google Scholar 

24.
Shetty, S. A., Hugenholtz, F., Lahti, L., Smidt, H. & de Vos, W. M. Intestinal microbiome landscaping: Insight in community assemblage and implications for microbial modulation strategies. FEMS Microbiol. Rev. 41, 182–199 (2017).
CAS  PubMed  PubMed Central  Article  Google Scholar 

25.
Gould, A. L. et al. Microbiome interactions shape host fitness. Proc. Natl. Acad. Sci. 115, E11951–E11960 (2018).
CAS  PubMed  Article  Google Scholar 

26.
Hibbing, M. E., Fuqua, C., Parsek, M. R. & Peterson, S. B. Bacterial competition: Surviving and thriving in the microbial jungle. Nat. Rev. Microbiol. 8, 15–25 (2010).
CAS  PubMed  PubMed Central  Article  Google Scholar 

27.
Fox, G. E., Magrum, L. J., Balcht, W. E., Wolfef, R. S. & Woese, C. R. Classification of methanogenic bacteria by 16S ribosomal RNA characterization (comparative oligonucleotide cataloging/phylogeny/molecular evolution). Evolution (N.Y.) 74, 4537–4541 (1977).
CAS  Google Scholar 

28.
Venter, J. C. et al. Environmental genome shotgun sequencing of the Sargasso Sea. Science 304, 66 (2004).
CAS  PubMed  Article  ADS  Google Scholar 

29.
Větrovský, T. & Baldrian, P. The variability of the 16S rRNA gene in bacterial genomes and its consequences for bacterial community analyses. PLoS ONE 8, e57923 (2013).
PubMed  PubMed Central  Article  ADS  CAS  Google Scholar 

30.
Edgar, R. C. Accuracy of taxonomy prediction for 16S rRNA and fungal ITS sequences. PeerJ 6, 1–29 (2018).
Google Scholar 

31.
Ranjan, R., Rani, A., Metwally, A., McGee, H. S. & Perkins, D. L. Analysis of the microbiome: Advantages of whole genome shotgun versus 16S amplicon sequencing. Biochem. Biophys. Res. Commun. 469, 967–977 (2016).
CAS  PubMed  Article  Google Scholar 

32.
Laudadio, I. et al. Quantitative assessment of shotgun metagenomics and 16S rDNA amplicon sequencing in the study of human gut microbiome. Omi. A J. Integr. Biol. 22, 248–254 (2018).
CAS  Article  Google Scholar 

33.
Gloor, G. B., Macklaim, J. M., Pawlowsky-Glahn, V. & Egozcue, J. J. Microbiome datasets are compositional: And this is not optional. Front. Microbiol. 8, 1–6 (2017).
Article  Google Scholar 

34.
Friedman, J. & Alm, E. J. Inferring correlation networks from genomic survey data. PLoS Comput. Biol. 8, e1002687 (2012).
CAS  PubMed  PubMed Central  Article  ADS  Google Scholar 

35.
Layeghifard, M., Hwang, D. M. & Guttman, D. S. Disentangling interactions in the microbiome: A network perspective. Trends Microbiol. 25, 217–228 (2017).
CAS  PubMed  Article  Google Scholar 

36.
Aitchison, J. The statistical analysis of compositional data. J. R. Stat. Soc. Ser. B 44, 40 (1982).
MathSciNet  MATH  Google Scholar 

37.
Kurtz, Z. D. et al. Sparse and compositionally robust inference of microbial ecological networks. PLoS Comput. Biol. 11, 1–25 (2015).
Article  CAS  Google Scholar 

38.
Friedman, J., Hastie, T. & Tibshirani, R. Sparse inverse covariance estimation with the graphical lasso. Biostatistics 9, 432–441 (2008).
PubMed  MATH  Article  Google Scholar 

39.
Falony, G. et al. Population-level analysis of gut microbiome variation. Science 352, 560–564 (2016).
CAS  PubMed  Article  ADS  Google Scholar 

40.
Efron, B. & Tibshirani, R. Bootstrap methods for standard errors, confidence intervals, and other measures of statistical accuracy. Stat. Sci. 1, 54–75 (1986).
MathSciNet  MATH  Article  Google Scholar 

41.
Su, W., Bogdan, M., Candès, E. & Candes, E. False discoveries occur early on the lasso path. Ann. Stat. 45, 2133–2150 (2017).
MathSciNet  MATH  Article  Google Scholar 

42.
Saunders, A. M., Albertsen, M., Vollertsen, J. & Nielsen, P. H. The activated sludge ecosystem contains a core community of abundant organisms. ISME J. 10, 11–20 (2016).
CAS  PubMed  Article  PubMed Central  Google Scholar 

43.
Tsvetovat, M. & Kouznetsov, A. Social network analysis for startups. Zhurnal Eksperimental’noi i Teoreticheskoi Fiziki (2011).

