Ecology

1.Brussaard, L., Ruiter, P. C. & Brown, G. G. Soil biodiversity for agricultural sustainability. Agric. Ecosyst. Environ. 121, 233–244 (2007).Article 

Google Scholar 
2.Nielsen, U. N., Wall, D. H. & Six, J. Soil biodiversity and the environment. Annu. Rev. Environ. Resour. 40, 63–90 (2015).Article 

Google Scholar 
3.El Mujtar, V., Muñoz, N., Mc Cormick, B. P., Pulleman, M. & Tittonell, P. Role and management of soil biodiversity for food security and nutrition; where do we stand?. Glob. Food Secur. 20, 132–144 (2019).Article 

Google Scholar 
4.Bardgett, R. D. & Putten, W. H. Belowground biodiversity and ecosystem functioning. Nature 515, 505–511 (2014).ADS 
CAS 
PubMed 
Article 

Google Scholar 
5.Kardol, P. & De Long, J. R. How anthropogenic shifts in plant community composition alter soil food webs. F1000Res 7, 4 (2018).PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
6.Smith, P. et al. Global change pressures on soils from land-use and management. Glob. Change Biol. 22, 1008–1028 (2016).ADS 
Article 

Google Scholar 
7.Geisen, S. et al. A methodological framework to embrace soil biodiversity. Soil Biol. Biochem. 136, 107536 (2019).CAS 
Article 

Google Scholar 
8.Creamer, R. E. et al. Ecological network analysis reveals the inter-connection between soil biodiversity and ecosystem function as affected by land use across Europe. Appl. Soil. Ecol. 97, 112–124 (2016).Article 

Google Scholar 
9.Tsiafouli, M. A. et al. Intensive agriculture reduces soil biodiversity across Europe. Glob. Change Biol. 21, 973–985 (2015).ADS 
Article 

Google Scholar 
10.de Vries, F. T. et al. Soil food web properties explain ecosystem services across European land use systems. PNAS 110, 14296–14301 (2013).ADS 
PubMed 
PubMed Central 
Article 

Google Scholar 
11.Ponge, J. F. et al. Collembolan communities as bioindicators of land-use intensification. Soil Biol. Biochem. 35, 813–826 (2003).CAS 
Article 

Google Scholar 
12.Postma-Blaauw, M. B., de Goede, R. G. M., Bloem, J., Faber, J. H. & Brussaard, L. Soil biota community structure and abundance under agricultural intensification and extensification. Ecology 91, 460–473 (2010).PubMed 
Article 
PubMed Central 

Google Scholar 
13.Decaëns, T. & Jiménez, J. J. Earthworm communities under an agricultural intensification gradient in Colombia. Plant Soil 240, 133–143 (2002).Article 

Google Scholar 
14.Dequiedt, S. et al. Biogeographical patterns of soil molecular microbial biomass as influenced by soil characteristics and management. Globa. Ecol. Biogeogr. 20, 641–652 (2011).Article 

Google Scholar 
15.Thomson, B. C. et al. Soil conditions and land-use intensification effects on soil microbial communities across a range of European field sites. Soil Biol. Biochem. 88, 403–413 (2015).CAS 
Article 

Google Scholar 
16.de Graaff, M. A., Hornslein, N., Throop, H., Kardol, P. & van Diepen, L. T. A. Effects of agricultural intensification on soil biodiversity and implications for ecosystem functioning: A meta-analysis. Adv. Agron. 155, 1–44 (2019).Article 

Google Scholar 
17.Karimi, B. et al. Biogeography of soil bacterial networks along a gradient of cropping intensity. Sci. Rep. 9, 3812 (2019).ADS 
PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
18.Wardle, D. A., Nicholson, K. S., Bonner, K. I. & Yeates, G. W. Effects of agricultural intensification on soil-associated arthropod population dynamics, community structure, diversity and temporal variability over a seven-year period. Soil Biol. Biochem. 31, 1691–1706 (1999).CAS 
Article 

Google Scholar 
19.Gossner, M. M. et al. Land-use intensification causes multitrophic homogenization of grassland communities. Nature 540, 266–269 (2016).ADS 
CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
20.Valiente-Banuet, A. et al. Beyond species loss: The extinction of ecological interactions in a changing world. Funct. Ecol. 29, 299–307 (2015).Article 

Google Scholar 
21.Freilich, M. A., Wieters, E., Broitman, B. R., Marquet, P. A. & Navarrete, S. A. Species co-occurrence networks: Can they reveal trophic and non-trophic interactions in ecological communities?. Ecology 99, 690–699 (2018).PubMed 
Article 
PubMed Central 

Google Scholar 
22.Gray, C. et al. FORUM: Ecological networks: The missing links in biomonitoring science. J. Appl. Ecol. 51, 1444–1449 (2014).PubMed 
PubMed Central 
Article 

Google Scholar 
23.Evans, D., Kitson, J., Lunt, D., Straw, N. & Pocock, M. Merging DNA metabarcoding and ecological network analysis to understand and build resilient terrestrial ecosystems. Funct. Ecol. 30, 1904–1916 (2016).Article 

Google Scholar 
24.Ruppert, K. M., Kline, R. J. & Rahman, M. S. Past, present, and future perspectives of environmental DNA (eDNA) metabarcoding: A systematic review in methods, monitoring, and applications of global eDNA. Glob. Ecol. Conserv. 17, 00547 (2019).
Google Scholar 
25.Taberlet, P., Coissac, E., Hajibabaei, M. & Rieseberg, L. H. Environmental DNA. Mol. Ecol. 21, 1789–1793 (2012).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
26.Vacher, C. et al. Chapter one—Learning ecological networks from next-generation sequencing data. Adv. Ecol. Res. 54, 1–39 (2016).Article 

Google Scholar 
27.Poelen, J. H., Simons, J. D. & Mungall, C. J. Global biotic interactions: An open infrastructure to share and analyze species-interaction datasets. Ecol. Inform. 24, 148–159 (2014).Article 

Google Scholar 
28.Nguyen, N. H. et al. FUNGuild: An open annotation tool for parsing fungal community datasets by ecological guild. Fungal Ecol. 20, 241–248 (2016).Article 

Google Scholar 
29.Dopheide, A. et al. Rarity is a more reliable indicator of land-use impacts on soil invertebrate communities than other diversity metrics. Elife 9, e52787 (2020).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
30.García-Callejas, D., Molowny-Horas, R. & Araújo, M. B. Multiple interactions networks: Towards more realistic descriptions of the web of life. Oikos 127, 5–22 (2018).Article 

