Ecology

1.
Forster, P. et al. Changes in atmospheric constituents and in radiative forcing. Chapter 2. In Climate Change 2007. The Physical Science Basis. (Cambridge University Press, 2007).
2.
World Meteorological Organization. The State of Greenhouse Gases in the Atmosphere Based on Global Observation through 2017. WMO Greenhouse Gas Bulletin No. 14. https://library.wmo.int/doc_num.php?explnum_id=5455 (2018).

3.
Anderson, B., et al. Methane and nitrous oxide emissions from natural sources, Office of Atmospheric Programs, US EPA. EPA 430-R-10-001, Washington DC (2010).

4.
Bremner, J. M. Sources of nitrous oxide in soils. Nutr. Cycl. Agroecosyst. 49, 7–16 (1997).
CAS  Article  Google Scholar 

5.
Brentrup, F., Küsters, J., Lammel, J. & Kuhlmann, H. Methods to estimate on-field nitrogen emissions from crop production as an input to LCA studies in the agricultural sector. Int. J. Life Cycle Assess. 5, 349 (2000).
CAS  Article  Google Scholar 

6.
Snyder, C. S., Bruulsema, T. W., Jensen, T. L. & Fixen, P. E. Review of greenhouse gas emissions from crop production systems and fertilizer management effects. Agric. Ecosyst. Environ. 133, 247–266 (2009).
CAS  Article  Google Scholar 

7.
Enanga, E. M., Creed, I. F., Casson, N. J. & Beall, F. D. Summer storms trigger soil N2O efflux episodes in forested catchments. J. Geophys. Res. Biogeo. 121, 95–108 (2016).
CAS  Article  Google Scholar 

8.
Stewart, D. J., Taylor, C. M., Reeves, C. E. & Mcquaid, J. B. Biogenic nitrogen oxide emissions from soils: Impact on NOx and ozone over West Africa during AMMA (African Monsoon Multidisciplinary Analysis): Observational study. Atmos. Chem. Phys. Eur. Geosci. Union 8, 2285–2297 (2008).
ADS  CAS  Article  Google Scholar 

9.
Kralova, M., Masscheleyn, P. H., Lindau, C. W. & Patrick, W. H. Jr. Production of dinitrogen and nitrous oxide in soil suspensions as affected by redox potential. Water Air Soil Poll. 61, 37–45 (1992).
ADS  CAS  Article  Google Scholar 

10.
Davidsson, T. E. & Ståhl, M. The influence of organic carbon on nitrogen transformations in five wetland soils. Soil Sci. Soc. Am. J. 64, 1129–1136 (2000).
ADS  CAS  Article  Google Scholar 

11.
Groffman, P. M. et al. Challenges to incorporating spatially and temporally explicit phenomena (hotspots and hot moments) in denitrification models. Biogeochemistry 93, 49–77 (2009).
CAS  Article  Google Scholar 

12.
Vidon, P. et al. Hot spots and hot moments in riparian zones: Potential for improved water quality management1. J Am. Water Resour. As. 46, 278–298 (2010).
CAS  Article  Google Scholar 

13.
Vidon, P., Jacinthe, P.-A., Liu, X., Fisher, K. & Baker, M. Hydrobiogeochemical controls on riparian nutrient and greenhouse gas dynamics: 10 years post-restoration. J Am. Water Resour. As. 50, 639–652 (2014).
CAS  Article  Google Scholar 

14.
Liu, G., Li, Y., Migliaccio, K., Olczyk, T. & Alva, A. Oxygen amendment on growth and nitrogen use efficiency of flooded Italian basil. Int. J. Veg. Sci. 19, 217–227 (2013).
Article  Google Scholar 

15.
Liu, G., Li, Y. & Fu, X. (SL206) Practices to minimize flooding damage to commercial vegetable production. https://edis.ifas.ufl.edu/ss425 (2019).

16.
Li, C., Frolking, S. & Frolking, T. A. A model of nitrous oxide evolution from soil driven by rainfall events: 1. Model structure and sensitivity. J. Geophys. Res. Atmos. 97, 9759–9776 (1992).
ADS  CAS  Article  Google Scholar 

17.
Roque-Malo, S. & Kumar, P. Patterns of change in high frequency precipitation variability over North America. Sci. Rep. 7, 10853. https://doi.org/10.1038/s41598-017-10827-8 (2017).
ADS  CAS  Article  PubMed  PubMed Central  Google Scholar 

18.
Corradi, R., M., Lambert, F., Ramirez-Villegas, J. & Challinor, A. Climate change affects rainfall patterns in crop-producing regions: Findings from the study “Emergence of robust precipitation changes across crop production areas in the 21st century”. In CCAFS Info Note. Wageningen, Netherlands: CGIAR Research Program on Climate Change, Agriculture and Food Security (CCAFS) (2019).

19.
Liu, G., et al. 2020–2021 Vegetable Production Handbook: Chapter 2. Fertilizer Management for Vegetable Production in Florida. https://edis.ifas.ufl.edu/cv296 (2020).

20.
Lehmann, J., Gaunt, J. & Rondon, M. Bio-char Sequestration in Terrestrial Ecosystems – A Review. Mitig. Adapt. Strat. GL. 11, 403–427 (2006).
Article  Google Scholar 

21.
Lehmann, J. & Joseph, S. Biochar for environmental management: An introduction. In Biochar for Environmental Management: Science, Technology, and Implementation (ed. Lehmann, J. & Joseph, S.) 33–46. (Routledge, 2015).

22.
Bera, T., Collins, H. P., Alva, A. K., Purakayastha, T. J. & Patra, A. K. Biochar and manure effluent effects on soil biochemical properties under corn production. Appl. Soil Ecol. 107, 360–367 (2016).
Article  Google Scholar 

23.
Bera, T. et al. Influence of select bioenergy by-products on soil carbon and microbial activity: A laboratory study. Sci. Total Environ. 653, 1354–1363 (2019).
ADS  CAS  Article  Google Scholar 

24.
Purakayastha, T. J. et al. A review on biochar modulated soil condition improvements and nutrient dynamics concerning crop yields: Pathways to climate change mitigation and global food security. Chemosphere 227, 345–365 (2019).
ADS  CAS  Article  Google Scholar 

25.
Zimmerman, A. R. Abiotic and microbial oxidation of laboratory-produced black carbon (biochar). Environ. Sci. Technol. 44, 1295–1301 (2010).
ADS  CAS  Article  Google Scholar 

26.
Woolf, D., Amonette, J., Street-Perrott, F., Lehmann, J. & Joseph, S. Sustainable biochar to mitigate global climate change. Nat. Commun. 1, 56 (2010).
ADS  Article  Google Scholar 

27.
Mukherjee, A., Lal, R. & Zimmerman, A. R. Effects of biochar and other amendments on the physical properties and greenhouse gas emissions of an artificially degraded soil. Sci. Total Environ. 487, 26–36 (2014).
ADS  CAS  Article  Google Scholar 

28.
Lan, Z. M., Chen, C. R., Rashti, R. M., Yang, H. & Zhang, D. K. Stoichiometric ratio of dissolved organic carbon to nitrate regulates nitrous oxide emission from the biochar-amended soils. Soil Sci. Plant Nutr. 576, 559–571 (2017).
CAS  Google Scholar 

29.
Yanai, Y., Toyota, K. & Okazaki, M. Effects of charcoal addition on N2O emissions from soil resulting from rewetting air-dried soil in short-term laboratory experiments. Soil Sci. Plant Nutr. 53, 181–188 (2007).
CAS  Article  Google Scholar 

30.
Clough, T. J. et al. Unweathered wood biochar impact on nitrous oxide emissions from a bovine-urine-amended pasture soil. Soil Sci. Soc. Am. J. 74, 852–860 (2010).
ADS  CAS  Article  Google Scholar 

31.
Singh, B. P. et al. Influence of biochars on nitrous oxide emission and nitrogen leaching from two contrasting soils. J. Environ. Qual. 39, 1224–1235 (2010).
CAS  Article  Google Scholar 

32.
Cayuela, M. L. et al. Biochar’s role in mitigating soil nitrous oxide emissions: A review and meta-analysis. Agric. Ecosyst. Environ. 191, 5–16 (2014).
CAS  Article  Google Scholar 

33.
Cayuela, M. L. et al. Biochar and denitrification in soils: When, how much and why does biochar reduce N2O emissions?. Sci. Rep. 3, 1732 (2013).
Article  Google Scholar 

34.
Liu, G. & Porterfield, D. M. Oxygen enrichment with magnesium peroxide for minimizing hypoxic stress of flooded corn. J. Plant Nutr. Soil Sci. 177, 733–740 (2014).
CAS  Article  Google Scholar 

35.
Brady, N. C. & Weil, R. R. The Nature and Properties of Soils 1–187 (Prentice-Hall Inc., Upper Saddle River, 1999).
Google Scholar 

36.
Weier, K. L., Doran, J. W., Power, J. F. & Walters, D. T. Denitrification and the dinitrogen/nitrous oxide ratio as affected by soil water, available carbon, and nitrate. Soil Sci. Soc. Am. J. 57, 66–72 (1993).
ADS  CAS  Article  Google Scholar 

37.
Ameloot, N. et al. Short-term CO2 and N2O emissions and microbial properties of biochar amended sandy loam soils. Soil Biol. Biochem. 57, 401–410 (2013).
CAS  Article  Google Scholar 

38.
Spokas, K. A. et al. Qualitative analysis of volatile organic compounds on biochar. Chemosphere 85, 869–882 (2011).
ADS  CAS  Article  Google Scholar 

39.
Chendrayan, K., Adhya, T. K. & Sethunathan, N. Dehydrogenase and invertase activities of flooded soils. Soil Biol. Biochem. 12, 271–273 (1980).
CAS  Article  Google Scholar 

40.
Macé, O. G., Steinauer, K., Jousset, A., Eisenhauer, N. & Scheu, S. Flood-induced changes in soil microbial functions as modified by plant diversity. PLoS ONE 11(11), e0166349. https://doi.org/10.1371/journal.pone.0166349 (2016).
CAS  Article  Google Scholar 

41.
van Zwieten, L. et al. Influence of biochars on flux of N2O and CO2 from Ferrosol. Aust. J. Soil Res. 48, 555–568 (2010).
Article  Google Scholar 

42.
Zheng, J., Stewart, C. E. & Cotrufo, F. M. Biochar and nitrogen fertilizer alters soil nitrogen dynamics and greenhouse gas fluxes from two temperate soils. J. Environ. Qual. 41, 1361–1370 (2012).
CAS  Article  Google Scholar 

43.
Lu, S., Zhang, X. & Xue, Y. Application of calcium peroxide in water and soil treatment: A review. J. Hazard. Mater. 337, 163–177 (2017).
ADS  CAS  Article  Google Scholar 

44.
Reyes-Cabrera, J. et al. Amending marginal sandy soils with biochar and lignocellulosic fermentation residual sustains fertility in elephantgrass bioenergy cropping systems. Nutr. Cycl. Agroecosyst. 115, 69–83 (2019).
CAS  Article  Google Scholar 

45.
Zobeck, T. M. et al. Soil property effects on wind erosion of organic soils. Aeolian Res. 10, 43–51 (2013).
ADS  Article  Google Scholar 

46.
Collins, M. E. Key to soil orders in Florida. University of Florida Cooperative Extension Service, Institute of Food and Agriculture Sciences, EDIS (2009).

47.
Bera, T., Purakayastha, T. J., Patra, A. K. & Datta, S. C. Comparative analysis of physicochemical, nutrient, and spectral properties of agricultural residue biochars as influenced by pyrolysis temperatures. J. Mater. Cycles Waste 20, 1115–1127 (2018).
CAS  Article  Google Scholar 

48.
Vance, E. D., Brookes, P. C. & Jenkinson, D. S. Microbial biomass measurements in forest soils: The use of the chloroform fumigation-incubation method in strongly acid soils. Soil Biol. Biochem. 19, 697–702 (1987).
CAS  Article  Google Scholar 

49.
Butnan, S., Deenik, J. L., Toomsan, B. M., Antal, J. & Vityakon, P. Biochar properties influencing greenhouse gas emissions in tropical soils differing in texture and mineralogy. J. Environ. Qual. 45, 1509–1519 (2016).
CAS  Article  Google Scholar 

50.
SAS Institute Inc. Base SAS 9.4 Procedures Guide, 5th ed. SAS Institute Inc., Cary (2015).
Google Scholar  More

1.
Carlton, J. T. Pattern, process, and prediction in marine invasion ecology. Biol. Conserv. 78, 97–106. https://doi.org/10.1016/0006-3207(96)00020-1 (1996).
Article  Google Scholar 
2.
Stepien, C. A., Brown, J. E., Neilson, M. E. & Tumeo, M. A. Genetic diversity of invasive species in the Great Lakes versus their Eurasian source populations: insights for risk analysis. Risk Anal. 25, 1043–1060. https://doi.org/10.1111/j.1539-6924.2005.00655.x (2005).
Article  PubMed  Google Scholar 

3.
Geller, J. B., Darling, J. A. & Carlton, J. T. Genetic perspectives on marine biological invasions. Annu. Rev. Mar. Sci. 2, 367–393. https://doi.org/10.1146/annurev.marine.010908.163745 (2010).
ADS  Article  Google Scholar 

4.
Estoup, A. & Guillemaud, T. Reconstructing routes of invasion using genetic data: why, how and so what?. Mol. Ecol. 19, 4113–4130. https://doi.org/10.1111/j.1365-294X.2010.04773.x (2010).
Article  PubMed  Google Scholar 

5.
Hudson, J., Viard, F., Roby, C. & Rius, M. Anthropogenic transport of species across native ranges: unpredictable genetic and evolutionary consequences. Biol. Lett. https://doi.org/10.1098/rsbl.2016.0620 (2016).
Article  PubMed  PubMed Central  Google Scholar 