44.
Stadtfeld, C., Takács, K. & Vörös, A. The emergence and stability of groups in social networks. Soc. Netw. 60, 129–145 (2020).
Article  Google Scholar 

45.
Cordasco, G. & Gargano, L. Community detection via semi-synchronous label propagation algorithms. 2010 IEEE Int. Work. Bus. Appl. Soc. Netw. Anal. BASNA 2010 (2010). https://doi.org/10.1109/BASNA.2010.5730298.

46.
Prettejohn, B. J., Berryman, M. J. & McDonnell, M. D. Methods for generating complex networks with selected structural properties for simulations: A review and tutorial for neuroscientists. Front. Comput. Neurosci. 5, 11 (2011).
PubMed  PubMed Central  Article  Google Scholar 

47.
Guimerà, R., Sales-Pardo, M. & Amaral, L. A. N. Modularity from fluctuations in random graphs and complex networks. Phys. Rev. E 70, 25101 (2004).
Article  ADS  CAS  Google Scholar 

48.
Trosvik, P. & de Muinck, E. J. Ecology of bacteria in the human gastrointestinal tract—Identification of keystone and foundation taxa. Microbiome 3, 44 (2015).
PubMed  PubMed Central  Article  Google Scholar 

49.
Verster, A. J. & Borenstein, E. Competitive lottery-based assembly of selected clades in the human gut microbiome. Microbiome 6, 186 (2018).
PubMed  PubMed Central  Article  Google Scholar 

50.
Berry, D. & Widder, S. Deciphering microbial interactions and detecting keystone species with co-occurrence networks. Front. Microbiol. 5, 219 (2014).
PubMed  PubMed Central  Article  Google Scholar 

51.
Foster, K. R. & Bell, T. Competition, not cooperation, dominates interactions among culturable microbial species. Curr. Biol. 22, 1845–1850 (2012).
CAS  PubMed  Article  PubMed Central  Google Scholar 

52.
Nemergut, D. R. et al. Patterns and processes of microbial community assembly. Microbiol. Mol. Biol. Rev. 77, 342–356 (2013).
PubMed  PubMed Central  Article  Google Scholar 

53.
Hamilton, W. D. The genetical evolution of social behaviour. I. J. Theor. Biol. 7, 1–16 (1964).
CAS  PubMed  Article  PubMed Central  Google Scholar 

54.
Hardin, G. The competitive exclusion principle. Science 131, 1292–1297 (1960).
CAS  PubMed  Article  ADS  Google Scholar 

55.
Jackson, M. A. et al. Detection of stable community structures within gut microbiota co-occurrence networks from different human populations. PeerJ 6, e4303 (2018).
PubMed  PubMed Central  Article  CAS  Google Scholar 

56.
Darcy, J. L. et al. A phylogenetic model for the recruitment of species into microbial communities and application to studies of the human microbiome. ISME J. 14, 1359–1368 (2020).
PubMed  Article  Google Scholar 

57.
Pacheco, A. R., Moel, M. & Segrè, D. Costless metabolic secretions as drivers of interspecies interactions in microbial ecosystems. Nat. Commun. 10, 103 (2019).
PubMed  PubMed Central  Article  ADS  CAS  Google Scholar 

58.
Chase, J. M. & Leibold, M. A. Spatial scale dictates the productivity–biodiversity relationship. Nature 416, 427–430 (2002).
CAS  PubMed  Article  ADS  PubMed Central  Google Scholar 

59.
Zarrinpar, A., Chaix, A., Yooseph, S. & Panda, S. Diet and feeding pattern affect the diurnal dynamics of the gut microbiome. Cell Metab. 20, 1006–1017 (2014).
CAS  PubMed  PubMed Central  Article  Google Scholar 

60.
Mark Welch, J. L., Hasegawa, Y., McNulty, N. P., Gordon, J. I. & Borisy, G. G. Spatial organization of a model 15-member human gut microbiota established in gnotobiotic mice. Proc. Natl. Acad. Sci. 114, E9105–E9114 (2017).
CAS  PubMed  Article  Google Scholar 