Google Scholar 
31.Delmas, E. et al. Analysing ecological networks of species interactions. Biol. Rev. 94, 16–36 (2019).Article 

Google Scholar 
32.Morrison, B. M. L., Brosi, B. J. & Dirzo, R. Agricultural intensification drives changes in hybrid network robustness by modifying network structure. Ecol. Lett. 23, 359–369 (2020).PubMed 
Article 

Google Scholar 
33.Banerjee, S. et al. Agricultural intensification reduces microbial network complexity and the abundance of keystone taxa in roots. ISME J. 13, 1722–1736 (2019).PubMed 
PubMed Central 
Article 

Google Scholar 
34.Thakur, M. P. & Wright, A. J. Environmental filtering, niche construction, and trait variability: The missing discussion. Trends Ecol. Evol. 32, 884–886 (2017).PubMed 
Article 

Google Scholar 
35.Xue, L. et al. Long term effects of management practice intensification on soil microbial community structure and co-occurrence network in a non-timber plantation. For. Ecol. Manag. 459, 117805 (2020).Article 

Google Scholar 
36.Felipe-Lucia, M. R. et al. Land-use intensity alters networks between biodiversity, ecosystem functions, and services. PNAS https://doi.org/10.1073/pnas.2016210117 (2020).Article 
PubMed 
PubMed Central 

Google Scholar 
37.Heemsbergen, D. A. et al. Biodiversity effects on soil processes explained by interspecific functional dissimilarity. Science 306, 1019–1020 (2004).ADS 
CAS 
PubMed 
Article 

Google Scholar 
38.Erdozain, M. et al. Metabarcoding of storage ethanol vs. conventional morphometric identification in relation to the use of stream macroinvertebrates as ecological indicators in forest management. Ecol. Indic. 101, 173–184 (2019).CAS 
Article 

Google Scholar 
39.Moore, J. C., McCann, K., Setälä, H. & De Ruiter, P. C. Top-down is bottom-up: Does predation in the rhizosphere regulate aboveground dynamics?. Ecology 84, 846–857 (2003).Article 

Google Scholar 
40.Wollrab, S., Diehl, S. & De Roos, A. M. Simple rules describe bottom-up and top-down control in food webs with alternative energy pathways. Ecol. Lett. 15, 935–946 (2012).PubMed 
Article 
PubMed Central 

Google Scholar 
41.de Vries, F. T. & Wallenstein, M. D. Below-ground connections underlying above-ground food production: A framework for optimising ecological connections in the rhizosphere. J. Ecol. 105, 913–920 (2017).Article 

Google Scholar 
42.de Vries, F. T. & Caruso, T. Eating from the same plate? Revisiting the role of labile carbon inputs in the soil food web. Soil Biol. Biochem. 102, 4–9 (2016).PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
43.Bramon Mora, B., Gravel, D., Gilarranz, L. J., Poisot, T. & Stouffer, D. B. Identifying a common backbone of interactions underlying food webs from different ecosystems. Nat. Commun. 9, 2603 (2018).ADS 
PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
44.Socolar, J. B., Gilroy, J. J., Kunin, W. E. & Edwards, D. P. How should beta-diversity inform biodiversity conservation?. Trends Ecol. Evol. 31, 67–80 (2016).PubMed 
Article 
PubMed Central 

Google Scholar 
45.Malik, A. A. et al. Land-use driven change in soil pH affects microbial carbon cycling processes. Nat. Commun. 9, 3591 (2018).ADS 
PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
46.Jia, Y. & Whalen, J. K. Functional redundancy and phylogenetic niche conservatism in the soil microbial community. Pedosphere 30, 18–24 (2020).ADS 
Article 

Google Scholar 
47.Bush, A. et al. DNA metabarcoding reveals metacommunity dynamics in a threatened boreal wetland wilderness. Proc. Natl. Acad. Sci. 117, 8539–8545 (2020).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
48.Blüthgen, N. et al. A quantitative index of land-use intensity in grasslands: Integrating mowing, grazing and fertilization. Basic Appl. Ecol. 13, 207–220 (2012).Article 

Google Scholar 
49.Ruiz-Martinez, I., Marraccini, E., Debolini, M. & Bonari, E. Indicators of agricultural intensity and intensification: A review of the literature. Ital. J. Agron. 10, 74–84 (2015).Article 

Google Scholar 
50.Taberlet, P., Bonin, A., Zinger, L. & Coissac, E. Environmental DNA: For Biodiversity Research and Monitoring (OUP Oxford, Oxford, 2018).Book 

Google Scholar 
51.Taberlet, P. et al. Soil sampling and isolation of extracellular DNA from large amount of starting material suitable for metabarcoding studies. Mol. Ecol. 21, 1816–1820 (2012).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
52.Boyer, F. et al. obitools: A unix-inspired software package for DNA metabarcoding. Mol. Ecol. Resour. 16, 176–182 (2016).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
53.Zinger, L. et al. Extracellular DNA extraction is a fast, cheap and reliable alternative for multi-taxa surveys based on soil DNA. Soil Biol. Biochem. 96, 16–19 (2016).CAS 
Article 

Google Scholar 
54.Compson, Z. G. et al. Chapter two—Linking DNA metabarcoding and text mining to create network-based biomonitoring tools: A case study on boreal wetland macroinvertebrate communities. Adv. Ecol. Res. 59, 33–74 (2018).Article 

Google Scholar 
55.G.B.I.F. GBIF backbone taxonomy. (2017).56.Allesina, S. & Pascual, M. Food web models: A plea for groups. Ecol. Lett. 12, 652–662 (2009).PubMed 
Article 
PubMed Central 

Google Scholar 
57.Gauzens, B., Thébault, E., Lacroix, G. & Legendre, S. Trophic groups and modules: Two levels of group detection in food webs. J. R. Soc. Interface 12, 20141176 (2015).PubMed 
PubMed Central 
Article 

Google Scholar 
58.Makiola, A. et al. Key questions for next-generation biomonitoring. Front. Environ. Sci. 7, 197 (2020).Article 

Google Scholar 
59.Nowicki, K. & Snijders, T. A. B. Estimation and prediction for stochastic block structures. J. Am. Stat. Assoc. 96, 1077–1087 (2001).MATH 
Article 

Google Scholar 
60.Biernacki, C., Celeux, G. & Govaert, G. Assessing a mixture model for clustering with the integrated completed likelihood. IEEE Trans. Pattern Anal. Mach. Intell. 22, 719–725 (2000).Article 