6.
Carlton, J. T. Biological invasions and cryptogenic species. Ecology 77, 1653–1655. https://doi.org/10.2307/2265767 (1996).
Article  Google Scholar 

7.
Holland, B. S. Genetics of marine bioinvasions. Hydrobiologia 420, 63–71. https://doi.org/10.1023/a:1003929519809 (2000).
CAS  Article  Google Scholar 

8.
Reitzel, A. M., Herrera, S., Layden, M. J., Martindale, M. Q. & Shank, T. M. Going where traditional markers have not gone before: utility of and promise for RAD sequencing in marine invertebrate phylogeography and population genomics. Mol. Ecol. 22, 2953–2970. https://doi.org/10.1111/mec.12228 (2013).
CAS  Article  PubMed  PubMed Central  Google Scholar 

9.
Darling, J. A. et al. Recommendations for developing and applying genetic tools to assess and manage biological invasions in marine ecosystems. Mar. Pol. 85, 54–64. https://doi.org/10.1016/j.marpol.2017.08.014 (2017).
Article  Google Scholar 

10.
Palumbi, S. R. Genetic-divergence, reproductive isolation, and marine speciation. Annu. Rev. Ecol. Syst. 25, 547–572. https://doi.org/10.1146/annurev.ecolsys.25.1.547 (1994).
Article  Google Scholar 

11.
Kelly, R. P. & Palumbi, S. R. Genetic Structure among 50 species of the northeastern Pacific rocky intertidal community. PLoS ONE 5, 13. https://doi.org/10.1371/journal.pone.0008594 (2010).
CAS  Article  Google Scholar 

12.
Boissin, E., Stohr, S. & Chenuil, A. Did vicariance and adaptation drive cryptic speciation and evolution of brooding in Ophioderma longicauda (Echinodermata: Ophiuroidea), a common Atlanto-Mediterranean ophiuroid?. Mol. Ecol. 20, 4737–4755. https://doi.org/10.1111/j.1365-294X.2011.05309.x (2011).
CAS  Article  PubMed  Google Scholar 

13.
Selkoe, K. A. & Toonen, R. J. Marine connectivity: a new look at pelagic larval duration and genetic metrics of dispersal. Mar. Ecol. Prog. Ser. 436, 291–305. https://doi.org/10.3354/meps09238 (2011).
ADS  Article  Google Scholar 

14.
Stewart, J. R. & Lister, A. M. Cryptic northern refugia and the origins of the modern biota. Trends Ecol. Evol. 16, 608–613. https://doi.org/10.1016/s0169-5347(01)02338-2 (2001).
Article  Google Scholar 

15.
Hewitt, G. M. Genetic consequences of climatic oscillations in the quaternary. Philos. Trans. R. Soc. Lond. Ser. B Biol. Sci. 359, 183–195. https://doi.org/10.1098/rstb.2003.1388 (2004).
CAS  Article  Google Scholar 

16.
Provan, J. & Bennett, K. D. Phylogeographic insights into cryptic glacial refugia. Trends Ecol. Evol. 23, 564–571. https://doi.org/10.1016/j.tree.2008.06.010 (2008).
Article  PubMed  Google Scholar 

17.
Carlton, J. T. & Geller, J. B. Ecological roulette—the global transport of nonindigenous marine organisms. Science 261, 78–82. https://doi.org/10.1126/science.261.5117.78 (1993).
ADS  Article  Google Scholar 

18.
Ruiz, G. M., Fofonoff, P. W., Carlton, J. T., Wonham, M. J. & Hines, A. H. Invasion of coastal marine communities in North America: apparent patterns, processes, and biases. Annu. Rev. Ecol. Syst. 31, 481–531. https://doi.org/10.1146/annurev.ecolsys.31.1.481 (2000).
Article  Google Scholar 

19.
Molnar, J. L., Gamboa, R. L., Revenga, C. & Spalding, M. D. Assessing the global threat of invasive species to marine biodiversity. Front. Ecol. Environ. 6, 485–492. https://doi.org/10.1890/070064 (2008).
Article  Google Scholar 

20.
Morton, J. The habits of Cyclope neritea, a style-bearing stenoglossan gastropod. Proc. Malacol. Soc. Lond. 34, 96–105 (1960).
Google Scholar 

21.
Gomoiu, M. T. Biologisches Studium der Arten Nassa reticulata L. und Cyclonassa neritea (L.) im Schwarzen Meer (rumänischer Küstenbereich). Rev. Roum. Biol. Ser. Zool. 9, 39–49 (1964).
Google Scholar 

22.
Galindo, L. A., Puillandre, N., Utge, J., Lozouet, P. & Bouchet, P. The phylogeny and systematics of the Nassariidae revisited (Gastropoda, Buccinoidea). Mol. Phylogenet. Evol. 99, 337–353. https://doi.org/10.1016/j.ympev.2016.03.019 (2016).
Article  PubMed  Google Scholar 

23.
Poppe, G. & Goto, Y. European Seashells Vol. 1 (Vera Christa Hemmen, Germany, 1991).
Google Scholar 

24.
Gofas, S., Moreno, D. & Salas, C. Moluscos Marinos de Andalucía. (Servicio de Publicaciones e Intercambio Científico, Universidad de Málaga., 2011).

25.
WoRMS. http://www.marinespecies.org/aphia.php?p=taxdetails&id=246140, accessed 28 January 2019 (2019).

26.
Pérès, J. M. & Picard, J. Nouveau manuel de bionomie benthique. Recl. Trav. Stn. Mar. Endoume 31, 5–137 (1964).
Google Scholar 

27.
Mars, P. Recherches sur quelques étangs du littoral méditerranéen français et leurs faunes malacologiques. Vie et milieu supp. 20, 359 (1966).
Google Scholar 

28.
Zaouali, J. Influence des facteurs thermiques et halins sur la faune malacologique de quelques lagunes tunisiennes (lac lchkeul, lac de Bizerte, lac de Tunis, mer de Bou Grara. Rapp. Comm. Int. Mer Medit. 23, 99–101 (1975).
Google Scholar 

29.
UNEP/MAP-RAC/SPA. Handbook for Interpreting Types of Marine Habitat for the Selection of Sites to be Included in the National Inventories of Natural Sites of Conservation Interest (Bellan-Santini D, Bellan G, Ghazi Bitar G, Harmelin J-G, Pergent ) 217 (2007).

30.
Russo, P. Lagoon malacofauna: results of malacological research in the Venice Lagoon. Boll. Malacol. 53, 49–62 (2017).
Google Scholar 

31.
Nobre, A. Moluscos Marinhos de Portugal (Imprensa Portuguesa, Porto, 1931).
Google Scholar 

32.
Grossu, A. V. Gastropoda Prosobranchia şi Opisthobranchia. Fauna Republicii Populare Române. Mollusca, Bucureşti, 3, fasc. 2, p 220. (1956).

33.
Parenzan, P. Carta d’identità delle conchiglie del Mediterraneo. Volume Primo. Gasteropodi. Bios Taras, Taranto, 283 (1970).

34.
Sauriau, P. G. Spread of cyclope-neritea (mollusca, gastropoda) along the north-eastern Atlantic coasts in relation to oyster culture and to climatic fluctuations. Mar. Biol. 109, 299–309. https://doi.org/10.1007/bf01319398 (1991).
Article  Google Scholar 

35.
Anistratenko, V., Khaliman, I. & Anistratenko, O. The Molluscs of the Sea of Azov, Naukova Dumka, p 186. ISBN: 978-966-00-1112-0. (2011).

36.
Revkov, N. et al. in BSC, State of the Environment of the Black Sea (20012006/7) 243–290. (Black Sea Commission Publications 2008-3, 2008).

37.
Gili, C. & Martinell, J. Phylogeny, speciation and species turnover. The case of the Mediterranean gastropods of genus Cyclope Risso, 1826. Lethaia 33, 236–250. https://doi.org/10.1080/00241160025100080 (2000).
Article  Google Scholar 

38.
Sabelli, B. & Taviani, M. In The Mediterranean Sea: Its History and Present Challenges (eds Goffredo, S. & Dubinsky, Z.) 285–306 (Springer, Dordrecht, 2014).
Google Scholar 

39.
Borsa, P. et al. Infraspecific zoogeography of the Mediterranean: population genetic analysis on sixteen atlanto-mediterranean species (fishes and invertebrates). Vie Milieu 47, 295–305 (1997).
Google Scholar 

40.
Bremer, J. R. A., Vinas, J., Mejuto, J., Ely, B. & Pla, C. Comparative phylogeography of Atlantic bluefin tuna and swordfish: the combined effects of vicariance, secondary contact, introgression, and population expansion on the regional phylogenies of two highly migratory pelagic fishes. Mol. Phylogenet. Evol. 36, 169–187. https://doi.org/10.1016/j.ympev.2004.12.011 (2005).
CAS  Article  Google Scholar 

41.
Patarnello, T., Volckaert, F. & Castilho, R. Pillars of Hercules: is the Atlantic-Mediterranean transition a phylogeographical break?. Mol. Ecol. 16, 4426–4444. https://doi.org/10.1111/j.1365-294X.2007.03477.x (2007).
Article  PubMed  Google Scholar 

42.
Maggs, C. A. et al. Evaluating signatures of glacial refugia for north Atlantic benthic marine taxa. Ecology 89, S108–S122. https://doi.org/10.1890/08-0257.1 (2008).
Article  PubMed  Google Scholar 

43.
Rolán, E. D. Especies más de moluscos mediterráneos introducidos en la bahía de O Grove. Thalassas 10, 135 (1992).
ADS  Google Scholar 

44.
Bachelet, G., Cazaux, C., Gantès, H. & Labourg, P. Contribution à l’étude de la faune marine de la région d’Arcachon. Bull. Cent. Etudes Rech. Sci. Biarritz IX, 45–64 (1980).
Google Scholar 

45.
Bachelet, G. et al. Invasion of the eastern Bay of Biscay by the nassariid gastropod Cyclope neritea: origin and effects on resident fauna. Mar. Ecol. Prog. Ser. 276, 147–159. https://doi.org/10.3354/meps276147 (2004).
ADS  Article  Google Scholar 

46.
Simon-Bouhet, B., Garcia-Meunier, P. & Viard, F. Multiple introductions promote range expansion of the mollusc Cyclope neritea (Nassariidae) in France: evidence from mitochondrial sequence data. Mol. Ecol. 15, 1699–1711. https://doi.org/10.1111/j.1365-294X.2006.02881.x (2006).
CAS  Article  PubMed  Google Scholar 

47.
Couceiro, L., Miguez, A., Ruiz, J. M. & Barreiro, R. Introduced status of Cyclope neritea (Gastropoda, Nassariidae) in the NW Iberian Peninsula confirmed by mitochondrial sequence data. Mar. Ecol. Prog. Ser. 354, 141–146. https://doi.org/10.3354/meps07257 (2008).
ADS  CAS  Article  Google Scholar 

48.
Simon-Bouhet, B., Daguin, C., Garcia-Meunier, P. & Viard, F. Polymorphic microsatellites for the study of newly established populations of the gastropod Cyclope neritea. Mol. Ecol. Notes 5, 121–123. https://doi.org/10.1111/j.1471-8286.2005.00857.x (2005).
CAS  Article  Google Scholar 

49.
Aissaoui, C., Galindo, L. A., Puillandre, N. & Bouchet, P. The nassariids from the Gulf of Gabes revisited (Neogastropoda, Nassariidae). Mar. Biol. Res. 13, 370–389. https://doi.org/10.1080/17451000.2016.1273528 (2017).
Article  Google Scholar 

50.
Knowlton, N. & Jackson, J. Inbreeding and outbreeding in marine invertebrates. In The Natural History of Inbreeding and Outbreeding: Theoretical and Empirical Perspectives (ed. Thornhill, N. W.) 200–249 (University of Chicago Press, Chicago, 1993).
Google Scholar 

51.
Cahill, A. E. & Levinton, J. S. Genetic differentiation and reduced genetic diversity at the northern range edge of two species with different dispersal modes. Mol. Ecol. 25, 515–526. https://doi.org/10.1111/mec.13497 (2016).
Article  PubMed  Google Scholar 

52.
Cahill, A. E. & Viard, F. Genetic structure in native and non-native populations of the direct-developing gastropod Crepidula convexa. Mar. Biol. 161, 2433–2443. https://doi.org/10.1007/s00227-014-2519-2 (2014).
Article  Google Scholar 

53.
Boissin, E. et al. Contemporary genetic structure and postglacial demographic history of the black scorpionfish, Scorpaena porcus, in the Mediterranean and the Black Seas. Mol. Ecol. 25, 2195–2209. https://doi.org/10.1111/mec.13616 (2016).
CAS  Article  PubMed  Google Scholar 

54.
Simon-Bouhet, B. Expansion d’aire et processus d’introductions biologiques en milieu marin: le cas de Cyclope neritea (Nassariidae) sur les côtes françaises. Thèse de Doctorat, Université de La Rochelle, France, p. 248 (2006).