61.
Fung, T. C., Artis, D. & Sonnenberg, G. F. Anatomical localization of commensal bacteria in immune cell homeostasis and disease. Immunol. Rev. 260, 35–49 (2014).
CAS  PubMed  PubMed Central  Article  Google Scholar 

62.
Donaldson, G. P., Lee, S. M. & Mazmanian, S. K. Gut biogeography of the bacterial microbiota. Nat. Rev. Microbiol. 14, 20–32 (2016).
CAS  PubMed  Article  Google Scholar 

63.
Stachowicz, J. J. Mutualism, facilitation, and the structure of ecological communities. Bioscience 51, 235 (2001).
Article  Google Scholar 

64.
Lozupone, C. A., Stombaugh, J. I., Gordon, J. I., Jansson, J. K. & Knight, R. Diversity, stability and resilience of the human gut microbiota. Nature 489, 220–230 (2012).
CAS  PubMed  PubMed Central  Article  ADS  Google Scholar 

65.
Lynd, L. R., Weimer, P. J., van Zyl, W. H. & Pretorius, I. S. Microbial cellulose utilization: Fundamentals and biotechnology. Microbiol. Mol. Biol. Rev. 66, 72 (2002).
Article  Google Scholar 

66.
Turroni, F. et al. Glycan cross-feeding activities between bifidobacteria under in vitro conditions. Front. Microbiol. 6, 1030 (2015).
PubMed  PubMed Central  Article  Google Scholar 

67.
Hall, C. V. et al. Co-existence of network architectures supporting the human gut microbiome. iScience 22, 380–391 (2019).
PubMed  PubMed Central  Article  ADS  Google Scholar 

68.
Fisher, C. K. & Mehta, P. Identifying keystone species in the human gut microbiome from metagenomic timeseries using sparse linear regression. PLoS ONE 9, e102451 (2014).
PubMed  PubMed Central  Article  ADS  CAS  Google Scholar 

69.
Jones, M. B. et al. Library preparation methodology can influence genomic and functional predictions in human microbiome research. Proc. Natl. Acad. Sci. 112, 14024–14029 (2015).
CAS  PubMed  Article  ADS  Google Scholar 

70.
Lahti, L., Salojärvi, J., Salonen, A., Scheffer, M. & de Vos, W. M. Tipping elements in the human intestinal ecosystem. Nat. Commun. 5, 4344 (2014).
CAS  PubMed  PubMed Central  Article  ADS  Google Scholar 

71.
Dhakan, D. B. et al. The unique composition of Indian gut microbiome, gene catalogue, and associated fecal metabolome deciphered using multi-omics approaches. Gigascience 8, giz004 (2019).
PubMed  PubMed Central  Article  CAS  Google Scholar 

72.
Qin, J. et al. A human gut microbial gene catalogue established by metagenomic sequencing. Nature 464, 59–65 (2010).
CAS  PubMed  PubMed Central  Article  Google Scholar 

73.
Yachida, S. et al. Metagenomic and metabolomic analyses reveal distinct stage-specific phenotypes of the gut microbiota in colorectal cancer. Nat. Med. 25, 968–976 (2019).
CAS  PubMed  Article  Google Scholar 

74.
Bolger, A. M., Lohse, M. & Usadel, B. Trimmomatic: A flexible trimmer for Illumina sequence data. Bioinformatics 30, 2114–2120 (2014).
CAS  PubMed  PubMed Central  Article  Google Scholar 

75.
Langmead, B. & Salzberg, S. Bowtie2. Nat. Methods 9, 357–359 (2013).
Article  CAS  Google Scholar 

76.
O’Leary, N. A. et al. Reference sequence (RefSeq) database at NCBI: Current status, taxonomic expansion, and functional annotation. Nucleic Acids Res. 44, D733–D745 (2016).
PubMed  Article  CAS  Google Scholar 

77.
Xia, L. C., Cram, J. A., Chen, T., Fuhrman, J. A. & Sun, F. Accurate genome relative abundance estimation based on shotgun metagenomic reads. PLoS ONE 6, e27992 (2011).
CAS  PubMed  PubMed Central  Article  ADS  Google Scholar 