Google Scholar 
61.Compson, Z. G. et al. Network-based biomonitoring: Exploring freshwater food webs with stable isotope analysis and DNA metabarcoding. Front. Ecol. Evol. 7, 395 (2019).Article 

Google Scholar 
62.Ohlmann, M. et al. Diversity indices for ecological networks: A unifying framework using Hill numbers. Ecol. Lett. 22, 737–747 (2019).PubMed 
Article 
PubMed Central 

Google Scholar 
63.Gauzens, B., Legendre, S., Lazzaro, X. & Lacroix, G. Intermediate predation pressure leads to maximal complexity in food webs. Oikos 125, 595–603 (2016).Article 

Google Scholar 
64.Lau, M. K., Borrett, S. R., Baiser, B., Gotelli, N. J. & Ellison, A. M. Ecological network metrics: Opportunities for synthesis. Ecosphere 8, 01900 (2017).Article 

Google Scholar  More

1.Barnosky, A. D. et al. Has the Earth’s sixth mass extinction already arrived?. Nature 471(7336), 51–57 (2011).ADS 
CAS 
PubMed 
Article 

Google Scholar 
2.Ripple, W. J. et al. Status and ecological effects of the world’s largest carnivores. Science 343, 151–163 (2014).CAS 
Article 

Google Scholar 
3.Haswell, P. M., Kusak, J. & Hayward, M. W. Large carnivore impacts are context-dependent. Food Webs 12, 3–13. https://doi.org/10.1016/j.fooweb.2016.02.005 (2017).Article 

Google Scholar 
4.Barbosa, P. & Castellanos, I. Ecology of Predator–Prey Interactions (Oxford University Press, 2005).
Google Scholar 
5.Terborgh, J. & Estes, J. A. Trophic Cascades: Predator, Prey, and the Changing Dynamics of Nature (Island Press, 2010).
Google Scholar 
6.Estes, J. A. et al. Trophic downgrading of planet earth. Science 333, 301–306 (2011).ADS 
CAS 
PubMed 
Article 

Google Scholar 
7.Crooks, K. R. & Soulé, M. E. Mesopredator release and avifaunal extinctions in a fragmented system. Nature 400, 563–566 (1999).ADS 
CAS 
Article 

Google Scholar 
8.Ritchie, E. G. & Johnson, C. N. Predator interactions, mesopredator release and biodiversity conservation. Ecol. Lett. 12(9), 982–998. https://doi.org/10.1111/j.1461-0248.2009.01347.x (2009).Article 
PubMed 

Google Scholar 
9.Jachowski, D. S. et al. Identifying mesopredator release in multi-predator systems: A review of evidence from North America. Mamm. Rev. 50, 367–381. https://doi.org/10.1111/mam.12207 (2020).Article 

Google Scholar 
10.Letnic, M., Ritchie, E. G. & Dickman, C. R. Top predators as biodiversity regulators: The dingo Canis lupus dingo as a case study. Biol. Rev. 87(2), 390–413. https://doi.org/10.1111/j.1469-185X.2011.00203.x (2012).Article 
PubMed 

Google Scholar 
11.Glen, A. S. & Dickman, C. R. Complex interactions among mammalian carnivores in Australia, and their implications for wildlife management. Biol. Rev. 80(3), 387–401 (2005).PubMed 
Article 

Google Scholar 
12.Allen, B. L. et al. Can we save large carnivores without losing large carnivore science?. Food Webs. 12, 64–75 (2017).Article 

Google Scholar 
13.Allen, B. L. & Leung, K.-P. The (non)effects of lethal population control on the diet of Australian dingoes. PLoS ONE 9(9), e108251. https://doi.org/10.1371/journal.pone.0108251 (2014).ADS 
CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
14.Wallach, A. D. Australia should enlist dingoes to control invasive species. The Conversation 2014. https://theconversation.com/australia-should-enlist-dingoes-to-control-invasive-species-24807. Accessed 26 March, 2014.15.Letnic, M. & Feit, B. Like cats and dogs: dingoes can keep feral cats in check. The Conversation. 2019. https://theconversation.com/like-cats-and-dogs-dingoes-can-keep-feral-cats-in-check-114748. Accessed 4 April 2019.16.Newsome, T. Thinking big gives top predators the competitive edge. The Conversation 2017. https://theconversation.com/thinking-big-gives-top-predators-the-competitive-edge-78106. Accessed 24 May 2017.17.Johnson, C. & VanDerWal, J. Evidence that dingoes limit the abundance of a mesopredator in eastern Australian forests. J Appl Ecol. 46, 641–646 (2009).Article 

Google Scholar 
18.Rolls, E. C. They All Ran Wild: The Animals and Plants that Plague Australia (Angus & Robertson Publishers, 1969).
Google Scholar 
19.Balme, J., O’Connor, S. & Fallon, S. New dates on dingo bones from Madura Cave provide oldest firm evidence for arrival of the species in Australia. Sci. Rep. 8(1), 9933. https://doi.org/10.1038/s41598-018-28324-x (2018).ADS 
CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
20.Fleming, P. J. S., Allen, B. L. & Ballard, G. Seven considerations about dingoes as biodiversity engineers: The socioecological niches of dogs in Australia. Aust. Mammal. 34(1), 119–131 (2012).Article 

Google Scholar 
21.Corbett, L. K. The Dingo in Australia and Asia 2nd edn. (J.B. Books, South Australia, 2001).
Google Scholar 
22.Fleming, P. J. S. et al. Management of wild canids in Australia: Free-ranging dogs and red foxes. In Carnivores of Australia: Past, Present and Future (eds Glen, A. S. & Dickman, C. R.) 105–149 (CSIRO Publishing, 2014).
Google Scholar 
23.Doherty, T. S. et al. Impacts and management of feral cats Felis catus in Australia. Mamm. Rev. 42, 83–97 (2017).Article 

Google Scholar 
24.Brook, L. A., Johnson, C. N. & Ritchie, E. G. Effects of predator control on behaviour of an apex predator and indirect consequences for mesopredator suppression. J. Appl. Ecol. 49(6), 1278–1286. https://doi.org/10.1111/j.1365-2664.2012.02207.x (2012).Article 

Google Scholar 
25.Letnic, M., Koch, F., Gordon, C., Crowther, M. & Dickman, C. Keystone effects of an alien top-predator stem extinctions of native mammals. Proc. R. Soc. B Biol. Sci. 276, 3249–3256 (2009).Article 