55.
Couceiro, L., Lopez, L., Ruiz, J. M. & Barreiro, R. Population structure and range expansion: the case of the invasive gastropod Cyclope neritea in northwest Iberian Peninsula. Integr. Zool. 7, 286–298. https://doi.org/10.1111/j.1749-4877.2012.00305.x (2012).
Article  PubMed  Google Scholar 

56.
Spalding, M. D. et al. Marine ecoregions of the world: a bioregionalization of coastal and shelf areas. Bioscience 57, 573–583. https://doi.org/10.1641/b570707 (2007).
Article  Google Scholar 

57.
Boissin, E., Hoareau, T. B. & Berrebi, P. Effects of current and historic habitat fragmentation on the genetic structure of the sand goby Pomatoschistus minutus (Osteichthys, Gobiidae). Biol. J. Linn. Soc. 102, 175–198. https://doi.org/10.1111/j.1095-8312.2010.01565.x (2011).
Article  Google Scholar 

58.
Taviani, M. The Mediterranean benthos from Late Miocene up to Present: ten million years of dramatic climatic and geological vicissitudes. Biol. Mar. Mediterr. 9, 445–463 (2002).
Google Scholar 

59.
Marino, I. A. M., Pujolar, J. M. & Zane, L. Reconciling deep calibration and demographic history: Bayesian inference of post glacial colonization patterns in Carcinus aestuarii (Nardo, 1847) and C. maenas (Linnaeus, 1758). PLoS ONE 6, 10. https://doi.org/10.1371/journal.pone.0028567 (2011).
CAS  Article  Google Scholar 

60.
Grant, W. S., Liu, M., Gao, T. X. & Yanagimoto, T. Limits of Bayesian skyline plot analysis of mtDNA sequences to infer historical demographies in Pacific herring (and other species). Mol. Phylogenet. Evol. 65, 203–212. https://doi.org/10.1016/j.ympev.2012.06.006 (2012).
Article  PubMed  Google Scholar 

61.
Silva, G., Horne, J. B. & Castilho, R. Anchovies go north and west without losing diversity: post-glacial range expansions in a small pelagic fish. J. Biogeogr. 41, 1171–1182. https://doi.org/10.1111/jbi.12275 (2014).
Article  Google Scholar 

62.
Albaina, N., Olsen, J. L., Couceiro, L., Ruiz, J. M. & Barreiro, R. Recent history of the European Nassarius nitidus (Gastropoda): phylogeographic evidence of glacial refugia and colonization pathways. Mar. Biol. 159, 1871–1884. https://doi.org/10.1007/s00227-012-1975-9 (2012).
Article  Google Scholar 

63.
Krijgsman, W. et al. Quaternary time scales for the Pontocaspian domain: interbasinal connectivity and faunal evolution. Earth Sci. Rev. 188, 1–40. https://doi.org/10.1016/j.earscirev.2018.10.013 (2018).
ADS  Article  Google Scholar 

64.
Buyukmeric, Y. Postglacial floodings of the Marmara Sea: molluscs and sediments tell the story. Geomar. Lett. 36, 307–321. https://doi.org/10.1007/s00367-016-0446-6 (2016).
ADS  Article  Google Scholar 

65.
Semikolennykh, D., Ignatov, E., Yanina T. & Arslanov, K. Malacofauna of the Kerch Strait during the Late Pleistocene-Holocene: paleogeographical analysis. In: IGCP 610 Fourth Plenary Conference and Field Trip, Tbilisi, Georgia, 2–9 October 2016, 149–152 (2016).

66.
Samadi, S., Lambourdiere, J., Hebert, P. & Boisselier-Dubayle, M. C. Polymorphic microsatellites for the study of adults, egg-masses and hatchlings of five Cerithium species (Gastropoda) from the Mediterranean sea. Mol. Ecol. Notes 1, 44–46. https://doi.org/10.1046/j.1471-8278.2000.00019.x (2001).
CAS  Article  Google Scholar 

67.
Ribeiro, P. A., Branco, M., Hawkins, S. J. & Santos, A. M. Recent changes in the distribution of a marine gastropod, Patella rustica, across the Iberian Atlantic coast did not result in diminished genetic diversity or increased connectivity. J. Biogeogr. 37, 1782–1796. https://doi.org/10.1111/j.1365-2699.2010.02330.x (2010).
Article  Google Scholar 

68.
Cossu, P. et al. Surviving at the edge of a fragmented range: patterns of genetic diversity in isolated populations of the endangered giant Mediterranean limpet (Patella ferruginea). Mar. Biol. 164, 18. https://doi.org/10.1007/s00227-017-3080-6 (2017).
Article  Google Scholar 

69.
Dupont, L., Bernas, D. & Viard, F. Sex and genetic structure across age groups in populations of the European marine invasive mollusc, Crepidula fornicata L. (Gastropoda). Biol. J. Linn. Soc. 90, 365–374. https://doi.org/10.1111/j.1095-8312.2007.00731.x (2007).
Article  Google Scholar 

70.
Paterno, M. et al. A genome-wide approach to the phylogeography of the mussel Mytilus galloprovincialis in the Adriatic and the Black Seas. Front. Mar. Sci. 6, 16. https://doi.org/10.3389/fmars.2019.00566 (2019).
ADS  Article  Google Scholar 

71.
Hare, M. P., Karl, S. A. & Avise, J. C. Anonymous nuclear DNA markers in the American oyster and their implications for the heterozygote deficiency phenomenon in marine bivalves. Mol. Biol. Evol. 13, 334–345. https://doi.org/10.1093/oxfordjournals.molbev.a025593 (1996).
CAS  Article  PubMed  Google Scholar 

72.
Johnson, M. S. & Black, R. The Wahlund effect and the geographical scale of variation in the intertidal limpet Siphonaria sp. Mar. Biol. 79, 295–302. https://doi.org/10.1007/bf00393261 (1984).
Article  Google Scholar 

73.
Mallet, A. L., Zouros, E., Gartnerkepkay, K. E., Freeman, K. R. & Dickie, L. M. Larval viability and heterozygote deficiency in populations of marine bivalves—evidence from pair matings of mussels. Mar. Biol. 87, 165–172. https://doi.org/10.1007/bf00539424 (1985).
Article  Google Scholar 

74.
Boissin, E., Hoareau, T. B., Feral, J. P. & Chenuil, A. Extreme selfing rates in the cosmopolitan brittle star species complex Amphipholis squamata: data from progeny-array and heterozygote deficiency. Mar. Ecol. Prog. Ser. 361, 151–159. https://doi.org/10.3354/meps07411 (2008).
ADS  CAS  Article  Google Scholar 

75.
Boissin, E., Egea, E., Feral, J. P. & Chenuil, A. Contrasting population genetic structures in Amphipholis squamata, a complex of brooding, self-reproducing sister species sharing life history traits. Mar. Ecol. Prog. Ser. 539, 165–177. https://doi.org/10.3354/meps11480 (2015).
ADS  CAS  Article  Google Scholar 

76.
Dudu, A., Georgescu, S. E., Suciu, R., Dinischiotu, A. & Costache, M. Microsatelitte DNA variation in the black sea beluga sturgeon (Huso huso). Rom. Biotech. Lett. 13, 3779–3783 (2008).
CAS  Google Scholar 

77.
Wilson, A. B. & Veraguth, I. E. The impact of Pleistocene glaciation across the range of a widespread European coastal species. Mol. Ecol. 19, 4535–4553. https://doi.org/10.1111/j.1365-294X.2010.04811.x (2010).
CAS  Article  PubMed  Google Scholar 

78.
Limborg, M. T. et al. Imprints from genetic drift and mutation imply relative divergence times across marine transition zones in a pan-European small pelagic fish (Sprattus sprattus). Heredity 109, 96–107. https://doi.org/10.1038/hdy.2012.18 (2012).
CAS  Article  PubMed  PubMed Central  Google Scholar 

79.
Miralles, L., Juanes, F., Pardinas, A. F. & Garcia-Vazquez, E. Paleoclimate shaped bluefish structure in the northern hemisphere. Fisheries 39, 578–586. https://doi.org/10.1080/03632415.2014.976701 (2014).
Article  Google Scholar 

80.
Magoulas, A., Castilho, R., Caetano, S., Marcato, S. & Patarnello, T. Mitochondrial DNA reveals a mosaic pattern of phylogeographical structure in Atlantic and Mediterranean populations of anchovy (Engraulis encrasicolus). Mol. Phylogenet. Evol. 39, 734–746. https://doi.org/10.1016/j.ympev.2006.01.016 (2006).
CAS  Article  PubMed  Google Scholar 

81.
Durand, J. D., Blel, H., Shen, K. N., Koutrakis, E. T. & Guinand, B. Population genetic structure of Mugil cephalus in the Mediterranean and Black Seas: a single mitochondrial clade and many nuclear barriers. Mar. Ecol. Prog. Ser. 474, 243–261. https://doi.org/10.3354/meps10080 (2013).
ADS  Article  Google Scholar 

82.
Pascual, M., Rives, B., Schunter, C. & Macpherson, E. Impact of life history traits on gene flow: a multispecies systematic review across oceanographic barriers in the Mediterranean Sea. PLoS ONE 12, 20. https://doi.org/10.1371/journal.pone.0176419 (2017).
CAS  Article  Google Scholar 

83.
Anderson, E. C. & Dunham, K. K. The influence of family groups on inferences made with the program Structure. Mol. Ecol. Resour. 8, 1219–1229. https://doi.org/10.1111/j.1755-0998.2008.02355.x (2008).
CAS  Article  PubMed  Google Scholar 

84.
Peterman, W., Brocato, E. R., Semlitsch, R. D. & Eggert, L. S. Reducing bias in population and landscape genetic inferences: the effects of sampling related individuals and multiple life stages. PeerJ 4, 19. https://doi.org/10.7717/peerj.1813 (2016).
Article  Google Scholar 

85.
Waples, R. S. & Anderson, E. C. Purging putative siblings from population genetic data sets: a cautionary view. Mol. Ecol. 26, 1211–1224. https://doi.org/10.1111/mec.14022 (2017).
Article  PubMed  Google Scholar 

86.
Fernandez, R., Lemer, S., McIntyre, E. & Giribet, G. Comparative phylogeography and population genetic structure of three widespread mollusc species in the Mediterranean and near Atlantic. Mar. Ecol. Evol. Perspect. 36, 701–715. https://doi.org/10.1111/maec.12178 (2015).
Article  Google Scholar 

87.
Selwyn, J. D. et al. Kin-aggregations explain chaotic genetic patchiness, a commonly observed genetic pattern, in a marine fish. PLoS ONE 11, 11. https://doi.org/10.1371/journal.pone.0153381 (2016).
CAS  Article  Google Scholar 

88.
Highsmith, R. C. Floating and algal rafting as potential dispersal mechanisms in brooding invertebrates. Mar. Ecol. Prog. Ser. 25, 169–179. https://doi.org/10.3354/meps025169 (1985).
ADS  Article  Google Scholar 

89.
Thiel, M. & Haye, P. A. In Oceanography and Marine Biology—An Annual Review Vol. 44 (eds Gibson, R. N. et al.) 323–429 (CRC Press-Taylor & Francis Group, Boca Raton, 2006).
Google Scholar 

90.
Darras, H. & Aron, S. Introgression of mitochondrial DNA among lineages in a hybridogenetic ant. Biol. Lett. 11, 4. https://doi.org/10.1098/rsbl.2014.0971 (2015).
ADS  Article  Google Scholar 

91.
Perea, S., Vukic, J., Sanda, R. & Doadrio, I. Ancient mitochondrial capture as factor promoting mitonuclear discordance in freshwater fishes: a case study in the genus Squalius (Actinopterygii, Cyprinidae) in Greece. PLoS ONE 11, 26. https://doi.org/10.1371/journal.pone.0166292 (2016).
CAS  Article  Google Scholar 

92.
Markova, S., Dufresne, F., Manca, M. & Kotlik, P. Mitochondrial capture misleads about ecological speciation in the Daphnia pulex complex. PLoS ONE 8, 14. https://doi.org/10.1371/journal.pone.0069497 (2013).
CAS  Article  Google Scholar 

93.
Rawson, P. D. & Hilbish, T. J. Asymmetric introgression of mitochondrial DNA among European populations of blue mussels (Mytilus spp.). Evolution 52, 100–108. https://doi.org/10.2307/2410924 (1998).
Article  PubMed  Google Scholar 

94.
Azuma, N., Yamazaki, T. & Chiba, S. Mitochondrial and nuclear DNA analysis revealed a cryptic species and genetic introgression in Littorina sitkana (Mollusca, Gastropoda). Genetica 139, 1399–1408. https://doi.org/10.1007/s10709-012-9638-9 (2011).
CAS  Article  PubMed  Google Scholar 

95.
Rius, M. & Darling, J. A. How important is intraspecific genetic admixture to the success of colonising populations?. Trends Ecol. Evol. 29, 233–242. https://doi.org/10.1016/j.tree.2014.02.003 (2014).
Article  PubMed  Google Scholar 

96.
Boissin, E., Hoareau, T. B., Postaire, B., Gravier-Bonnet, N. & Bourmaud, C. A. F. Cryptic diversity, low connectivity and suspected human-mediated dispersal among 17 widespread Indo-Pacific hydroid species of the south-western Indian Ocean. J. Biogeogr. 45, 2104–2117. https://doi.org/10.1111/jbi.13388 (2018).
Article  Google Scholar 

97.
Boero, F. et al. CoCoNet: towards coast to coast networks of marine protected areas (from the shore to the high and deep sea), coupled with sea-based wind energy potential. Scires-It 6, 1–95 (2016).
Google Scholar 

98.
Katoh, K. & Standley, D. M. MAFFT multiple sequence alignment software version 7: improvements in performance and usability. Mol. Biol. Evol. 30, 772–780. https://doi.org/10.1093/molbev/mst010 (2013).
CAS  Article  PubMed  PubMed Central  Google Scholar 

99.
Librado, P. & Rozas, J. DnaSP v5: a software for comprehensive analysis of DNA polymorphism data. Bioinformatics 25, 1451–1452. https://doi.org/10.1093/bioinformatics/btp187 (2009).
CAS  Article  Google Scholar 

100.
Bandelt, H. J., Forster, P. & Rohl, A. Median-joining networks for inferring intraspecific phylogenies. Mol. Biol. Evol. 16, 37–48. https://doi.org/10.1093/oxfordjournals.molbev.a026036 (1999).
CAS  Article  PubMed  Google Scholar 

101.
Nylander, J. MrAIC.pl. Program Distributed by the Author (Evolutionary Biology Centre, Uppsala University, Sweden, 2004).
Google Scholar 

102.
Bouckaert, R. et al. BEAST 2: a software platform for Bayesian evolutionary analysis. PLoS Comput. Biol. 10, 6. https://doi.org/10.1371/journal.pcbi.1003537 (2014).
CAS  Article  Google Scholar 

103.
Wilke, T., Schultheiss, R. & Albrecht, C. As time goes by: a simple fool’s guide to molecular clock approaches in invertebrates. Am. Malacol. Bull. 27, 25–45 (2009).
Article  Google Scholar 

104.
Stelbrink, B., Shirokaya, A. A., Foller, K., Wilke, T. & Albrecht, C. Origin and diversification of Lake Ohrid’s endemic acroloxid limpets: the role of geography and ecology. BMC Evol. Biol. 16, 13. https://doi.org/10.1186/s12862-016-0826-6 (2016).
Article  Google Scholar 

105.
Van Oosterhout, C., Hutchinson, W. F., Wills, D. P. M. & Shipley, P. MICRO-CHECKER: software for identifying and correcting genotyping errors in microsatellite data. Mol. Ecol. Notes 4, 535–538. https://doi.org/10.1111/j.1471-8286.2004.00684.x (2004).
CAS  Article  Google Scholar 

106.
Panova, M., Makinen, T., Fokin, M., Andre, C. & Johannesson, K. Microsatellite cross-species amplification in the genus Littorina and detection of null alleles in Littorina saxatilis. J. Molluscan Stud. 74, 111–117. https://doi.org/10.1093/mollus/eym052 (2008).
Article  Google Scholar 

107.
GENETIX 4.05, logiciel sous Windows TM pour la génétique des populations, Laboratoire Génome, Populations, Interactions, CNRS UMR 5000, Université de Montpellier II, Montpellier (France) (1996–2004).