78.
Parks, D. H., Imelfort, M., Skennerton, C. T., Hugenholtz, P. & Tyson, G. W. CheckM: Assessing the quality of microbial genomes recovered from isolates, single cells, and metagenomes. Genome Res. 25, 1043–1055 (2015).
CAS  PubMed  PubMed Central  Article  Google Scholar 

79.
Hyatt, D. et al. Prodigal: Prokaryotic gene recognition and translation initiation site identification. BMC Bioinform. 11, 119 (2010).
Article  CAS  Google Scholar 

80.
Jones, P. et al. InterProScan 5: Genome-scale protein function classification. Bioinformatics 30, 1236–1240 (2014).
CAS  PubMed  PubMed Central  Article  Google Scholar 

81.
Haft, D. H. TIGRFAMs: A protein family resource for the functional identification of proteins. Nucleic Acids Res. 29, 41–43 (2001).
CAS  PubMed  PubMed Central  Article  Google Scholar 

82.
Liao, Y., Smyth, G. K. & Shi, W. featureCounts: An efficient general purpose program for assigning sequence reads to genomic features. Bioinformatics 30, 923–930 (2014).
CAS  PubMed  PubMed Central  Article  Google Scholar 

83.
R Core Team. R: A Language and Environment for Statistical Computing (R Foundation for Statistical Computing, Vienna, 2017).
Google Scholar 

84.
Zhao, T., Liu, H., Roeder, K., Lafferty, J. & Wasserman, L. The huge package for high-dimensional undirected graph estimation in R. J. Mach. Learn. Res. 13, 6 (2016).
MathSciNet  MATH  Google Scholar 

85.
Liu, H., Roeder, K. & Wasserman, L. Stability approach to regularization selection (stars) for high dimensional graphical models. Advances in Neural Information Processing Systems (2010).

86.
Hagberg, A., Swart, P. & Chult, D. S. Exploring network structure, dynamics, and function using NetworkX. No. LA-UR-08-05495; LA-UR-08-5495 (Los Alamos National Lab. (LANL), Los Alamos, 2008).

87.
Newman, M. E. J. Networks: An Introduction 168–234 (Oxford University Press, Oxford, 2010).
Google Scholar 

88.
Newman, M. E. J. Modularity and community structure in networks. Proc. Natl. Acad. Sci. 103, 8577–8582 (2006).
CAS  PubMed  Article  ADS  PubMed Central  Google Scholar 

89.
Newman, M. E. J. Mixing patterns in networks. Phys. Rev. E 67, 26126 (2003).
MathSciNet  CAS  Article  ADS  Google Scholar 

90.
Fortunato, S. Community detection in graphs. Phys. Rep. 486, 75–174 (2010).
MathSciNet  Article  ADS  Google Scholar 

91.
Brandes, U. A faster algorithm for betweenness centrality*. J. Math. Sociol. 25, 163–177 (2001).
MATH  Article  Google Scholar  More

1.
Keller, R. P., Geist, J., Jeschke, J. M. & Kühn, I. Invasive species in Europe: ecology, status, and policy. Environ. Sci. Eur. 23, 1–17 (2011).
Article  Google Scholar 
2.
Parker, M., Thompson, J. N. & Weller, S. G. The population biology of invasive species. Annu. Rev. Ecol. Syst. 32, 305–332 (2001).
Article  Google Scholar 

3.
Allendorf, F. W. & Lundquist, L. L. Introduction: population biology, evolution, and control of invasive species. Conserv. Biol. 17, 24–30 (2003).
Article  Google Scholar 

4.
Crowl, T. A., Crist, T. O., Parmenter, R. R., Belovsky, G. & Lugo, A. E. The spread of invasive species and infectious disease as drivers of ecosystem change. Front. Ecol. Environ. 6, 238–246 (2008).
Article  Google Scholar 

5.
van der Veer, G. & Nentwig, W. Environmental and economic impact assessment of alien and invasive fish species in Europe using the generic impact scoring system. Ecol. Freshw. Fish 24, 646–656 (2015).
Article  Google Scholar 

6.
Clavero, M. & García-Berthou, E. Invasive species are a leading cause of animal extinctions. Trends Ecol. Evol. 20, 110 (2005).
PubMed  Article  PubMed Central  Google Scholar 

7.
Scalera, R. How much is Europe spending on invasive alien species?. Biol. Invasions 12, 173–177 (2010).
Article  Google Scholar 