Google Scholar 
26.Wallach, A. D., Johnson, C. N., Ritchie, E. G. & O’Neill, A. J. Predator control promotes invasive dominated ecological states. Ecol. Lett. 13, 1008–1018 (2010).PubMed 

Google Scholar 
27.Leo, V., Reading, R. P., Gordon, C. & Letnic, M. Apex predator suppression is linked to restructuring of ecosystems via multiple ecological pathways. Oikos 128, 630–639. https://doi.org/10.1111/oik.05546 (2019).Article 

Google Scholar 
28.Johnson, C. Australia’s Mammal Extinctions: A 50,000 Year History (Cambridge University Press, 2006).
Google Scholar 
29.Read, J. L. & Scoleri, V. Ecological implications of reptile mesopredator release in arid South Australia. J. Herpetol. 49(1), 64–69. https://doi.org/10.1670/13-208 (2015).Article 

Google Scholar 
30.Sutherland, D. R., Glen, A. S. & de Tores, P. J. Could controlling mammalian carnivores lead to mesopredator release of carnivorous reptiles?. Proc. R. Soc. B 278(1706), 641–648. https://doi.org/10.1098/rspb.2010.2103 (2010).Article 
PubMed 
PubMed Central 

Google Scholar 
31.Davis, N. E. et al. Interspecific and geographic variation in the diets of sympatric carnivores: Dingoes/wild dogs and red foxes in south-eastern Australia. PLoS ONE 10(3), e0120975. https://doi.org/10.1371/journal.pone.0120975 (2015).CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
32.Paltridge, R. The diets of cats, foxes and dingoes in relation to prey availability in the Tanami Desert, Northern Territory. Wildl. Res. 29, 389–403 (2002).Article 

Google Scholar 
33.Cupples, J. B., Crowther, M. S., Story, G. & Letnic, M. Dietary overlap and prey selectivity among sympatric carnivores: Could dingoes suppress foxes through competition for prey?. J. Mammal. 92(3), 590–600. https://doi.org/10.1644/10-MAMM-A-164.1 (2011).Article 

Google Scholar 
34.Glen, A. S., Pennay, M., Dickman, C. R., Wintle, B. A. & Firestone, K. B. Diets of sympatric native and introduced carnivores in the Barrington Tops, eastern Australia. Aust. Ecol. 36(3), 290–296. https://doi.org/10.1111/j.1442-9993.2010.02149.x (2011).Article 

Google Scholar 
35.Moseby, K. E., Neilly, H., Read, J. L. & Crisp, H. A. Interactions between a top order predator and exotic mesopredators in the Australian rangelands. Int. J. Ecol. 2012; Article ID 250352.36.Allen, B. L. & Fleming, P. J. S. Reintroducing the dingo: The risk of dingo predation to threatened vertebrates of western New South Wales. Wildl. Res. 39(1), 35–50 (2012).Article 

Google Scholar 
37.Glen, A. S. & Woodman, A. P. What Impact Does Altering Dingo Populations Have on Trophic Structure? (Environmental Evidence Australia, 2013).
Google Scholar 
38.Allen, B. L., Allen, L. R. & Leung, K.-P. Interactions between two naturalised invasive predators in Australia: Are feral cats suppressed by dingoes?. Biol. Invasions 17, 761–776. https://doi.org/10.1007/s10530-014-0767-1 (2015).Article 

Google Scholar 
39.Arthur, A. D., Catling, P. C. & Reid, A. Relative influence of habitat structure, species interactions and rainfall on the post-fire population dynamics of ground-dwelling vertebrates. Aust. Ecol. 37(8), 958–970 (2013).Article 

Google Scholar 
40.Claridge, A. W., Cunningham, R. B., Catling, P. C. & Reid, A. M. Trends in the activity levels of forest-dwelling vertebrate fauna against a background of intensive baiting for foxes. For. Ecol. Manag. 260(5), 822–832. https://doi.org/10.1016/j.foreco.2010.05.041 (2010).Article 

Google Scholar 
41.Stobo-Wilson, A. M. et al. Habitat structural complexity explains patterns of feral cat and dingo occurrence in monsoonal Australia. Divers. Distrib. 247, 108638. https://doi.org/10.1111/ddi.13065 (2020).Article 

Google Scholar 
42.Pavey, C. R., Eldridge, S. R. & Heywood, M. Population dynamics and prey selection of native and introduced predators during a rodent outbreak in arid Australia. J. Mammal. 89(3), 674–683 (2008).Article 

Google Scholar 
43.Greenville, A. C., Wardle, G. M., Tamayo, B. & Dickman, C. R. Bottom-up and top-down processes interact to modify intraguild interactions in resource-pulse environments. Oecologia 175(4), 1349–1358. https://doi.org/10.1007/s00442-014-2977-8 (2014).ADS 
Article 
PubMed 

Google Scholar 
44.Allen, B. L. et al. Does lethal control of top-predators release mesopredators? A re-evaluation of three Australian case studies. Ecol. Manag. Restor. 15(3), 191–195. https://doi.org/10.1111/emr.12118 (2014).Article 

Google Scholar 
45.Allen, B. L. et al. As clear as mud: A critical review of evidence for the ecological roles of Australian dingoes. Biol. Conserv. 159, 158–174 (2013).Article 

Google Scholar 
46.Hayward, M. W. & Marlow, N. Will dingoes really conserve wildlife and can our methods tell?. J. Appl. Ecol. 51(4), 835–838. https://doi.org/10.1111/1365-2664.12250 (2014).Article 

Google Scholar 
47.Newsome, T. M., Greenville, A. C., Letnic, M., Ritchie, E. G. & Dickman, C. R. The case for a dingo reintroduction in Australia remains strong: A reply to Morgan et al., 2016. Food Webs https://doi.org/10.1016/j.fooweb.2017.02.001 (2017).Article 

Google Scholar 
48.Letnic, M., Crowther, M. S., Dickman, C. R. & Ritchie, E. Demonising the dingo: How much wild dogma is enough?. Curr. Zool. 57(5), 668–670 (2011).Article 

Google Scholar 
49.Glen, A. S. Enough dogma: Seeking the middle ground on the role of dingoes. Curr. Zool. 58(6), 856–858 (2012).Article 

Google Scholar 
50.Johnson, C. N. et al. Experiments in no-impact control of dingoes: Comment on Allen et al. 2013. Front. Zool. 11, 17 (2014).PubMed 
PubMed Central 
Article 