108.
Benjamini, Y. & Hochberg, Y. Controlling the false discovery rate—a practical and powerful approach to multiple testing. J. R. Stat. Soc. Ser. B Methodol. 57, 289–300 (1995).
MathSciNet  MATH  Google Scholar 

109.
Peakall, R. & Smouse, P. E. GenAlEx 6.5: genetic analysis in Excel. Population genetic software for teaching and research—an update. Bioinformatics 28, 2537–2539. https://doi.org/10.1093/bioinformatics/bts460 (2012).
CAS  Article  PubMed  PubMed Central  Google Scholar 

110.
Pritchard, J. K., Stephens, M. & Donnelly, P. Inference of population structure using multilocus genotype data. Genetics 155, 945–959 (2000).
CAS  PubMed  PubMed Central  Google Scholar 

111.
Evanno, G., Regnaut, S. & Goudet, J. Detecting the number of clusters of individuals using the software STRUCTURE: a simulation study. Mol. Ecol. 14, 2611–2620. https://doi.org/10.1111/j.1365-294X.2005.02553.x (2005).
CAS  Article  Google Scholar 

112.
Earl, D. A. & Vonholdt, B. M. STRUCTURE HARVESTER: a website and program for visualizing STRUCTURE output and implementing the Evanno method. Conserv. Genet. Resour. 4, 359–361. https://doi.org/10.1007/s12686-011-9548-7 (2012).
Article  Google Scholar 

113.
Rousset, F. Genetic differentiation and estimation of gene flow from F-statistics under isolation by distance. Genetics 145, 1219–1228 (1997).
CAS  PubMed  PubMed Central  Google Scholar 

114.
Jones, O. R. & Wang, J. L. COLONY: a program for parentage and sibship inference from multilocus genotype data. Mol. Ecol. Resour. 10, 551–555. https://doi.org/10.1111/j.1755-0998.2009.02787.x (2010).
Article  PubMed  Google Scholar 

115.
Fu, Y. X. & Li, W. H. Statistical tests of neutrality of mutations. Genetics 133, 693–709 (1993).
CAS  PubMed  PubMed Central  Google Scholar 

116.
Ramos-Onsins, S. E. & Rozas, J. Statistical properties of new neutrality tests against population growth. Mol. Biol. Evol. 19, 2092–2100. https://doi.org/10.1093/oxfordjournals.molbev.a004034 (2002).
CAS  Article  PubMed  Google Scholar  More

Microorganisms inhabiting biofilms form structurally and functionally well-organized communities where the interactions among the participants can be extremely complex1. The in situ model systems arranged in controlled, natural and quasi-stagnant physical–chemical conditions are important to understand the changes that take place in the development of biofilms. Therefore, the present study performed in the RT spring cave highlighted some interesting phenomena on biofilm formation in thermal karst systems.
In the initial stage of biofilm formation, primary colonizing microorganisms generally bind through specific and non-specific interactions to a conditioning film adhering to solid surfaces, consisting of various organic materials19. It was also clearly visible on the SEM images (Fig. 2) made at the beginning of the model experiment. In the next stage of biofilm formation, microbial cells embed into an EPS. The increasing EPS production was observed from the 9–12 weeks during the experiment of biofilm development in the RT spring cave. The EPS consists of polysaccharides, glycoproteins, exoenzymes and nucleic acids that attach directly to the surface, also known as a substrate20,21.
The microscopically observed biofilm maturation correlated well with species richness estimators and diversity indices. The number of OTUs greatly increased during the first nine weeks when the morphological diversity of the biofilm also became more and more complex. Most of the dynamic changes observed at the OTU level happened until the twelfth week, so we could state that nine to twelve weeks were needed for the maturation of the biofilm. The greatest difference between the species richness estimators was among the water and biofilm samples.
In an earlier study, a natural biofilm sample (RTB) developed for years on the rock of the RT spring cave17 was analyzed. The taxonomic composition of bacteria inhabiting the natural and experimental biofilms was similar. The most abundant community members of the RTB as well as the 3–30 weeks and one-year in situ model biofilm samples were also the same, showing no difference or selection between the glass slide and the carbonate rock as a surface substrate. Based on our previous results17, unforeseen taxonomic bacterial diversity was obtained from these highly radioactive environments (600 ± 21 Bq/L radon concentration in the case of Rudas-Török spring cave) based on next-generation sequencing data, containing mainly unclassified bacteria affiliated with low level similarity to cultured bacterial taxa. The surprisingly high diversity suggests that the microorganisms living here are well adapted to this extreme environment.
As regards the taxonomic diversity, Proteobacteria was detected with relative high abundance both in the water and biofilm samples (Fig. 3). It could be presumed based on the previous studies, because members of this phylum were frequently detected as constituents of bacterial communities in different cave samples6,22,23,24,25,26. The water sample of the RT spring cave was dominated almost exclusively by an unclassified Hydrogenophilaceae (OTU3) that showed the highest sequence similarity with chemo-lithotrophic sulfur-oxidizing bacteria (Fig. 3). OTU3 (with its 96.1% relative abundance) could be considered as the potential ‘core OTU’ of the thermal water. To our knowledge, this is the first report about such extraordinary high relative abundance of an OTU in the thermal waters of karst caves. Deja-Sikora et al.27 reported a similar phenomenon, the dominance of one unclassified Betaproteobacteria OTU affiliated with Comamonadaceae (abundance ranging from 1.7 to 57.8%) in sulfide-rich waters of the Carpathian Foredeep. The authors assumed that members of Comamonadaceae were likely to represent archetypal microbial species in those waters. The dominant read of OTU3 originated from the discharging deep thermal waters, however, was almost completely absent from the biofilm samples of the spring cave. This finding was surprising because the role of bacteria arriving with the discharging water sample was hypothesized in the formation of the biofilm. Among Proteobacteria, an unclassified Gammaproteobacteria was the most abundant in the biofilm samples (Fig. 3), nevertheless, no more information is known about these unclassified phylotypes.
The phylum Chloroflexi was dominant in the biofilms throughout the studied period with the members of unclassified Anaerolineaceae (Fig. 3). These Gram-negative, filamentous, thermophilic and strictly anaerobic, chemo-organotrophic organisms28 can serve as the basis of the biofilm formation in the RT spring cave. The non-cultivated members of the class Dehalococcoidi have fermentative metabolism and can use N-acetylglucosamine under anoxic conditions (using nitrate as electron acceptor). N-acetylglucosamine, which forms the backbone of the murein of most bacterial cell walls, is released continuously when cells are destroyed29. Presumably, the representatives of these filamentous Chloroflexi can be the first adherent organisms according to the SEM images (Fig. 2), and the low oxygen level in the spring cave may have favored their reproduction. The OTUs assigned to the phylum Chloroflexi were also frequent not only in the biofilm formed on the glass slides but in the biofilm developed on the rock surface of the RT spring cave as well17.
Representatives of the genus Nitrospira (Nitrospirota) were also present throughout the experiment. Their highest proportion were observed in the sixth week of the biofilm formation (Fig. 3). The characteristic cell shape typical for the genus Nitrospira has been observed in the three-week biofilm sample on the SEM images, as well (Fig. 2). Nitrogen is frequently a limited nutrient source in caves; therefore, the importance of the nitrogen cycle has been emphasized in other studies25,30. Chemolithotrophic autotrophic prokaryotes, including nitrifiers, play a key role in the primary production of cave environments31. The presence of ammonia-oxidizing Nitrosospira and nitrite-oxidizing Nitrospira and Nitrobacter were revealed previously from the deposits of the cave wall of the western Loess Plateau of China32 and the presence of these organisms were detected in the caves and spring caves of the BTKS5,6,7,8, as well. Through their activity, nitrite-oxidizing aerobic chemolithotrophic bacteria may contribute to the low nitrite concentration values, which were also measured in the cave waters of the BTKS.
A possible reason for the low ammonia content in the BTKS is the oxidation of ammonia, in which members of both the Archaea and Bacteria may be involved. The ammonia-oxidizing archaea (AOA) organisms belonging to the phylum Thaumarchaeota appeared in high proportion in the archaeal clone libraries created from biofilms originated from the caves and spring caves of the BTKS6,7. For the members of the Archaea, an increasing temporal trend was observed in the biofilms from the fifteenth weeks, although the primer-pair which was used for amplification is rather Bacteria-specific33. The diversity and importance of Archaea in karst cave environments, in contrast to the Bacteria, is largely unexplored2,3,34. In the study of the speleothems of the Weebubbie Cave (Nullarbor karst, Australia) and Kartchner Caverns (Arizona, USA), the authors also demonstrated the importance of members of Archaea, especially the ammonia-oxidizing Thaumarchaeota2,3,34. Our findings may confirm the hypothesis that AOA organisms could have an important role in the nitrification process in the RT spring cave as well.
The members of the phylum Planctomycetota (Candidatus Brocadia) proved to be dominant in the biofilm samples (Fig. 3). Representatives of the ‘Candidatus Brocadia’ may participate also in the local nitrogen cycle by the anaerobic oxidation of ammonia (anammox) combined with nitrite reduction that results in the formation of elemental nitrogen35. The anaerobic ammonia-oxidizing bacteria grow very slowly, the fastest growing species also have a 10-day generation time36, which may be associated with the fact that the relative abundance of the phylum showed a significant increase only from the sixth week of biofilm formation.
Representatives of the phylum Patescibacteria (unclassified Parcubacteria) were found in high proportions in the biofilm samples (Fig. 3). These organisms were mostly observed in anoxic environments37, their presence can be associated with the low dissolved oxygen values in the RT spring cave. The members of the Parcubacteria have small genome size ( More

Ecosystems emit biogenic volatile organic compounds (BVOCs) to facilitate ecophysiological functioning, pathogen defence and stress responses, and are thus critical to ecosystem health. In addition, BVOCs influence local and global climate through reactions that produce secondary organic aerosols, in turn, impacting cloud production and properties. While the range and volume of BVOC emissions — the ‘volatilome’ — is well-established for terrestrial systems, explicit quantification for marine systems, including corals, is lacking.

Caitlin Lawson from the University of Technology Sydney and University of Newcastle, Australia, and colleagues, use samples of Acropora intermedia and Pocillopora damicornis from Heron Island in the Southern Great Barrier Reef to characterize the volatilome and determine the influence of heat stress. A. intermedia and P. damicornis holobionts are associated with 79 and 76 BVOCs, respectively, the majority of which fall into antimicrobial and climatic functional categories. Both species, for example, emit bromoform and chlorodibromomethane, known precursors for ozone depletion but previously undetected in corals. The chemical diversity and abundance of BVOCs is significantly altered by heat stress, including a 42% reduction in BVOC variety for A. intermedia (the majority with antimicrobial functionality) and 62% for P. damicornis (the majority being climatically active).