8.
Sala, O. E. et al. Global biodiversity scenarios for the year 2100. Science 287, 1770–1774 (2000).
CAS  PubMed  Article  PubMed Central  Google Scholar 

9.
McLellan, R., Iyengar, L., Jeffries, B. & Oerlemans, N. Living Planet Report 2014: Species and Spaces, People and Places (WWF International, Gland, 2014).
Google Scholar 

10.
García-Berthou, E. et al. Introduction pathways and establishment rates of invasive aquatic species in Europe. Can. J. Fish. Aquat. Sci. 62, 453–463 (2005).
Article  Google Scholar 

11.
Karatayev, A. Y., Burlakova, L. E., Padilla, D. K., Mastitsky, S. E., & Olenin, S. Invaders are not a random selection of species. Biol. Invasions, 11, 2009. https://doi.org/10.1007/s10530-009-9498-0 (2009).
Article  Google Scholar 

12.
Verdonschot, R. C. M., Vos, J. H., & Verdonschot, P. F. M. Exotische macrofauna en macrofyten in de Nederlandse zoete wateren: voorkomen en beleid in 2012. (WOt-werkdocument 334) (Wettelijke Onderzoekstaken Natuur & Milieu, 2013).

13.
Holdich, D. M., Reynolds, J. D., Souty-Grosset, C. & Sibley, P. J. A review of the ever increasing threat to European crayfish from non-indigenous crayfish species. Knowl. Manag. Aquat. Ecosyst. 394–395, 11 (2009).
Article  Google Scholar 

14.
Chucholl, C. Invaders for sale: trade and determinants of introduction of ornamental freshwater crayfish. Biol. Invasions 15, 125–141 (2013).
Article  Google Scholar 

15.
Barbaresi, S. & Gherardi, F. The invasion of the alien crayfish Procambarus clarkii in Europe, with particular reference to Italy. Biol. Invasions 2, 259–264 (2000).
Article  Google Scholar 

16.
Gherardi, F. Crayfish invading Europe: the case study of Procambarus clarkii. Mar. Freshw. Behav. Physiol. 39, 175–191 (2006).
Article  Google Scholar 

17.
Kouba, A., Petrusek, A. & Kozák, P. Continental-wide distribution of crayfish species in Europe: update and maps. Knowl. Manag. Aquat. Ecosyst. 413, 5 (2014).
Article  Google Scholar 

18.
Lowe, S., Browne, M., Boudjelas, S., & De Poorter, M. 100 of the world’s worst invasive alien species: a selection from the global invasive species database in Aliens vol. 12 (Invasive Species Specialist Group, 2000).

19.
Padilla, D. K. & Williams, S. L. Beyond ballast water: aquarium and ornamental trades as sources of invasive species in aquatic ecosystems. Front. Ecol. Environ. 2, 131–138 (2004).
Article  Google Scholar 

20.
Faulkes, Z. The global trade in crayfish as pets. Crustacean Res. 44, 75–92 (2015).
Article  Google Scholar 

21.
Soes, D. M., & Koese, B. Invasive Crayfish in the Netherlands: A Preliminary Risk Analysis. (Bureau Waardenburg bv, Stichting EIS-Nederland, Invasive Alien Species Team, 2010).

22.
Chucholl, C. & Wendler, F. Positive selection of beautiful invaders: long-term persistence and bio-invasion risk of freshwater crayfish in the pet trade. Biol. Invasions 19, 197–208 (2017).
Article  Google Scholar 

23.
Zeng, Y., Chong, K. Y., Grey, E. K., Lodge, D. M. & Yeo, D. C. Disregarding human pre-introduction selection can confound invasive crayfish risk assessments. Biol. Invasions 17, 2373–2385 (2015).
Article  Google Scholar 

24.
Blackburn, T. M. et al. A proposed unified framework for biological invasions. Trends Ecol. Evol. 26, 333–339 (2011).
PubMed  Article  Google Scholar 

25.
Statzner, B., Bonada, N. & Dolédec, S. Biological attributes discriminating invasive from native European stream macroinvertebrates. Biol. Invasions 10, 517–530 (2008).
Article  Google Scholar 

26.
Whitney, K. D. & Gabler, C. A. Rapid evolution in introduced species, ‘invasive traits’ and recipient communities: challenges for predicting invasive potential. Divers. Distrib. 14, 569–580 (2008).
Article  Google Scholar 