Google Scholar 
51.Nimmo, D. G., Watson, S. J., Forsyth, D. M. & Bradshaw, C. J. A. Dingoes can help conserve wildlife and our methods can tell. J. Appl. Ecol. 52(2), 281–285. https://doi.org/10.1111/1365-2664.12369 (2015).Article 

Google Scholar 
52.Allen, B. L. et al. Top-predators as biodiversity regulators: Contemporary issues affecting knowledge and management of dingoes in Australia. In Biodiversity Enrichment in a Diverse World. Chapter 4 (ed. Lameed, G. A.) 85–132 (InTech Publishing, 2012).
Google Scholar 
53.Platt, J. R. Strong inference: Certain systematic methods of scientific thinking may produce much more rapid progress than others. Science 146(3642), 347–353 (1964).ADS 
CAS 
PubMed 
Article 

Google Scholar 
54.Caughley, G. Analysis of Vertebrate Populations (Wiley, 1977).
Google Scholar 
55.Krebs, C. J. Ecology: The Experimental Analysis of Distribution and Abundance 6th edn. (Benjamin-Cummings Publishing, 2008).
Google Scholar 
56.Hone, J. Wildlife Damage Control (CSIRO Publishing, 2007).Book 

Google Scholar 
57.Fox, G. A., Negrete-Yankelevich, S. & Sosa, V. J. Ecological Statistics: Contemporary Theory and Application (Oxford University Press, 2015).MATH 
Book 

Google Scholar 
58.Kershaw, K. A. Quantitative and Dynamic Ecology (Edward Arnold Publishers, 1969).
Google Scholar 
59.Li, J. C. R. Introduction to Statistical Inference (Edwards Bos Distributors, 1957).Book 

Google Scholar 
60.Quinn, G. P. & Keough, M. J. Experimental Design and Data Analysis for Biologists (Cambridge University Press, 2002).Book 

Google Scholar 
61.Shadish, W. R., Cook, T. D. & Campbell, D. T. Experimental and Quasi-experimental Designs for Generalized Casual Inference 2nd edn. (Houghton, Mifflin and Company, 2002).
Google Scholar 
62.Underwood, A. J. Experiments in Ecology (Cambridge University Press, 1997).
Google Scholar 
63.Allen, B. L., Allen, L. R., Engeman, R. M. & Leung, L.K.-P. Intraguild relationships between sympatric predators exposed to lethal control: Predator manipulation experiments. Front. Zool. 10, 39 (2013).PubMed 
PubMed Central 
Article 

Google Scholar 
64.Allen, B. L., Allen, L. R., Engeman, R. M. & Leung, L.K.-P. Sympatric prey responses to lethal top-predator control: Predator manipulation experiments. Front. Zool. 11, 56 (2014).Article 

Google Scholar 
65.Eldridge, S. R., Shakeshaft, B. J. & Nano, T. J. The impact of wild dog control on cattle, native and introduced herbivores and introduced predators in central Australia. Final report to the Bureau of Rural Sciences. Alice Springs: Parks and Wildlife Commission of the Northern Territory; 2002.66.Kennedy, M., Phillips, B., Legge, S., Murphy, S. & Faulkner, R. Do dingoes suppress the activity of feral cats in northern Australia?. Austral Ecol. 37(1), 134–139 (2012).Article 

Google Scholar 
67.Allen, B. L., Allen, L. R., Engeman, R. M. & Leung, L. K.-P. Reply to the criticism by Johnson et al. (2014) on the report by Allen et al. (2013). Front. Zool. 2014. http://www.frontiersinzoology.com/content/11/1/7/comments#1982699. Accessed 1st June 2014.68.Newsome, T. M. et al. Resolving the value of the dingo in ecological restoration. Restor. Ecol. 23(3), 201–208. https://doi.org/10.1111/rec.12186 (2015).Article 

Google Scholar 
69.Glen, A. S., Dickman, C. R., Soulé, M. E. & Mackey, B. G. Evaluating the role of the dingo as a trophic regulator in Australian ecosystems. Austral Ecol. 32(5), 492–501 (2007).Article 

Google Scholar 
70.Mitchell, B. & Balogh, S. Monitoring techniques for vertebrate pests: wild dogs. Orange: NSW Department of Primary Industries, Bureau of Rural Sciences; 2007.71.Letnic, M. & Koch, F. Are dingoes a trophic regulator in arid Australia? A comparison of mammal communities on either side of the dingo fence. Austral Ecol. 35(2), 267–175 (2010).Article 

Google Scholar 
72.Contos, P. & Letnic, M. Top-down effects of a large mammalian carnivore in arid Australia extend to epigeic arthropod assemblages. J. Arid Environ. (in press). https://doi.org/10.1016/j.jaridenv.2019.03.002.73.Mills, C. H., Wijas, B., Gordon, C. E., Lyons, M., Feit, A., Wilkinson, A., et al. Two alternate states: Shrub, bird and mammal assemblages differ on either side of the Dingo Barrier Fence. Aust Zool. (in press). https://doi.org/10.7882/az.2021.005.74.Engeman, R. M., Allen, L. R. & Allen, B. L. Study design concepts for inferring functional roles of mammalian top predators. Food Webs. 12, 56–63 (2017).Article 

Google Scholar 
75.Kennedy, M. S., Kreplins, T. L., O’Leary, R. A. & Fleming, P. A. Responses of dingo (Canis familiaris) populations to landscape-scale baiting. Food Webs. (in press). https://doi.org/10.1016/j.fooweb.2021.e00195.76.Allen, L. R. Is landscape-scale wild dog control best practice?. Australas. J. Environ. Manag. 24(1), 5–15 (2017).Article 

Google Scholar 
77.Ballard, G., Fleming, P. J. S., Meek, P. D. & Doak, S. Aerial baiting and wild dog mortality in south-eastern Australia. Wildl. Res. 47(2), 99–105. https://doi.org/10.1071/WR18188 (2020).Article 

Google Scholar 
78.Smith, D. & Allen, B. L. Habitat use by yellow-footed rock-wallabies in predator exclusion fences. J. Arid Environ. (in press).79.Smith, D., King, R. & Allen, B. L. Impacts of exclusion fencing on target and non-target fauna: A global review. Biol. Rev. 95(6), 1590–1606 (2020).PubMed 
Article 
PubMed Central 

Google Scholar 
80.Smith, D., Waddell, K. & Allen, B. L. Expansion of vertebrate pest exclusion fencing and its potential benefits for threatened fauna recovery in Australia. Animals 10, 1550 (2020).PubMed Central 
Article 
PubMed 