Credit: Nature Picture Library / Alamy Stock Photo

This initial estimate of the coral volatilome for two common species indicates that BVOCs likely have a strong, yet previously undetermined, role in coral physiology and functioning. However, this healthy functioning is threatened by anthropogenic warming and corresponding BVOC interactions. Further research is thus required to better quantify the coral volatilome, including for more species, so as to better determine coral resilience to future warming. More

Experimental design and community composition
We conducted a randomized-block experiment at the Russell E. Larsen Agricultural Research Center (Pennsylvania Furnace, PA, USA) with replicated mesocosm ponds. Mesocosms were 1100-L cattle tanks covered with 60% shade cloth. The spatial block was distance from a tree line in our mesocosm field. Three weeks before pesticide application, these mesocosms were filled with 800 L water, 300 g mixed hardwood leaves, and inoculations of zooplankton, periphyton, and phytoplankton homogenized from four local ponds. Just before pesticide application on the same day, each tank received two snail, three larval anuran, one larval dragonfly, one water bug, one water beetle, one larval salamander, and one backswimmer species (11 Helisoma (Planorbella) trivolvis, 10 Physa gyrina; 20 Hyla versicolor, 20 Lithobates palustris, 20 Lithobates clamitans; 2 Anax junius; 2 Belostoma flumineum; 5 Hydrochara sp.; 3 Ambystoma maculatum; 6 Nototeca undulata) (Fig. 1b). These community members naturally coexist and were applied at naturally occurring densities40. Initial conditions of some mesocosms varied in simulated pesticide treatments (see below).
We randomly assigned 18 treatments (12 pesticides, 4 simulated pesticides, 2 controls) with four replicate mesocosms of each treatment, which resulted in 72 total mesocosms (Fig. 1a). The 12 pesticide treatments were nested; we included two pesticide types (insecticide, herbicide), two classes within each pesticide type (organophosphate insecticide, carbamate insecticide, chloroacetanilide herbicide, triazine herbicide), and three different pesticides in each of four classes (Fig. 1a). To represent runoff of pesticides into freshwater systems following a rainfall event, we applied single doses of technical grade pesticides at environmentally relevant concentrations at the beginning of the experiment. To ensure our exposures represented environmental relevance, we used estimated environmental concentrations of pesticides, calculated by U.S. Environmental Protection Agency’s GENEEC v2 software, Supplementary Table 2). Our design also included water and solvent (0.0001% acetone) controls (Fig. 1a). Pesticides were obtained from ChemService (West Chester, PA, USA). Nominal concentrations of pesticides (μg/L) were: 64 chlorpyrifos, 101 malathion, 171 terbufos, 91 aldicarb, 219 carbaryl, 209 carbofuran, 123 acetochlor, 127 alachlor, 105 metolachlor, 102 atrazine, 202 simazine, and 106 propazine. We collected composite water samples 1 h after application to mesocosms and shipped samples on ice to Mississippi State Chemical Laboratory to verify these nominal concentrations. Measured concentrations of pesticides (μg/L) were: 60 chlorpyrifos, 105 malathion, 174 terbufos, 84 aldicarb, 203 carbaryl, 227 carbofuran, 139 acetochlor, 113 alachlor, 114 metolachlor, 117 atrazine, 180 simazine, and 129 propazine.
The four simulated pesticide treatments were top-down or bottom-up food web manipulations intended to mimic effects of actual herbicides and insecticides on community members. These manipulations occurred once and were concurrent with the timing of pesticide applications. Top-down and bottom-up simulated insecticide treatments were designed to reduce densities of zooplankton, simulating effects of insecticides on zooplankton survival. For top-down simulated insecticides, we doubled the densities of zooplankton predators by including six total A. maculatum larval salamanders and 12 N. undulata backswimmers per mesocosm. For bottom-up simulated insecticides (i.e., direct manipulation of a lower arthropod trophic level), we removed zooplankton with a net. Top-down and bottom-up simulated herbicides were designed to reduce algae, simulating effects of herbicides on survival and growth of algae. For top-down simulated herbicides, we doubled the densities of large herbivores to increase grazing pressure by including 22 H. trivolvis snails, 20 P. gyrina snails, 40 H. versicolor larval anurans, 40 L. palustris larval anurans, and 40 L. clamitans larval anurans per mesocosm. For bottom-up simulated herbicides, we covered mesocosms in three sheets of 60% shade cloth in an attempt to block light and reduce photosynthesis. The experiment ran for four weeks, from June to July.
Measurements of experimental responses
During the experiment, we sampled periphyton using clay tiles (100 cm2) oriented perpendicularly along the bottom of the mesocosm. Each mesocosm had two periphyton measurements: ‘inaccessible periphyton’ taken from caged clay tiles that excluded herbivores and ‘accessible periphyton’ taken from clay tiles that were uncaged, allowing herbivore access. We sampled phytoplankton from water samples taken 10 cm below the water surface. Periphyton was scrubbed from tiles and phytoplankton from water samples (10 mL) were filtered onto glass fiber filters (under low vacuum pressure, More

1.
Lovell, S. J., Stone, S. F. & Fernandez, L. The economic impacts of aquatic invasive species: a review of the literature. Agric. Resour. Econ. Rev. 35(1), 195–208 (2006).
Article  Google Scholar 
2.
Ehrenfeld, J. G. Ecosystem consequences of biological invasions. Ann. Rev. Ecol. Evol. Syst. 41, 59–80 (2010).
Article  Google Scholar 

3.
Dunham, J. B., Adams, S. B., Schroeter, R. E. & Novinger, D. C. Non-native invasions in aquatic ecosystems: toward an understanding of brook trout invasions and potential impacts on inland cutthroat trout in western North America. Rev. Fish Biol. Fish. 12(4), 373–391 (2002).
Article  Google Scholar 

4.
Balzani, P. et al. Stable isotope analysis of trophic niche in two co-occurring native and invasive terrapins, Emys orbicularis and Trachemys scripta elegans. Biol. Invasions 18(12), 3611–3621 (2016).
Article  Google Scholar 

5.
Haubrock, P. J. et al. Control and eradication efforts of aquatic non-native fish species in Lake Caicedo Yuso-Arreo. Manag. Biol. Invasions 9, 267–278 (2018).
Article  Google Scholar 

6.
Preston, D. L., Henderson, J. S. & Johnson, P. T. Community ecology of invasions: direct and indirect effects of multiple invasive species on aquatic communities. Ecology 93(6), 1254–1261 (2012).
PubMed  Article  Google Scholar 

7.
Gallardo, B., Clavero, M., Sánchez, M. I. & Vilà, M. Global ecological impacts of invasive species in aquatic ecosystems. Glob. Change Biol. 22(1), 151–163 (2016).
ADS  Article  Google Scholar 

8.
Pejchar, L. & Mooney, H. A. Invasive species, ecosystem services and human well-being. Trends Ecol. Evol. 24(9), 497–504 (2009).
PubMed  Article  Google Scholar 

9.
Simberloff, D. & Von Holle, B. Positive interactions of nonindigenous species: invasional meltdown?. Biol. Invasions 1(1), 21–32 (1999).
Article  Google Scholar 

10.
Beisel, J. N. The elusive model of a biological invasion process: time to take differences among aquatic and terrestrial ecosystems into account? (2001).

11.
Ricciardi, A. & Cohen, J. The invasiveness of an introduced species does not predict its impact. Biol. Invasions 9(3), 309–315 (2007).
Article  Google Scholar 

12.
Strayer, D. L. Non-native species in fresh waters: ecological effects, interactions with other stressors, and prospects for the future. Freshw. Biol. 55, 152–174 (2010).
Article  Google Scholar 

13.
Früh, D., Stoll, S. & Haase, P. Physicochemical and morphological degradation of stream and river habitats increases invasion risk. Biol. Invasions 14(11), 2243–2253 (2012).
Article  Google Scholar 

14.
Höckendorff, S., Früh, D., Hormel, N., Haase, P. & Stoll, S. Biotic interactions under climate warming: temperature-dependent and species-specific effects of the oligochaete Chaetogaster limnaei on snails. Freshw. Sci. 34, 1304–1311 (2015).
Article  Google Scholar 

15.
Leung, B. & Mandrak, N. E. The risk of establishment of aquatic invasive species: joining invasibility and propagule pressure. Proc. R. Soc. B Biol. Sci. 274(1625), 2603–2609 (2007).
Article  Google Scholar 

16.
Copp, G. H., Garthwaite, R. & Gozlan, R. E. Risk identification and assessment of non-native freshwater fishes: a summary of concepts and perspectives on protocols for the UK. J. Appl. Ichthyol. 21(4), 371–373 (2005).
Article  Google Scholar 

17.
Copp, G. H. et al. European non-native species in aquaculture risk analysis scheme—a summary of assessment protocols and decision support tools for use of non-native species in aquaculture. Fish. Manag. Ecol. 23(1), 1–11 (2016).
Article  Google Scholar 

18.
Bacher, S. et al. Socio-economic impact classification of non-native taxa (SEICAT). Methods Ecol. Evol. 9(1), 159–168 (2018).
Article  Google Scholar 

19.
Roy, H. E. et al. Developing a framework of minimum standards for the risk assessment of non-native species. J. Appl. Ecol. 55(2), 526–538 (2018).
Article  Google Scholar 

20.
Moustakas, A. & Katsanevakis, S. Data mining and methods for early detection, horizon scanning, modelling, and risk assessment of invasive species. Front. Appl. Math. Stat. 4, 5 (2018).
Article  Google Scholar 

21.
Dick, J. T. et al. Invader relative impact potential: a new metric to understand and predict the ecological impacts of existing, emerging and future invasive non-native species. J. Appl. Ecol. 54(4), 1259–1267 (2017).
Article  Google Scholar 

22.
Cuthbert, R. N., Dickey, J. W., Coughlan, N. E., Joyce, P. W. & Dick, J. T. The functional response ratio (FRR): advancing comparative metrics for predicting the ecological impacts of invasive non-native species. Biol. Invasions 1–5 (2019).

23.
Haubrock, P. J. et al. Predatory functional responses under increasing temperatures of two life stages of an invasive gecko. Sci. Rep. 10(1), 1–10 (2020).
Article  CAS  Google Scholar 

24.
Vonesh, J., McCoy, M., Altwegg, R., Landi, P. & Measey, J. Functional responses can’t unify invasion ecology. Biol. Invasions 19(5), 1673–1676 (2017).
Article  Google Scholar 

25.
Dick, J. T. et al. Fictional responses from Vonesh et al. Biol. Invasions 19(5), 1677–1678 (2017).
Article  Google Scholar 

26.
Vander Zanden, M. J., Casselman, J. M. & Rasmussen, J. B. Stable isotope evidence for the food web consequences of species invasions in lakes. Nature 401(6752), 464 (1999).
ADS  Article  CAS  Google Scholar 

27.
Haubrock, P. J. et al. Shared histories of co-evolution may affect trophic interactions in a freshwater community dominated by non-native species. Front. Ecol. Evol. 7, 355 (2019).
Article  Google Scholar 

28.
Stellati, L. et al. Living with non-natives: suboptimal ecological condition in semiaquatic snakes inhabiting a hot spot of allodiversity. Acta Oecol. 100, 103466 (2019).
Article  Google Scholar 

29.
Huckembeck, S. et al. Feeding ecology and basal food sources that sustain the Paradoxal frog Pseudis minuta: a multiple approach combining stomach content, prey availability, and stable isotopes. Hydrobiologia 740(1), 253–264 (2014).
Article  Google Scholar 

30.
Middelburg, J. J. Stable isotopes dissect aquatic food webs from the top to the bottom. Biogeosciences. 11, 2357–2371 (2014).
ADS  Article  Google Scholar 

31.
Jackson, A. L., Inger, R., Parnell, A. C. & Bearhop, S. Comparing isotopic niche widths among and within communities: SIBER–stable isotope bayesian ellipses in R. J. Anim. Ecol. 80(3), 595–602 (2011).
Article  Google Scholar 

32.
Parnell, A. C. et al. Bayesian stable isotope mixing models. Environmetrics 24(6), 387–399 (2013).
MathSciNet  Google Scholar 

33.
Haubrock, P. J. et al. Predicting the effects of reintroducing a native predator (European eel, Anguilla anguilla) into a freshwater community dominated by non-native species using a multidisciplinary approach. Manag. Biol. Invasions 10(1), 171–191 (2019).
Article  Google Scholar 

34.
Post, D. M. Using stable isotopes to estimate trophic position: models, methods, and assumptions. Ecology 83(3), 703–718 (2002).
Article  Google Scholar 

35.
Füreder, L., Gherardi, F., Holdich, D., Reynolds, J., Sibley, P. & Souty-Grosset, C. Austropotamobius pallipes. The IUCN Red List of Threatened Species. e.T2430A9438817. https://doi.org/10.2305/IUCN.UK.2010-3.RLTS.T2430A9438817.en. (2010).

36.
Pike, C., Crook, V. & Gollock, M. Anguilla anguilla. The IUCN Red List of Threatened Species e.T60344A152845178. https://doi.org/10.2305/IUCN.UK.2020-2.RLTS.T60344A152845178.en. (2020).

37.
González-Mozo, M. E., Chicote, A., Rico, E. & Montes, C. Limnological characterization of an evaporite karstic lake in Spain (Arreo Lake). Trends Ecol. Evol. 19(9), 470–474 (2004).
Article  Google Scholar 

38.
Asensio, R. Actuaciones de descaste de cangrejos alóctonos en el lago de Caicedo Yuso – Arreo para los años 2014 y 2015. PROYECTO TREMEDAL “LIFE11 NAT/ES/707”. URA/Arabako Foru Aldundia/HAZI. (2015).

39.
Alonso de Santocildes, G., Criado, A., Manzanos, A. & A.P. Monteoliva. Fish sampling in inland lakes: methodological approach and case study, Arreo Lake (Álava). IV Jornadas Ibéricas de Ictiología (2012).

40.
Losos, J. B. Phylogenetic niche conservatism, phylogenetic signal and the relationship between phylogenetic relatedness and ecological similarity among species. Ecol. Lett. 11(10), 995–1003 (2008).
PubMed  Article  Google Scholar 

41.
Pauli, J. N., Steffan, S. A. & Newsome, S. D. It is time for IsoBank. BioScience 65(3), 229–230 (2015).
Article  Google Scholar 

42.
Pauli, J. N. et al. Opinion: Why we need a centralized repository for isotopic data. Proc. Natl. Acad. Sci. 114(12), 2997–3001 (2017).
CAS  PubMed  Article  Google Scholar 

43.
Gratwicke, B. & Marshall, B. E. The relationship between the exotic predators Micropterus salmoides and Serranochromis robustus and native stream fishes in Zimbabwe. J. Fish Biol. 58(1), 68–75 (2001).
Article  Google Scholar 

44.
Maezono, Y. & Miyashita, T. Community-level impacts induced by introduced largemouth bass and bluegill in farm ponds in Japan. Biol. Conserv. 109(1), 111–121 (2003).
Article  Google Scholar 

45.
Yonekura, R., Kita, M. & Yuma, M. Species diversity in native fish community in Japan: comparison between non-invaded and invaded ponds by exotic fish. Ichthyol. Res. 51(2), 176–179 (2004).
Article  Google Scholar 

46.
Maezono, Y., Kobayashi, R., Kusahara, M. & Miyashita, T. Direct and indirect effects of exotic bass and bluegill on exotic and native organisms in farm ponds. Ecol. Appl. 15(2), 638–650 (2005).
Article  Google Scholar 

47.
Almeida, D., Gomes-Lopes, A., Muñoz-López, M., Merino-Aquirre, R. & Miranda, R. Ecología de la agresión interespecífica en el pez sol Lepomis gibbosus y efectos sobre la fauna autóctona. In Posters from the Symposium on non-native freshwater species introduction in the Iberian Peninsula, Pamplona, Spain. http://www.unav.es/centro/especiesinvasoras/ (2009).