27.
Kolar, C. S. & Lodge, D. M. Progress in invasion biology: predicting invaders. Trends Ecol. Evol. 16, 199–204 (2001).
PubMed  Article  Google Scholar 

28.
Marchetti, M. P., Moyle, P. B. & Levine, R. Invasive species profiling? Exploring the characteristics of non-native fishes across invasion stages in California. Freshw. Biol. 49, 646–661 (2004).
Article  Google Scholar 

29.
Grabowski, M., Bacela, K. & Konopacka, A. How to be an invasive gammarid (Amphipoda: Gammaroidea)-comparison of life history traits. Hydrobiologia 590, 75–84 (2007).
Article  Google Scholar 

30.
Thiébaut, G. Invasion success of non-indigenous aquatic and semi-aquatic plants in their native and introduced ranges. A comparison between their invasiveness in North America and in France. Biol. Invasions 9, 1–12 (2007).
Article  Google Scholar 

31.
Swart, C., Visser, V. & Robinson, T. B. Patterns and traits associated with invasions by predatory marine crabs. NeoBiota 39, 79 (2018).
Article  Google Scholar 

32.
Larson, E. R. & Olden, J. D. Latent extinction and invasion risk of crayfishes in the southeastern United States. Conserv. Biol. 24, 1099–1110 (2010).
PubMed  Article  PubMed Central  Google Scholar 

33.
Tricarico, E., Vilizzi, L., Gherardi, F. & Copp, G. H. Calibration of FI-ISK, an invasiveness screening tool for nonnative freshwater invertebrates. Risk Anal. Int. J. 30, 285–292 (2010).
Article  Google Scholar 

34.
Larson, E. R. & Olden, J. D. Using avatar species to model the potential distribution of emerging invaders. Glob Ecol. Biogeogr. 21, 1114–1125 (2012).
Article  Google Scholar 

35.
Veselý, L., Buřič, M. & Kouba, A. Hardy exotics species in temperate zone: can “warm water” crayfish invaders establish regardless of low temperatures?. Sci. Rep. 5, 16340 (2015).
ADS  PubMed  PubMed Central  Article  CAS  Google Scholar 

36.
Jaklič, M. & Vrezec, A. The first tropical alien crayfish species in European waters: the redclaw Cherax quadricarinatus (Von Martens, 1868) (Decapoda, Parastacidae). Crustaceana 84, 651–665 (2011).
Article  Google Scholar 

37.
Colautti, R. I., Grigorovich, I. A. & MacIsaac, H. J. Propagule pressure: a null model for biological invasions. Biol. Invasions 8, 1023–1037 (2006).
Article  Google Scholar 

38.
Marchetti, M. P., Moyle, P. B. & Levine, R. Alien fishes in California watersheds: characteristics of successful and failed invaders. Ecol. Appl. 14, 587–596 (2004).
Article  Google Scholar 

39.
Bennett, S. N., Olson, J. R., Kershner, J. L. & Corbett, P. Propagule pressure and stream characteristics influence introgression: cutthroat and rainbow trout in British Columbia. Ecol. Appl. 20, 263–277 (2010).
PubMed  Article  PubMed Central  Google Scholar 

40.
Cruz, M. J. & Rebelo, R. Colonization of freshwater habitats by an introduced crayfish, Procambarus clarkii Southwest Iberian Peninsula. Hydrobiologia 575, 191–201 (2007).
Article  Google Scholar 

41.
Lynas, J., Storey, A. W. & Knott, B. Aggressive interactions between three species of freshwater crayfish of the genus Cherax (Decapoda: Parastacidae). Mar. Freshw. Behav. Physiol. 40, 105–116 (2007).
Article  Google Scholar 

42.
Corey, S. Comparative fecundity of four species of crayfish in southwestern Ontario, Canada (Decapoda, Astacidea). Crustaceana 52(3), 276–286 (1987).
Article  Google Scholar 

43.
Somers, K. M. Characterizing size-specific fecundity in crustaceans. Crustacean Egg Prod. 7, 357–378 (1991).
Google Scholar 

44.
Maguire, I., Klobučar, G. I. V. & Erben, R. The relationship between female size and egg size in the freshwater crayfish Austropotamobius torrentium. Bulletin Français de la Pêche et de la Pisciculture 376–377, 777–785 (2005).
Article  Google Scholar 