Google Scholar 
81.Clark, P., Clark, E. & Allen, B. L. Sheep, dingoes and kangaroos: New challenges and a change of direction 20 years on. In Advances in Conservation Through Sustainable Use of Wildlife (eds Baxter, G. et al.) 173–178 (University of Queensland, 2018).
Google Scholar 
82.Allen, L. R. The Impact of Wild Dog Predation and Wild Dog Control on Beef Cattle: Large-Scale Manipulative Experiments Examining the Impact of and Response to Lethal Control (LAP Lambert Academic Publishing, 2013).
Google Scholar 
83.Allen, L. R. Demographic and functional responses of wild dogs to poison baiting. Ecol. Manag. Restor. 16(1), 58–66 (2015).Article 

Google Scholar 
84.Eldridge, S. R., Bird, P. L., Brook, A., Campbell, G., Miller, H. A., Read, J. L., et al. The effect of wild dog control on cattle production and biodiversity in the South Australian arid zone: Final report. Port Augusta, South Australia: South Australian Arid Lands Natural Resources Management Board; 2016.85.Fancourt, B. A., Cremasco, P., Wilson, C. & Gentle, M. N. Do introduced apex predators suppress introduced mesopredators? A multiscale spatiotemporal study of dingoes and feral cats in Australia suggests not. J. Appl. Ecol. 56(12), 2584–2595. https://doi.org/10.1111/1365-2664.13514 (2019).Article 

Google Scholar 
86.Allen, B. L., Engeman, R. M. & Allen, L. R. Wild dogma I: An examination of recent “evidence” for dingo regulation of invasive mesopredator release in Australia. Curr. Zool. 57(5), 568–583 (2011).Article 

Google Scholar 
87.Allen, L. R. & Engeman, R. M. Evaluating and validating abundance monitoring methods in the absence of populations of known size: Review and application to a passive tracking index. Environ. Sci. Pollut. Res. 22, 2907–2915. https://doi.org/10.1007/s11356-014-3567-3 (2014).Article 

Google Scholar 
88.Caughley, G. Analysis of Vertebrate Populations, reprinted with corrections. (Wiley, 1980).
Google Scholar 
89.Wysong, M. L. et al. Space use and habitat selection of an invasive mesopredator and sympatric, native apex predator. Mov. Ecol. 8(1), 18. https://doi.org/10.1186/s40462-020-00203-z (2020).Article 
PubMed 
PubMed Central 

Google Scholar 
90.Ritchie, E. G. et al. Ecosystem restoration with teeth: What role for predators?. Trends Ecol. Evol. 27(5), 265–271 (2012).PubMed 
Article 

Google Scholar 
91.Letnic, M. Stop poisoning dingoes to protect native animals. University of New South Wales, Sydney, available at http://newsroom.unsw.edu.au/news/science/stop-poisoning-dingoes-protect-native-mammals. Accessed 1 April 2014: UNSW Newsroom; 2014.92.Ritchie, E. G. The world’s top predators are in decline, and it’s hurting us too. The Conversation. 2014. http://theconversation.com/the-worlds-top-predators-are-in-decline-and-its-hurting-us-too-21830. Accessed 10 January 2014.93.Brown, J. S., Laundre, J. W. & Gurung, M. The ecology of fear: Optimal foraging, game theory, and trophic interactions. J. Mammal. 80, 385–399 (1999).Article 

Google Scholar 
94.Laundré, J. W. et al. The landscape of fear: The missing link to understand top-down and bottom-up controls of prey abundance?. Ecology 95(5), 1141–1152. https://doi.org/10.1890/13-1083.1 (2014).Article 
PubMed 

Google Scholar 
95.Haswell, P. M., Jones, K. A., Kusak, J. & Hayward, M. W. Fear, foraging and olfaction: How mesopredators avoid costly interactions with apex predators. Oecologia https://doi.org/10.1007/s00442-018-4133-3 (2018).Article 
PubMed 
PubMed Central 

Google Scholar 
96.Colman, N. J., Gordon, C. E., Crowther, M. S. & Letnic, M. Lethal control of an apex predator has unintended cascading effects on forest mammal assemblages. Proc. R. Soc. B Biol. Sci. 281(1782), 20133094. https://doi.org/10.1098/rspb.2013.3094 (2014).CAS 
Article 

Google Scholar 
97.Sheriff, M. J., Peacor, S., Hawlena, D. & Thaker, M. Non-consumptive predator effects on prey population size: A dearth of evidence. J. Anim. Ecol. 89, 1302–1316. https://doi.org/10.1111/1365-2656.13213 (2020).Article 
PubMed 

Google Scholar 
98.Fleming, P. J. S. et al. Roles for the Canidae in food webs reviewed: Where do they fit?. Food Webs. 12(Supplement C), 14–34. https://doi.org/10.1016/j.fooweb.2017.03.001 (2017).Article 

Google Scholar 
99.Wang, Y. & Fisher, D. Dingoes affect activity of feral cats, but do not exclude them from the habitat of an endangered macropod. Wildl. Res. 39, 611–620 (2012).Article 

Google Scholar 
100.Hayward, M. W. et al. Ecologists need robust survey designs, sampling and analytical methods. J. Appl. Ecol. 52(2), 286–290. https://doi.org/10.1111/1365-2664.12408 (2015).Article 

Google Scholar 
101.Johnson, C. N. & Ritchie, E. The dingo and biodiversity conservation: response to Fleming et al. (2012). Aust. Mammal. 35(1), 8–14 (2013).Article 

Google Scholar 
102.Wallach, A. D. & O’Neill, A. J. Threatened species indicate hot-spots of top-down regulation. Anim. Biodivers. Conserv. 32(2), 127–133 (2009).
Google Scholar 
103.Feit, B., Feit, A. & Letnic, M. Apex predators decouple population dynamics between mesopredators and their prey. Ecosystems. (in press). https://doi.org/10.1007/s10021-019-00360-2.104.Gordon, C. E., Moore, B. D. & Letnic, M. Temporal and spatial trends in the abundances of an apex predator, introduced mesopredator and ground-nesting bird are consistent with the mesopredator release hypothesis. Biodivers. Conserv. https://doi.org/10.1007/s10531-017-1309-9 (2017).Article 

Google Scholar 
105.Letnic, M. et al. Does a top predator suppress the abundance of an invasive mesopredator at a continental scale?. Glob. Ecol. Biogeogr. 20(2), 343–353 (2011).Article 