48.
Froese, R., & Pauly, D. (2010). www.FishBase.de. Accessed November 19th, 2019.

49.
Oficialdegui, F. J., Sánchez, M. I. & Clavero, M. One century away from home: how the red swamp crayfish took over the world. Rev. Fish Biol. Fish. 1–15 (2020).

50.
Fletcher, A. R., Morison, A. K. & Hume, D. J. Effects of carp, Cyprinus carpio L., on communities of aquatic vegetation and turbidity of waterbodies in the lower Goulburn River basin. Mar. Freshw. Res. 36(3), 311–327 (1985).
Article  Google Scholar 

51.
Pompei, L., Franchi, E., Giannetto, D. & Lorenzoni, M. Growth and reproductive properties of Tench, Tinca tinca Linnaeus, 1758 in Trasimeno Lake (Umbria, Italy). Knowl. Manag. Aquat. Ecosyst. 406 (2012).

52.
Angeler, D. G., Sánchez-Carrillo, S., García, G. & Alvarez-Cobelas, M. The influence of Procambarus clarkii (Cambaridae, Decapoda) on water quality and sediment characteristics in a Spanish floodplain wetland. Hydrobiologia 464(1–3), 89–98 (2001).
Article  Google Scholar 

53.
Jastrebski, C. J. & Robinson, B. W. Natural selection and the evolution of replicated trophic polymorphisms in pumpkinseed sunfish (Lepomis gibbosus). Evol. Ecol. Res. 6(2), 285–305 (2004).
Google Scholar 

54.
Gherardi, F. & Barbaresi, S. Feeding opportunism of the red swamp crayfish Procambarus clarkii, an invasive species. Freshw. Crayfish 16, 77–85 (2008).
Google Scholar 

55.
Wolfram-Wais, A., Wolfram, G., Auer, B., Mikschi, E. & Hain, A. Feeding habits of two introduced fish species (Lepomis gibbosus, Pseudorasbora parva) in Neusiedler See (Austria), with special reference to chironomid larvae (Diptera: Chironomidae). Shallow Lakes 98, 123–129 (1999).
Article  Google Scholar 

56.
Fell, P. E. et al. Does invasion of oligohaline tidal marshes by reed grass, Phragmites australis (Cav.) Trin. ex Steud., affect the availability of prey resources for the mummichog, Fundulus heteroclitus L.?. J. Exper. Mar. Biol. Ecol. 222(1–2), 59–77 (1998).
Article  Google Scholar 

57.
Bedford, A. P. & Powell, I. Long-term changes in the invertebrates associated with the litter of Phragmites australis in a managed reedbed. Hydrobiologia 549(1), 267–285 (2005).
Article  Google Scholar 

58.
Chambers, R. M., Meyerson, L. A. & Saltonstall, K. Expansion of Phragmites australis into tidal wetlands of North America. Aquat. Bot. 64(3–4), 261–273 (1999).
Article  Google Scholar 

59.
Gratton, C. & Denno, R. F. Restoration of arthropod assemblages in a Spartina salt marsh following removal of the invasive plant Phragmites australis. Restoration Ecology. 13(2), 358–372 (2005).
Article  Google Scholar 

60.
Gherardi, F. et al. A review of allodiversity in Lake Naivasha, Kenya: developing conservation actions to protect East African lakes from the negative impacts of non-native species. Biol. Conserv. 144(11), 2585–2596 (2011).
Article  Google Scholar 

61.
Stiers, I., Crohain, N., Josens, G. & Triest, L. Impact of three aquatic invasive species on native plants and macroinvertebrates in temperate ponds. Biol. Invasions 13(12), 2715–2726 (2011).
Article  Google Scholar 

62.
Barbaresi, S., Tricarico, E. & Gherardi, F. Factors inducing the intense burrowing activity of the red-swamp crayfish, Procambarus clarkii, an invasive species. Naturwissenschaften 91(7), 342–345 (2004).
ADS  CAS  PubMed  Article  Google Scholar 

63.
Britton, J. R. et al. From introduction to fishery dominance: the initial impacts of the invasive carp Cyprinus carpio in Lake Naivasha, Kenya, 1999 to 2006. J. Fish Biol. 71, 239–257. https://doi.org/10.1111/j.1095-8649.2007.01669.x (2007).
Article  Google Scholar 

64.
Anton-Pardo, M., Hlaváč, D., Másílko, J., Hartman, P. & Adámek, Z. Natural diet of mirror andscaly carp (Cyprinus carpio) phenotypes in earth ponds. Folia Zool. 63, 229–237. https://doi.org/10.25225/fozo.v63.i4.a1.2014 (2014).
Article  Google Scholar 

65.
Hauser, C. E. & McCarthy, M. A. Streamlining ‘search and destroy’: cost-effective surveillance for invasive species management. Ecol. Lett. 12(7), 683–692 (2009).
PubMed  Article  Google Scholar 

66.
Rinella, M. J., Maxwell, B. D., Fay, P. K., Weaver, T. & Sheley, R. L. Control effort exacerbates invasive-species problem. Ecol. Appl. 19(1), 155–162 (2009).
PubMed  Article  Google Scholar 

67.
Jourdan, J. et al. Reintroduction of freshwater macroinvertebrates: challenges and opportunities. Biol. Rev. 94(2), 368–387 (2019).
PubMed  Article  Google Scholar 

68.
Haase, P., & Pilotto, F. A method for the reintroduction of entire benthic invertebrate communities in formerly degraded streams. Limnologica, 77, 125689 (2019).
Article  Google Scholar 

69.
Feunteun, E. Management and restoration of European eel population (Anguilla anguilla): an impossible bargain. Ecol. Eng. 18(5), 575–591 (2002).
Article  Google Scholar 

70.
Clavero, M. & Hermoso, V. Historical data to plan the recovery of the European eel. J. Appl. Ecol. 52(4), 960–968 (2015).
Article  Google Scholar 

71.
Benndorf, J. Possibilities and limits for controlling eutrophication by biomanipulation. Int. Rev. Hydrobiol. 80, 519–534. https://doi.org/10.1002/iroh.19950800404 (1995).
CAS  Article  Google Scholar 

72.
Aquiloni, L. et al. Biological control of invasive populations of crayfish: the European eel (Anguilla anguilla) as a predator of Procambarus clarkii. Biol. Invasions 12, 3817–3824. https://doi.org/10.1007/s10530-010-9774-z (2010).
Article  Google Scholar 

73.
McCord JW American eel. South Carolina State Documents Depository (2005)

74.
Schiphouwer, M. E. et al. Risk assessment of the alien smallmouth bass (Micropterusdolomieu). Rep. Environ. Sci. 527, 1–60 (2017).
Google Scholar 

75.
Costantini, M. L. et al. The role of alien fish (the centrarchid Micropterus salmoides) in lake food webs highlighted by stable isotope analysis. Freshw. Biol. 63, 1130–1142. https://doi.org/10.1111/fwb.13122 (2018).
CAS  Article  Google Scholar 

76.
Laffaille, P., Caraguel, J. M. & Legault, A. Temporal patterns in the upstream migration of European glass eels (Anguilla anguilla) at the Couesnon estuarine dam. Estuarine Coast. Shelf Sci. 73(1–2), 81–90 (2007).
ADS  Article  Google Scholar 

77.
Prigge, E. Factors challenging the European eel (Anguilla anguilla) stock recovery in continental waters (Doctoral dissertation, Christian-Albrechts Universität Kiel) (2013).

78.
Catford, J. A., Jansson, R. & Nilsson, C. Reducing redundancy in invasion ecology by integrating hypotheses into a single theoretical framework. Divers. Distrib. 15(1), 22–40 (2009).
Article  Google Scholar 

79.
Marchi, M. et al. Resistance and re-organization of an ecosystem in response to biological invasion: some hypotheses. Ecol. Modell. 222(16), 2992–3001 (2011).
Article  Google Scholar 

80.
Martínez-Torres, L., Gonzáles-Tapia, J. R. & Ramóm-Luch, C. Batimetría y propuesta de cartografía geológica del lago de Arreo (Diapiro de salinas de Añana, Álava) Eusko Jkaskuntza. Cuadernos de Sección. Historia 20, 123–134 (1992).
Google Scholar 

81.
Camacho, A., Borja, C., Valero-Garcés, B., Sahuquillo, M., Cirujano, S., Soria, J. M., Rico, E., De la Hera, A., Santamans, A. C., García deDomingo, A., Chicote, A. & Gosálvez, R. U. 3190 Lagos ylagunas kársticas sobre yesos. In: Ministerio de Medio Ambiente,y Medio Rural y Marino Bases ecológicas preliminares para laconservación de los tipos de hábitat de interés comunitario en España. Madrid, Spain, 37 pp (2009).

82.
Vitoria-Gasteiz, L. Biodiversity Strategy of the Basque Autonomous Community 2030 and First Action Plan 2020; Servicio Central de Publicaciones del Gobierno Vasco (2016).

83.
Choi, W. J., Ro, H. M. & Chang, S. X. Carbon isotope composition of Phragmites australis in a constructed saline wetland. Aquat. Bot. 82(1), 27–38 (2005).
Article  Google Scholar 

84.
Bergamino, L., Dalu, T. & Richoux, N. B. Evidence of spatial and temporal changes in sources of organic matter in estuarine sediments: stable isotope and fatty acid analyses. Hydrobiologia 732(1), 133–145 (2014).
CAS  Article  Google Scholar 

85.
Kullman, M. A., Kidd, K. A., Podemski, C. L., Paterson, M. J. & Blanchfield, P. J. Assimilation of freshwater salmonid aquaculture waste by native aquatic biota. Can. J. Fish. Aquat. Sci. 66(11), 1965–1975 (2009).
CAS  Article  Google Scholar 

86.
Tonn, W. M., Klatt, P. H., Paszkowski, C. A., Gingras, B. A. & Wilcox, K. Trophic Relations of the Red-Necked Grebe on Lakes in the Western Boreal Forest: A Stable-Isotope Analysis (2004).

87.
Jardine, T. D. et al. Understanding and overcoming baseline isotopic variability in running waters. River Res. Appl. 30(2), 155–165 (2014).
Article  Google Scholar 

88.
Tran, T. N. Q., Jackson, M. C., Sheath, D., Verreycken, H. & Britton, J. R. Patterns of trophic niche divergence between invasive and native fishes in wild communities are predictable from mesocosm studies. J. Anim. Ecol. 84(4), 1071–1080 (2015).
PubMed  PubMed Central  Article  Google Scholar 

89.
Dörner, H. et al. Piscivory and trophic position of Anguilla anguilla in two lakes: importance of macrozoobenthos density. J. Fish Biol. 74(9), 2115–2131 (2009).
PubMed  Article  Google Scholar 

90.
Quezada-Romegialli, C. et al. tRophicPosition, an R package for the Bayesian estimation of trophic position from consumer stable isotope ratios. Methods Ecol. Evol. 9(6), 1592–1599 (2018).
Article  Google Scholar 

91.
Layman, C. A. et al. Applying stable isotopes to examine food-web structure: an overview of analytical tools. Biol. Rev. 87(3), 545–562 (2012).
PubMed  Article  Google Scholar 

92.
Layman, C. A., Arrington, D. A., Montaña, C. G. & Post, D. M. Can stable isotope ratios provide for community-wide measures of trophic structure?. Ecology 88(1), 42–48 (2007).
Article  Google Scholar 

93.
Swanson, H. K. et al. A new probabilistic method for quantifying n-dimensional ecological niches and niche overlap. Ecology 96(2), 318–324 (2015).
PubMed  Article  Google Scholar  More

1.
Ruxton, G. D., Allen, W. L., Sherratt, T. N. & Speed, M. P. Avoiding Attack. The Evolutionary Ecology of Crypsis, Aposematism, and Mimicry (Oxford University Press, Oxford, 2018).
Google Scholar 
2.
Skelhorn, J., Rowland, H. M. & Ruxton, G. D. The evolution and ecology of masquerade. Biol. J. Linn. Soc. 99, 1–8 (2010).
Article  Google Scholar 

3.
Stevens, M. & Merilaita, S. Animal Camouflage: Mechanisms and Function (Cambridge University Press, Cambridge, 2011).
Google Scholar 

4.
Stevens, M. & Ruxton, G. D. The key role of behaviour in animal camouflage. Biol. Rev. 94, 116–134. https://doi.org/10.1111/brv.12438 (2019).
Article  Google Scholar 

5.
Stevens, M., Troscianko, J., Wilson-Aggarwal, J. K. & Spottiswoode, C. N. Improvement of individual camouflage through background choice in ground-nesting birds. Nat. Ecol. Evol. 1, 1325–1333. https://doi.org/10.1038/s41559-017-0256-x (2017).
Article  PubMed  PubMed Central  Google Scholar 

6.
Lovell, P. G., Ruxton, G. D., Langridge, K. V. & Spencer, K. A. Egg-laying substrate selection for optimal camouflage by quail. Curr. Biol. 23, 260–264. https://doi.org/10.1016/j.cub.2012.12.031 (2013).
CAS  Article  PubMed  Google Scholar 

7.
Sargent, T. D. Background selections of geometrid and noctuid moths. Science 154, 1674. https://doi.org/10.1126/science.154.3757.1674 (1966).
ADS  Article  Google Scholar 

8.
Kang, C. K., Moon, J. Y., Lee, S. I. & Jablonski, P. G. Camouflage through an active choice of a resting spot and body orientation in moths. J. Evol. Biol. 25, 1695–1702 (2012).
Article  Google Scholar 

9.
Skelhorn, J., Rowland, H. M., Delf, J., Speed, M. P. & Ruxton, G. D. Density-dependent predation influences the evolution and behavior of masquerading prey. Proc. Natl. Acad. Sci. U.S.A. 108, 6532–6536. https://doi.org/10.1073/pnas.1014629108 (2011).
ADS  Article  PubMed  PubMed Central  Google Scholar 