45.
Pilotto, F. et al. The invasive crayfish Faxonius limosus in Lake Varese: estimating abundance and population size structure in the context of habitat and methodological constraints. J. Crustacean Biol. 28, 633–640 (2008).
Article  Google Scholar 

46.
Hobbs Jr, H. H. A checklist of the North and Middle American crayfishes (Decapoda: Astacidae and Cambaridae). Smithsonian Contrib. Zool. 166, 1–161 (1974).
Google Scholar 

47.
Mrugała, A. et al. Trade of ornamental crayfish in Europe as a possible introduction pathway for important crustacean diseases: crayfish plague and white spot syndrome. Biol. Invasions 17, 1313–1326 (2015).
Article  Google Scholar 

48.
Svoboda, J., Mrugała, A., Kozubíková-Balcarová, E. & Petrusek, A. Hosts and transmission of the crayfish plague pathogen Aphanomyces astaci: a review. J. Fish Dis. 40, 127–140 (2017).
CAS  PubMed  Article  PubMed Central  Google Scholar 

49.
Grandjean, F. et al. Status of Pacifastacus leniusculus and its role in recent crayfish plague outbreaks in France: improving distribution and crayfish plague infection patterns. Aquat. Invasions, 12, 541–549 (2017).
Article  Google Scholar 

50.
Crandall, K. A. & De Grave, S. An updated classification of the freshwater crayfishes (Decapoda: Astacidea) of the world, with a complete species list. J. Crustacean Biol. 37, 615–653 (2017).
Article  Google Scholar 

51.
Freshwater Crayfish: A Global Overview. (ed. Kawai, T., Faulkes, Z., & Scholtz, G.) (CRC Press, Boca Raton, 2015).

52.
Buřič, M., Kouba, A. & Kozak, P. Reproductive plasticity in freshwater invader: from long-term sperm storage to parthenogenesis. PLoS ONE 8, e77597. https://doi.org/10.1371/journal.pone.0077597 (2013).
ADS  CAS  Article  PubMed  PubMed Central  Google Scholar 

53.
Kaldre, K., Meženin, A., Paaver, T., & Kawai, T. A preliminary study on the tolerance of marble crayfish Procambarus fallax f. virginalis to low temperature in Nordic climate in Freshwater crayfish: global overview, 54–62 (2016).

54.
Vogt, G. Marmorkrebs: natural crayfish clone as emerging model for various biological disciplines. J. Biosci. 36, 377–382 (2011).
PubMed  Article  Google Scholar 

55.
Chucholl, C. Predicting the risk of introduction and establishment of an exotic aquarium animal in Europe: insights from one decade of Marmorkrebs (Crustacea, Astacida, Cambaridae) releases. Biol. Invasions 5, 309–318 (2014).
Article  Google Scholar 

56.
Chucholl, C., Morawetz, K. & Groß, H. The clones are coming–strong increase in Marmorkrebs [Procambarus fallax (Hagen, 1870) f. virginalis] records from Europe. Aquat. Invasions 7, 511–519 (2012).
Article  Google Scholar 

57.
Soes, D. M. & van Eekelen, R. Rivierkreeften, een oprukkend probleem?. De Levende Natuur 107, 56–59 (2006).
Google Scholar 

58.
Mauvisseau, Q., Tönges, S., Andriantsoa, R., Lyko, F. & Sweet, M. Early detection of an emerging invasive species: eDNA monitoring of a parthenogenetic crayfish in freshwater systems. Manag. Biol. Invasions 10, 461 (2019).
Article  Google Scholar 

59.
Strand, D. A. et al. Monitoring a Norwegian freshwater crayfish tragedy: eDNA snapshots of invasion, infection and extinction. J. Appl. Ecol. 56, 1661–1673 (2019).
CAS  Article  Google Scholar 

60.
Beentjes, K. K., Speksnijder, A. G., Schilthuizen, M., Schaub, B. E. & van der Hoorn, B. B. The influence of macroinvertebrate abundance on the assessment of freshwater quality in The Netherlands. Metabarcoding Metagenom. 2, e26744 (2018).
Article  Google Scholar 

61.
Melo-Merino, S. M., Reyes-Bonilla, H. & Lira-Noriega, A. Ecological niche models and species distribution models in marine environments: a literature review and spatial analysis of evidence. Ecol. Model. 415, 108837 (2020).
Article  Google Scholar 