Google Scholar 
106.Rees, J. D., Kingsford, R. T. & Letnic, M. Changes in desert avifauna associated with the functional extinction of a terrestrial top predator. Ecography 42(1), 67–76. https://doi.org/10.1111/ecog.03661 (2019).Article 

Google Scholar 
107.Allen, B. L. et al. Large carnivore science: Non-experimental studies are useful, but experiments are better. Food Webs 13, 49–50 (2017).Article 

Google Scholar 
108.Allen, B. L., Engeman, R. M. & Allen, L. R. Wild dogma II: The role and implications of wild dogma for wild dog management in Australia. Curr. Zool. 57(6), 737–740 (2011).Article 

Google Scholar 
109.Fleming, P. J. S., Allen, B. L. & Ballard, G. Cautionary considerations for positive dingo management: A response to the Johnson and Ritchie critique of Fleming et al. (2012). Aust Mammal. 35(1), 15–22 (2013).Article 

Google Scholar 
110.Allen, B. L. Did dingo control cause the elimination of kowaris through mesopredator release effects? A response to Wallach and O’Neill (2009). Anim. Biodivers. Conserv. 33(2), 1–4 (2010).
Google Scholar 
111.Woinarski, J. C. Z. et al. Reading the black book: The number, timing, distribution and causes of listed extinctions in Australia. Biol. Conserv. 239, 108261. https://doi.org/10.1016/j.biocon.2019.108261 (2019).Article 

Google Scholar 
112.Kearney, S. G., Cawardine, J., Reside, A. E., Fisher, D., Maron, M., Doherty, T. S., et al. The threats to Australia’s imperilled species and implications for a national conservation response. Pac. Conserv. Biol. (in press). https://doi.org/10.1071/PC18024.113.Burbidge, A. A. & McKenzie, N. L. Patterns in the modern decline of Western Australia’s vertebrate fauna: Causes and conservation implications. Biol. Conserv. 50, 143–198 (1989).Article 

Google Scholar 
114.Lunney, D. Causes of the extinction of native mammals of the western division of New South Wales: An ecological interpretation of the nineteenth century historical record. Rangel. J. 23(1), 44–70 (2001).Article 

Google Scholar 
115.Cremona, T., Crowther, M. S. & Webb, J. K. High mortality and small population size prevents population recovery of a reintroduced mesopredator. Anim. Conserv. 20, 555–563. https://doi.org/10.1111/acv.12358 (2017).Article 

Google Scholar 
116.Bannister, H. L., Lynch, C. E. & Moseby, K. E. Predator swamping and supplementary feeding do not improve reintroduction success for a threatened Australian mammal, Bettongia lesueur. Aust. Mammal. 38, 177–187 (2016).Article 

Google Scholar 
117.Mori, E. et al. Spatiotemporal mechanisms of coexistence in an European mammal community in a protected area of southern Italy. J. Zool. 310(3), 232–245. https://doi.org/10.1111/jzo.12743 (2020).Article 

Google Scholar 
118.Saggiomo, L. Mesopredator Release and Competitive Exclusion: A Global Review and Potential for European Carnivores [Masters] (Alma Mater Studiorum University, 2014).
Google Scholar 
119.Gigliotti, L. C. et al. Context dependency of top-down, bottom-up and density-dependent influences on cheetah demography. J. Anim. Ecol. 89, 449–459. https://doi.org/10.1111/1365-2656.13099 (2020).Article 
PubMed 

Google Scholar 
120.Cozzi, G. et al. Fear of the dark or dinner by moonlight? Reduced temporal partitioning among Africa’s large carnivores. Ecology 93(12), 2590–2599. https://doi.org/10.1890/12-0017.1 (2012).Article 
PubMed 

Google Scholar 
121.Rafiq, K. et al. Spatial and temporal overlaps between leopards (Panthera pardus) and their competitors in the African large predator guild. J. Zool. 311(4), 246–259. https://doi.org/10.1111/jzo.12781 (2020).Article 

Google Scholar 
122.Comley, J., Joubert, C. J., Mgqatsa, N. & Parker, D. M. Lions do not change rivers: Complex African savannas preclude top-down forcing by large predators. J. Nat. Conserv. 56, 125844 (2020).Article 

Google Scholar 
123.Allen, M. L., Peterson, B. & Krofel, M. No respect for apex carnivores: Distribution and activity patterns of honey badgers in the Serengeti. Mamm. Biol. 89, 90–94. https://doi.org/10.1016/j.mambio.2018.01.001 (2018).Article 

Google Scholar 
124.Vitekere, K. et al. Dynamic in species estimates of carnivores (leopard cat, red fox, and north Chinese leopard): A multi-year assessment of occupancy and coexistence in the Tieqiaoshan Nature Reserve, Shanxi Province, China. Animals 10(8), 1333. https://doi.org/10.3390/ani10081333 (2020).Article 
PubMed Central 
PubMed 

Google Scholar 
125.Brodie, J. F. & Giordano, A. Lack of trophic release with large mammal predators and prey in Borneo. Biol. Conserv. 63, 58–67. https://doi.org/10.1016/j.biocon.2013.01.003 (2013).Article 

Google Scholar 
126.Lahkar, D., Ahmed, M. F., Begum, R. H., Das, S. K. & Harihar, A. Inferring patterns of sympatry among large carnivores in Manas National Park—A prey-rich habitat influenced by anthropogenic disturbances. Anim. Conserv. (in press). https://doi.org/10.1111/acv.12662.127.Gehrt, S. D. & Prange, S. Interference competition between coyotes and raccoons: A test of the mesopredator release hypothesis. Behav. Ecol. 18(1), 204–214 (2007).Article 

Google Scholar 
128.Dias, D. M., Massara, R. L., de Campos, C. B. & Rodrigues, F. H. G. Feline predator–prey relationships in a semi-arid biome in Brazil. J. Zool. (in press). https://doi.org/10.1111/jzo.12647.129.Foster, V. C. et al. Jaguar and puma activity patterns and predator–prey interactions in four Brazilian biomes. Biotropica 45(3), 373–379. https://doi.org/10.1111/btp.12021 (2013).Article 

Google Scholar 
130.Allen, L. R. Best practice baiting: Dispersal and seasonal movement of wild dogs (Canis lupus familiaris). Technical highlights: Invasive plant and animal research 2008–09. Brisbane: QLD Department of Employment, Economic Development and Innovation; 2009. 61–62.131.Fleming, P., Corbett, L., Harden, R. & Thomson, P. Managing the impacts of dingoes and other wild dogs. Bomford M, editor. Canberra: Bureau of Rural Sciences; 2001.132.Thomas, L. et al. Distance software: Design and analysis of distance sampling surveys for estimating population size. J. Appl. Ecol. 47, 5–14 (2010).PubMed 
Article 