10.
Eacock, A. et al. Adaptive colour change and background choice behaviour in peppered moth caterpillars is mediated by extraocular photoreception. Commun. Biol. 2, 286. https://doi.org/10.1038/s42003-019-0502-7 (2019).
Article  PubMed  PubMed Central  Google Scholar 

11.
Skelhorn, J. et al. Size-dependent misclassification of masquerading prey. Behav. Ecol. 21, 1344–1348. https://doi.org/10.1093/beheco/arq159 (2010).
Article  Google Scholar 

12.
Eacock, A., Rowland, H. M., Edmonds, N. & Saccheri, I. J. Colour change of twig-mimicking peppered moth larvae is a continuous reaction norm that increases camouflage against avian predators. PeerJ 5, e3999. https://doi.org/10.7717/peerj.3999 (2017).
CAS  Article  PubMed  PubMed Central  Google Scholar 

13.
Ruxton, G. D. & Stevens, M. The evolutionary ecology of decorating behaviour. Biol. Lett. 11, 20150325. https://doi.org/10.1098/rsbl.2015.0325 (2015).
Article  PubMed  PubMed Central  Google Scholar 

14.
Liu, M., Blamires, S. J., Liao, C. & Min Tso, I. Evidence of bird dropping masquerading by a spider to avoid predators. Sci. Rep. 4, 5058. https://doi.org/10.1038/srep05058 (2014).
CAS  Article  PubMed  PubMed Central  Google Scholar 

15.
Konstantinov, A. S., Prathapan, K. D. & Vencl, F. V. Hiding in plain sight: leaf beetles (Chrysomelidae: Galerucinae) use feeding damage as a masquerade decoy. Biol. J. Linn. Soc. 123, 311–320. https://doi.org/10.1093/biolinnean/blx149 (2018).
Article  Google Scholar 

16.
Poulton, E. B. The Colours of Animals: Their Meaning and Use. Especially Considered in the Case of Insects (Kegan Paul, Trench Trubner & Co, London, 1890).
Google Scholar 

17.
Cott, H. B. Adaptive Coloration in Animals (Methuen, London, 1940).
Google Scholar 

18.
Cooper, W. E. J. & Sherbrooke, W. C. Choosing between a rock and a hard place: camouflage in the round-tailed horned lizard Phrynosoma modestum. Curr. Zool. 58, 541–548 (2012).
Article  Google Scholar 

19.
Pianka, E. R. Lizards: Windows to the Evolution of Diversity (University of California Press, Berkeley, 2006).
Google Scholar 

20.
Zhang, S. et al. Crypsis via leg clustering: twig masquerading in a spider. R. Soc. Open Sci. 2, 150007. https://doi.org/10.1098/rsos.150007 (2015).
ADS  Article  PubMed  PubMed Central  Google Scholar 

21.
Skelhorn, J. Masquerade. Curr. Biol. 25, R643–R644. https://doi.org/10.1016/j.cub.2015.02.069 (2015).
CAS  Article  PubMed  Google Scholar 

22.
Cestari, C., Gonçalves, C. S. & Sazima, I. Use flexibility of perch types by the branch-camouflaged Common Potoo (Nyctibius griseus): why this bird may occasionally dare to perch on artificial substrates. Wilson J. Ornithol. 130, 191–199 (2018).
Article  Google Scholar 

23.
Hanlon, R. T., Forsythe, J. W. & Joneschild, D. E. Crypsis, conspicuousness, mimicry and polyphenism as antipredator defences of foraging octopuses on Indo-Pacific coral reefs, with a method of quantifying crypsis from video tapes. Biol. J. Linn. Soc. 66, 1–22 (1999).
Article  Google Scholar 

24.
Barbosa, A., Allen, J. J., Mäthger, L. M. & Hanlon, R. T. Cuttlefish use visual cues to determine arm postures for camouflage. Proc. Biol. Sci. 279, 84–90. https://doi.org/10.1098/rspb.2011.0196 (2012).
Article  PubMed  Google Scholar 

25.
Panetta, D., Buresch, K. & Hanlon, R. T. Dynamic masquerade with morphing three-dimensional skin in cuttlefish. Biol. Lett. https://doi.org/10.1098/rsbl.2017.0070 (2017).
Article  PubMed  PubMed Central  Google Scholar 

26.
Suzuki, T. N. & Sakurai, R. Bent posture improves the protective value of bird dropping masquerading by caterpillars. Anim. Behav. 105, 79–84. https://doi.org/10.1016/j.anbehav.2015.04.009 (2015).
Article  Google Scholar 

27.
Dockery, M., Meneely, J. & Costen, P. Avoiding detection by predators: the tactics used by Biston betularia larvae. Br. J. Entomol. Nat. Hist. 22, 247–253 (2009).
Google Scholar 

28.
Galler, S., Litzlbauer, J., Kröss, M. & Grassberger, H. The highly efficient holding function of the mollusc catch muscle is not based on decelerated myosin head cross-bridge cycles. Proc. R. Soc. B Biol. Sci. 277, 803–808. https://doi.org/10.1098/rspb.2009.1618 (2010).
Article  Google Scholar 

29.
Gally, M., Silva, A. S. F. L. & Zina, J. Death feigning in Physalaemus kroyeri (Reinhardt and Lütken, 1862) (Anura, Leiuperidae). Herpetol. Notes 5, 133–135 (2012).
Google Scholar 

30.
Levesque, K. R., Levesque, K. R., Fortin, M. & Mauffette, Y. Temperature and food quality effects on growth, consumption and post-ingestive utilization efficiencies of the forest tent caterpillar Malacosoma disstria (Lepidoptera: Lasiocampidae). Bull. Entomol. Res. 92, 127–136. https://doi.org/10.1079/ber2002153 (2002).
CAS  Article  PubMed  Google Scholar 

31.
Skelhorn, J., Rowland, H. M., Speed, M. P. & Ruxton, G. D. Masquerade: camouflage without crypsis. Science 327, 51 (2010).
ADS  CAS  Article  Google Scholar 

32.
Skelhorn, J. & Ruxton, G. D. Mimicking multiple models: polyphenetic masqueraders gain additional benefits from crypsis. Behav. Ecol. 22, 60–65. https://doi.org/10.1093/beheco/arq166 (2011).
Article  Google Scholar 

33.
Skelhorn, J. & Ruxton, G. D. Context-dependent misclassification of masquerading prey. Evol. Ecol. 25, 751–761. https://doi.org/10.1007/s10682-010-9435-9 (2011).
Article  Google Scholar 

34.
Ewert, J. P. The neural basis of visually guided behavior. Sci. Am. 230, 34–42. https://doi.org/10.1038/scientificamerican0374-34 (1974).
CAS  Article  PubMed  Google Scholar 

35.
Scholl, B. J. Objects and attention: the state of the art. Cognition 80, 1–46. https://doi.org/10.1016/S0010-0277(00)00152-9 (2001).
CAS  Article  PubMed  Google Scholar 

36.
Miller, C. T. & Bee, M. A. Receiver psychology turns 20: Is it time for a broader approach?. Anim. Behav. 83, 331–343. https://doi.org/10.1016/j.anbehav.2011.11.025 (2012).
Article  PubMed  Google Scholar 

37.
Snowden, R., Thompson, P. & Troscianko, T. Basic Vision: An Introduction to Visual Perception (Oxford University Press, Oxford, 2012).
Google Scholar 

38.
Opell, B. D. & Eberhard, W. G. Resting postures of orb-weaving uloborid spiders (Araneae, Uloboridae). J. Arachnol. 11, 369–376 (1983).
Google Scholar 

39.
Skelhorn, J. & Ruxton, G. D. Size-dependent microhabitat selection by masquerading prey. Behav. Ecol. 24, 89–97 (2012).
Article  Google Scholar 

40.
Hill, G. E. & McGraw, K. J. Bird Coloration, Volume 1: Mechanisms and Measurements (Harvard University Press, Cambridge, 2006).
Google Scholar 

41.
Bates, D., Mächler, M., Bolker, B. & Walker, S. Fitting linear mixed-effects models using lme4. J. Stat. Softw. 67, 1–48 (2015).
Article  Google Scholar 

42.
Brooks, M. E. et al. glmmTMB balances speed and flexibility among packages for zero-inflated generalized linear mixed modeling. R J. 9, 378–400 (2017).
Article  Google Scholar 

43.
Boggs, C. L. & Niitepõld, K. Effects of larval dietary restriction on adult morphology, with implications for flight and life history. Entomol. Exp. Appl. 159, 189–196. https://doi.org/10.1111/eea.12420 (2016).
Article  Google Scholar 

44.
Johnson, H., Solensky, M. J., Satterfield, D. A. & Davis, A. K. Does skipping a meal matter to a butterfly’s appearance? Effects of larval food stress on wing morphology and color in monarch butterflies. PLoS ONE 9, e93492. https://doi.org/10.1371/journal.pone.0093492 (2014).
ADS  CAS  Article  PubMed  PubMed Central  Google Scholar 

45.
Kingsolver, J. G., Shlichta, J. G., Ragland, G. J. & Massie, K. R. Thermal reaction norms for caterpillar growth depend on diet. Evol. Ecol. Res. 8, 703–715 (2006).
Google Scholar 

46.
Grayson, J., Edmunds, M., Evans, E. H. & Britton, G. Carotenoids and colouration of poplar hawkmoth caterpillars (Laothoe populi). Biol. J. Linn. Soc. 42, 457–465. https://doi.org/10.1111/j.1095-8312.1991.tb00574.x (1991).
Article  Google Scholar 

47.
Core Team, R. A Language and Environment for Statistical Computing (R Foundation for Statistical Computing, Vienna, Austria, 2019).
Google Scholar 

48.
Guidelines for the treatment of animals in behavioural research and teaching. Anim. Behav. 159, 1-XI, https://doi.org/10.1016/j.anbehav.2019.11.002 (2020)

49.
U. K. Government, Guidance to the operation of the Animals (Scientific Procedures) 1986. ScotPIL manual—avian species. (2009). More

1.
Tisdale, H. The process of urbanization. Soc. Forces 20, 311–316 (1942).
Article  Google Scholar 
2.
McKinney, M. L. Urbanization, biodiversity, and conservation. Bioscience 52, 883–890 (2002).
Article  Google Scholar 

3.
Grimm, N. B. et al. Global change and the ecology of cities. Science 319, 756–760 (2008).
ADS  CAS  PubMed  Article  PubMed Central  Google Scholar 

4.
Johnson, M. T. J. & Munshi-South, J. Evolution of life in urban environments. Science 358, eaam8327 (2017).
PubMed  Article  CAS  PubMed Central  Google Scholar 

5.
Turrini, T., Sanders, D. & Knop, E. Effects of urbanization on direct and indirect interactions in a tri-trophic system. Ecol. Appl. 26, 664–675 (2016).
Article  Google Scholar 

6.
Theodorou, P. et al. Genome-wide single nucleotide polymorphism scan suggests adaptation to urbanization in an important pollinator, the red-tailed bumblebee (Bombus lapidarius L.). Proc. R. Soc. B Biol. Sci. 285, 20172806 (2018).
Article  Google Scholar 

7.
Thompson, K. A., Renaudin, M. & Johnson, M. T. J. Urbanization drives the evolution of parallel clines in plant populations. Proc. R. Soc. B Biol. Sci. 283, 20162180 (2016).
Article  Google Scholar 

8.
Theodorou, P., Baltz, L. M., Paxton, R. J. & Soro, A. Urbanisation is associated with shifts in bumblebee body size, with cascading effects on pollination. Evol. Appl. 10, 1–16 (2020).
Google Scholar 

9.
Ollerton, J., Winfree, R. & Tarrant, S. How many flowering plants are pollinated by animals?. Oikos 120, 321–326 (2011).
Article  Google Scholar 

10.
Potts, S. G., Vulliamy, B., Dafni, A., Nee’man, G. & Willmer, P. Linking bees and flowers: how do floral communities structure pollinator communities?. Ecology 84, 2628–2642 (2003).
Article  Google Scholar 

11.
Steffan-Dewenter, I. & Tscharntke, T. Succession of bee communities on fallows. Ecography 24, 83–93 (2001).
Article  Google Scholar 

12.
Fründ, J., Linsenmair, K. E. & Blüthgen, N. Pollinator diversity and specialization in relation to flower diversity. Oikos 119, 1581–1590 (2010).
Article  Google Scholar 

13.
Ebeling, A., Klein, A. M., Schumacher, J., Weisser, W. W. & Tscharntke, T. How does plant richness affect pollinator richness and temporal stability of flower visits?. Oikos 117, 1808–1815 (2008).
Article  Google Scholar 

14.
Theodorou, P. et al. The structure of flower visitor networks in relation to pollination across an agricultural to urban gradient. Funct. Ecol. 31, 838–847 (2017).
Article  Google Scholar 

15.
Biesmeijer, J. C. et al. Parallel declines in pollinators and insect-pollinated plants in Britain and the Netherlands. Science 313, 351–354 (2006).
ADS  CAS  PubMed  Article  PubMed Central  Google Scholar 

16.
Ghazoul, J. Floral diversity and the facilitation of pollination. J. Ecol. 94, 295–304 (2006).
Article  Google Scholar 

17.
Clough, Y. et al. Density of insect-pollinated grassland plants decreases with increasing surrounding land-use intensity. Ecol. Lett. 17, 1168–1177 (2014).
PubMed  Article  PubMed Central  Google Scholar 

18.
Lundgren, R., Totland, Ø. & Lázaro, A. Experimental simulation of pollinator decline causes community-wide reductions in seedling diversity and abundance. Ecology 97, 1420–1430 (2016).
PubMed  Article  PubMed Central  Google Scholar 