62.
Zhang, Z. et al. Impacts of climate change on the global potential distribution of two notorious invasive crayfishes. Freshw. Biol. 65, 353–365 (2020).
Article  Google Scholar 

63.
Capinha, C., Leung, B. & Anastácio, P. Predicting worldwide invasiveness for four major problematic decapods: an evaluation of using different calibration sets. Ecography 34, 448–459 (2011).
Article  Google Scholar 

64.
Havel, J. E., Kovalenko, K. E., Thomaz, S. M., Amalfitano, S. & Kats, L. B. Aquatic invasive species: challenges for the future. Hydrobiologia 750, 147–170 (2015).
PubMed  PubMed Central  Article  Google Scholar 

65.
Früh, D., Stoll, S. & Haase, P. Physicochemical and morphological degradation of stream and river habitats increases invasion risk. Biol. Invasions 14, 2243–2253 (2012).
Article  Google Scholar 

66.
Ghalambor, C. K., McKay, J. K., Carroll, S. P. & Reznick, D. N. Adaptive versus non-adaptive phenotypic plasticity and the potential for contemporary adaptation in new environments. Funct. Ecol. 21, 394–407 (2007).
Article  Google Scholar 

67.
Scalici, M. et al. The new threat to Italian inland waters from the alien crayfish “gang”: the Australian Cherax destructor Clark, 1936. Hydrobiologia 632, 341–345 (2009).
Article  Google Scholar 

68.
Koese, B. & Evers, C. H. M. A National Inventory of Invasive Freshwater Crayfish in the Netherlands in 2010 (EIS, Stichting European Invertebrate Survey Nederland, 2011).
Google Scholar 

69.
Clement, J., & van Puijenbroek, P. Basiskaart Aquatisch: de Watertypenkaart Het oppervlaktewater in de TOP10NL geclassificeerd naar watertype (No. 500067004). (Planbureau voor de Leefomgeving 2010).

70.
Peel, M. C., Finlayson, B. L. & McMahon, T. A. Updated world map of the Köppen-Geiger climate classification. Hydrol. Earth Syst. Sci. Discuss. 4, 439–473 (2007).
ADS  Google Scholar 

71.
Lyko, F. The marbled crayfish (Decapoda: Cambaridae) represents an independent new species. Zootaxa 4363(4), 544–552 (2017).
PubMed  Article  PubMed Central  Google Scholar 

72.
Usseglio-Polatera, P. & Tachet, H. Theoretical habitat templets, species traits, and species richness: Plecoptera and Ephemeroptera in the Upper Rhône River and its floodplain. Freshw. Biol. 31, 357–375 (1994).
Article  Google Scholar 

73.
Poff, N. L. et al. Functional trait niches of North American lotic insects: traits-based ecological applications in light of phylogenetic relationships. J. North Am. Benthological. Soc. 25, 730–755 (2006).
Article  Google Scholar 

74.
Wyse, S. V. et al. A quantitative assessment of shoot flammability for 60 tree and shrub species supports rankings based on expert opinion. Int. J. Wildland Fire 25, 466–477 (2016).
Article  Google Scholar 

75.
Hill, M. O. TWINSPAN. A FORTRAN program for arranging multivariate data in an ordered two-way table by classification of the individuals and attributes. (Ecology and Systematics, Cornell University, 1979).

76.
Hu, G. et al. Regeneration of different plant functional types in a Masson pine forest following pine wilt disease. PLoS ONE 7, e36432. https://doi.org/10.1371/journal.pone.0036432 (2012).
ADS  CAS  Article  PubMed  PubMed Central  Google Scholar 

77.
Agir, S. U., Kutbay, H. G. & Surmen, B. Plant diversity along coastal dunes of the Black Sea (North of Turkey). Rendiconti Lincei 27, 443–453 (2016).
Article  Google Scholar 

78.
Andrej, P. & Andraž, Č. Functional response traits and plant community strategy indicate the stage of secondary succession. Hacquetia 11, 209–225 (2012).
Article  Google Scholar 

79.
Hill, M.O. & Šmilauer, P. TWINSPAN for Windows version 2.3. (Centre for Ecology and Hydrology & University of South Bohemia, Huntingdon & Ceske Budejovice, 2005).

80.
Roleček, J., Tichý, L., Zelený, D. & Chytrý, M. Modified TWINSPAN classification in which the hierarchy respects cluster heterogeneity. J. Veg. Sci. 20, 596–602 (2009).
Article  Google Scholar  More