19.
Papanikolaou, A. D. et al. Wild bee and floral diversity co-vary in response to the direct and indirect impacts of land use. Ecosphere 8, e02008 (2017).
Article  Google Scholar 

20.
Brosi, B. J. & Briggs, H. M. Single pollinator species losses reduce floral fidelity and plant reproductive function. Proc. Natl. Acad. Sci. U. S. A. 110, 13044–13048 (2013).
ADS  CAS  PubMed  PubMed Central  Article  Google Scholar 

21.
Vázquez, D. P., Blüthgen, N., Cagnolo, L. & Chacoff, N. P. Uniting pattern and process in plant–animal mutualistic networks: a review. Ann. Bot. 103, 1445–1457 (2009).
PubMed  PubMed Central  Article  Google Scholar 

22.
Albrecht, J. et al. Plant and animal functional diversity drive mutualistic network assembly across an elevational gradient. Nat. Commun. 9, 3177 (2018).
ADS  PubMed  PubMed Central  Article  CAS  Google Scholar 

23.
Kremen, C. et al. Pollination and other ecosystem services produced by mobile organisms: a conceptual framework for the effects of land-use change. Ecol. Lett. 10, 299–314 (2007).
PubMed  Article  Google Scholar 

24.
Harrison, T. & Winfree, R. Urban drivers of plant-pollinator interactions. Funct. Ecol. 29, 879–888 (2015).
Article  Google Scholar 

25.
Baldock, K. C. R. et al. Where is the UK’s pollinator biodiversity? The importance of urban areas for flower-visiting insects. Proc. R. Soc. B Biol. Sci. 282, 20142849 (2015).
Article  Google Scholar 

26.
Bates, A. J. et al. Changing bee and hoverfly pollinator assemblages along an urban–rural gradient. PLoS ONE 6, e23459 (2011).
ADS  CAS  PubMed  PubMed Central  Article  Google Scholar 

27.
Fortel, L. et al. Decreasing abundance, increasing diversity and changing structure of the wild bee community (Hymenoptera: Anthophila) along an urbanization gradient. PLoS ONE 9, e104679 (2014).
ADS  PubMed  PubMed Central  Article  CAS  Google Scholar 

28.
Theodorou, P. et al. Urban areas as hotspots for bees and pollination but not a panacea for all insects. Nat. Commun. 11, 576 (2020).
ADS  CAS  PubMed  PubMed Central  Article  Google Scholar 

29.
Buchholz, S., Gathof, A. K., Grossmann, A. J., Kowarik, I. & Fischer, L. K. Wild bees in urban grasslands: urbanisation, functional diversity and species traits. Landsc. Urban Plan. 196, 103731 (2020).
Article  Google Scholar 

30.
Hung, K. J., Ascher, J. S., Davids, J. A. & Holway, D. A. Ecological filtering in scrub fragments restructures the taxonomic and functional composition of native bee assemblages. Ecology 100, e02654 (2019).
PubMed  Article  PubMed Central  Google Scholar 

31.
Buchholz, S. & Egerer, M. H. Functional ecology of wild bees in cities: towards a better understanding of trait-urbanization relationships. Biodivers. Conserv. 29, 2779–2801 (2020).
Article  Google Scholar 

32.
Cane, J. H., Minckley, R. L., Kervin, L. J., Roulston, T. H. & Williams, N. M. Complex responses within a desert bee guild (Hymenoptera: Apiformes) to urban habitat fragmentation. Ecol. Appl. 16, 632–644 (2006).
PubMed  Article  PubMed Central  Google Scholar 

33.
Banaszak-Cibicka, W. & Żmihorski, M. Wild bees along an urban gradient: winners and losers. J. Insect Conserv. 16, 331–343 (2011).
Article  Google Scholar 

34.
Neame, L. A., Griswold, T. & Elle, E. Pollinator nesting guilds respond differently to urban habitat fragmentation in an oak-savannah ecosystem. Insect Conserv. Divers. 6, 57–66 (2013).
Article  Google Scholar 

35.
Fitch, G. et al. Does urbanization favour exotic bee species? Implications for the conservation of native bees in cities. Biol. Lett. 15, 20190574 (2019).
PubMed  PubMed Central  Article  Google Scholar 

36.
Knapp, S., Kühn, I., Schweiger, O. & Klotz, S. Challenging urban species diversity: contrasting phylogenetic patterns across plant functional groups in Germany. Ecol. Lett. 11, 1054–1064 (2008).
PubMed  Article  PubMed Central  Google Scholar 

37.
Kühn, I., Brandl, R. & Klotz, S. The flora of German cities is naturally species rich. Evol. Ecol. Res. 6, 749–764 (2004).
Google Scholar 

38.
Knapp, S., Winter, M. & Klotz, S. Increasing species richness but decreasing phylogenetic richness and divergence over a 320-year period of urbanization. J. Appl. Ecol. 54, 1152–1160 (2016).
Article  Google Scholar 

39.
Lososová, Z. et al. Patterns of plant traits in annual vegetation of man-made habitats in central Europe. Perspect. Plant Ecol. Evol. Syst. 8, 69–81 (2006).
Article  Google Scholar 

40.
Pysek, P. Alien and native species in Central European urban floras: a quantitative comparison. J. Biogeogr. 25, 155–163 (1998).
Article  Google Scholar 

41.
Schleuning, M. et al. Ecological networks are more sensitive to plant than to animal extinction under climate change. Nat. Commun. 7, 13965 (2016).
ADS  CAS  PubMed  PubMed Central  Article  Google Scholar 

42.
Ollerton, J. Pollinator diversity: distribution, ecological function, and conservation. Annu. Rev. Ecol. Evol. Syst. 48, 353–376 (2017).
Article  Google Scholar 

43.
Schleuning, M., Fründ, J. & García, D. Predicting ecosystem functions from biodiversity and mutualistic networks: an extension of trait-based concepts to plant–animal interactions. Ecography 38, 380–392 (2014).
Article  Google Scholar 

44.
Mallinger, R. E., Gaines-Day, H. R. & Gratton, C. Do managed bees have negative effects on wild bees?: A systematic review of the literature. PLoS ONE 12, e0189268 (2017).
PubMed  PubMed Central  Article  CAS  Google Scholar 

45.
Potts, S. G. et al. Role of nesting resources in organising diverse bee communities in a Mediterranean landscape. Ecol. Entomol. 30, 78–85 (2005).
Article  Google Scholar 

46.
Pardee, G. L. & Philpott, S. M. Native plants are the bee’s knees: local and landscape predictors of bee richness and abundance in backyard gardens. Urban Ecosyst. 17, 641–659 (2014).
Article  Google Scholar 

47.
Ballare, K. M., Neff, J. L., Ruppel, R. & Jha, S. Multi-scalar drivers of biodiversity: local management mediates wild bee community response to regional urbanization. Ecol. Appl. 29, e01869 (2019).
PubMed  Article  PubMed Central  Google Scholar 

48.
Torné-Noguera, A. et al. Determinants of spatial distribution in a bee community: nesting resources, flower resources, and body size. PLoS ONE 9, e97255 (2014).
ADS  PubMed  PubMed Central  Article  CAS  Google Scholar 

49.
Baldock, K. C. R. et al. A systems approach reveals urban pollinator hotspots and conservation opportunities. Nat. Ecol. Evol. 3, 363–373 (2019).
PubMed  PubMed Central  Article  Google Scholar 

50.
Fetridge, E. D., Ascher, J. S. & Langellotto, G. A. The bee fauna of residential gardens in a suburb of New York City (Hymenoptera: Apoidea). Ann. Entomol. Soc. Am. 101, 1067–1077 (2008).
Article  Google Scholar 

51.
Stang, M., Klinkhamer, P. G. L. & van der Meijden, E. Size constraints and flower abundance determine the number of interactions in a plant–flower visitor web. Oikos 112, 111–121 (2006).
Article  Google Scholar 

52.
Scolozzi, R. & Geneletti, D. A multi-scale qualitative approach to assess the impact of urbanization on natural habitats and their connectivity. Environ. Impact Assess. Rev. 36, 9–22 (2012).
Article  Google Scholar 

53.
Cheptou, P.-O., Hargreaves, A. L., Bonte, D. & Jacquemyn, H. Adaptation to fragmentation: evolutionary dynamics driven by human influences. Philos. Trans. R. Soc. B Biol. Sci. 372, 2 (2017).
Google Scholar 

54.
Hennig, E. I. & Ghazoul, J. Plant–pollinator interactions within the urban environment. Perspect. Plant Ecol. Evol. Syst. 13, 137–150 (2011).
Article  Google Scholar 

55.
Winfree, R., Aguilar, R., Vázquez, D. P., LeBuhn, G. & Aizen, M. A. A meta-analysis of bees’ responses to anthropogenic disturbance. Ecology 90, 2068–2076 (2009).
PubMed  Article  PubMed Central  Google Scholar 

56.
Quantum GIS Development Team. Quantum GIS Geographic Information System. Open Source Geospatial Foundation Project. Available at: http://qgis.osgeo.org. (2014).

57.
Greenleaf, S. S., Williams, N. M., Winfree, R. & Kremen, C. Bee foraging ranges and their relationship to body size. Oecologia 153, 589–596 (2007).
ADS  PubMed  Article  PubMed Central  Google Scholar 

58.
Westphal, C. et al. Measuring bee diversity in different European habitats and biogeographical regions. Ecol. Monogr. 78, 653–671 (2008).
Article  Google Scholar 

59.
Amiet, F. & Gesellschaft, S. E. Insecta Helvetica. A, Fauna: 12. Hymenoptera. Apidae.-T. 1. Allgemeiner Teil, Gattungsschlüssel, Gattungen Apis, Bombus und Psithyrus. (Musée d’Histoire naturelle, 1996).

60.
Amiet, F., Herrmann, M., Müller, A. & Neumeyer, R. Fauna Helvetica 6. Apidae 3: Halictus, Lasioglossum. Fauna Helv. 6. Apidae 3 Halictus, Lasioglossum (2001).

61.
Amiet, F., Müller, A. & Neumeyer, R. Apidae 2: Colletes, Dufourea, Hylaeus, Nomia, Nomioides, Rhophitoides, Rophites, Sphecodes, Systropha. 4 (Schweizerische Entomologische Gesellschaft, 1999).

62.
Hebert, P. D. N., Cywinska, A., Ball, S. L. & de Waard, J. R. Biological identifications through DNA barcodes. Proc. R. Soc. Lond. B Biol. Sci. 270, 313–321 (2003).
CAS  Article  Google Scholar 

63.
Bäßler, M., Jäger, J. E. & Werner, K. Rothmaler, W. (Begr.): Exkursionsflora von Deutschland. Bd.2: Gefäßpflanzen. 17.Aufl (Berlin: Spektrum, 1999).

64.
Jäger, J. E., Wesche, K., Ritz, C., Müller, F. & Welk, E. Rothmaler – Exkursionsflora von Deutschland, Gefäßpflanzen: Atlasband (Springer-Verlag, 2013).

65.
Westrich, P. Die Wildbienen Deutschlands (Verlag Eugen Ulmer, 2018).

66.
Kattge, J. et al. TRY plant trait database—enhanced coverage and open access. Glob. Change Biol. 26, 119–188 (2020).
ADS  Article  Google Scholar 

67.
Botta-Dukát, Z. Rao’s quadratic entropy as a measure of functional diversity based on multiple traits. J. Veg. Sci. 16, 533–540 (2005).
Article  Google Scholar 

68.
Laliberté, E. & Legendre, P. A distance-based framework for measuring functional diversity from multiple traits. Ecology 91, 299–305 (2010).
PubMed  Article  Google Scholar 

69.
Rader, R., Bartomeus, I., Tylianakis, J. M. & Lalibert, E. The winners and losers of land use intensification: pollinator community disassembly is non-random and alters functional diversity. Divers. Distrib. 20, 908–917 (2014).
Article  Google Scholar 

70.
Faith, D. P. Conservation evaluation and phylogenetic diversity. Biol. Conserv. 61, 1–10 (1992).
Article  Google Scholar 

71.
Bartoń, K. MuMIn: Multi-Model Inference. R package version 1.15.1 (2013).

72.
Burnham, K. P. & Anderson, D. R. Multimodel inference. Sociol. Methods Res. 33, 261–304 (2004).
MathSciNet  Article  Google Scholar 

73.
Bates, D., Mächler, M., Bolker, B. & Walker, S. Fitting linear mixed-effects models using lme4. J. Stat. Softw. 67, 1–48 (2015).
Article  Google Scholar 

74.
Legendre, P., Galzin, R. & Harmelin-Vivien, M. L. Relating behavior to habitat: solutions to the fourth-corner problem. Ecology 78, 547–562 (1997).
Google Scholar 

75.
Wang, Y., Naumann, U., Eddelbuettel, D., Wilshire, J. & Warton, D. mvabund: Statistical Methods for Analysing Multivariate Abundance Data. R package version 4.1.3 (2020).

76.
Lefcheck, J. S. piecewiseSEM: piecewise structural equation modelling in r for ecology, evolution, and systematics. Methods Ecol. Evol. 7, 573–579 (2016).
Article  Google Scholar 

77.
Shipley, B. Confirmatory path analysis in a generalized multilevel context. Ecology 90, 363–368 (2009).
PubMed  Article  PubMed Central  Google Scholar 

78.
Sobel, M. E. Sociological methodology. In: Sociological Methodology (ed. Leinhart, S.) 290–312 (1982).

79.
Zuur, A., Ieno, E. N., Walker, N., Saveliev, A. A. & Smith, G. M. Mixed effects models and extensions in ecology with R (Springer, New York, 2009).
Google Scholar 

80.
Paradis, E., Claude, J. & Strimmer, K. APE: analyses of phylogenetics and evolution in R language. Bioinformatics 20, 289–290 (2004).
CAS  PubMed  Article  PubMed Central  Google Scholar 

81.
R Core Team. R: A Language and Environment for Statistical Computing. R Foundation for Statistical Computing. http://www.r-project.org (2016). More