Google Scholar 
133.Ruette, S., Stahl, P. & Albaret, M. Applying distance-sampling methods to spotlight counts of red foxes. J. Appl. Ecol. 40, 32–43 (2003).Article 

Google Scholar 
134.Engeman, R. Indexing principles and a widely applicable paradigm for indexing animal populations. Wildl. Res. 32(3), 202–210 (2005).Article 

Google Scholar 
135.R Core Team. R: A Language and Environment for Statistical Computing. R Foundation for Statistical Computing; 2020. More

Biodiversity assessment through DNA metabarcodingOur analysis detected 160 Operational Taxonomic Units (OTUs) with 12,007,712 sequenced reads, 222,370 ± 41,954 (sd) reads per sample, for a total of 54 sequenced samples. The rarefaction curves showed good sequencing effort for the samples (Supplementary Figure S1) which were rarefied to the least count among samples corresponding to 135,443 reads. Twenty OTUs, (7.2% of the total), were assigned to taxa not relevant to our work (mainly to mosses and ferns during the periods October 2014–March 2015 and July–October 2015). From the remaining OTUs, 108 (88% of the reads) were taxonomically assigned to 32 families of vascular plants (68 identified taxa) (Table 2, Supplementary Table S1) and 32 OTUs (4.8% of the reads) remained unidentified either because of low sequence identity and/or query coverage percentage or the absence of any sequence classification result, even when compared to the complete ‘Nucleotide’ Genbank database. The results of the taxonomic assignment to vascular plants are presented in Supplementary Table S1. The OTU sequences were assigned to plant taxa with at least 95% identity and coverage, from which 70% of the OTUs had ≥ 98% sequence identity with the assigned taxa. The positive control of the DNA extraction, Corylus avellana pollen, was correctly identified after HTS. From the 19 negative controls included in the extraction plate, one negative control was selected for sequencing, the only one with sufficient amplicon concentration (2 ng μl−1). In this sample two OTUs were detected (263,649 reads), both assigned to Quercus spp. and contributing  More

1.Namita, P., Mukesh, R. & Vijay, K. J. Camellia Sinensis (Green Tea): A review. Glob. J. Pharmacol. 6(2), 52–59 (2012).
Google Scholar 
2.Chang, K. World Tea Production and Trade. Current and Future Development (FAO, Rome, 2015).
Google Scholar 
3.Chang, K. & Brattlof, M. World Tea Production and Trade. Current and Future Development (FAO, 2015).
Google Scholar 
4.Kochlamazashvili, I. & Kakulia, N. The Georgian Tea Sector: A Value Chain Study. ISET Policy Institute. Study prepared in the framework of ENPARD project Cooperation for Rural Prosperity in Georgia (2015).5.Lesica, P., McCune, B., Cooper, S. V. & Hong, W. S. Differences in lichen and bryophyte communities between old-growth and managed second-growth forests in the Svan Valley Montana. Can. J. Bot. 69, 1745–1755 (1991).Article 

Google Scholar 
6.Nowak, A., Plášek, V., Nobis, M. & Nowak, S. Epiphytic communities of open habitats in the Western Tian-Shan Mts (Middle Asia: Kyrgyzstan). Cryptog. Bryol. 37(4), 415–433 (2016).Article 

Google Scholar 
7.Rhoades, F. M. Nonvascular epiphytes in forest canopies: Worldwide distribution, abundance and ecological roles. In Forest Canopies (eds. Lowman, M.D. & Nadkarni, N. M.) 353–408 (1995).8.Haines, W. P. & Renwick, J. A. A. Bryophytes as food: Comparative consumption and utilization of mosses by a generalist insect herbivore. Entomol Exp Appl. 133, 296–306. https://doi.org/10.1111/j.1570-7458.2009.00929.x (2009).Article 

Google Scholar 
9.Kuřavová, K. et al. Is feeding on mosses by groundhoppers in the genus Tetrix (Insecta: Orthoptera) opportunistic or selective?. Arthropod-Plant Int. 11, 35–43. https://doi.org/10.1007/s11829-016-9461-9 (2017).Article 

Google Scholar 
10.Matuszkiewicz, W. Przewodnik do Oznaczania Zbiorowisk Roślinnych Polski (Wyd Nauk, PWN, 2001).
Google Scholar 
11.Krestov, P. V. Forest vegetation of easternmost Russia (Russian Far East). In Forest Vegetation of Northeast Asia (eds Kolbek, J. et al.) 93–180 (Springer, 2003).Chapter 

Google Scholar 
12.Kuznetsov, O. Topology-ecological classification of mire vegetation in the Republic of Karelia (Russia). In Biodiversity and Conservation of Boreal Nature. Proceedings of the 10 years anniversary symposium of the Nature Reserve Friendship (eds Heikkilä, R. & Lindholm, T.) 117–123 (Elsevier, 2003).
Google Scholar 
13.Černý, T. Phytosociological Study of Selected Critical Thermophilous Vegetation Complexes in the Czech Republic. A thesis submitted for the degree of Doctor of Philosophy in the Department of Botany Faculty of Sciences, Charles University (2007).14.Chytrý, M. et al. A modern analogue of the Pleistocene steppe-tundra ecosystem in southern Siberia. Boreas 48, 36–56 (2019).Article 

Google Scholar 
15.Wolski, G. J. & Kruk, A. Determination of plant communities based on bryophytes: The combined use of Kohonen artificial neural network and indicator species analysis. Ecol. Indic 113, 106160. https://doi.org/10.1016/j.ecolind.2020.106160 (2020).Article 

Google Scholar 
16.Benzing, D. Vulnerabilities of tropical forests to climate change: The significance of resident epiphytes. Clim. Change 39, 519–540 (1998).Article 

Google Scholar 
17.Gustafsson, L., Fiskesjö, A., Ingelög, T., Petterson, B. & Thor, G. Factors of importance to some lichen species of deciduous broad-leaved woods in southern Sweden. Lichenologist 24, 255–266 (1992).Article 

Google Scholar 
18.Frahm, J. P. Ecology of bryophytes along altitudinal and latitudinal gradients in Chile. Trop. Bryol. 21, 67–79 (2002).
Google Scholar 
19.Číhal, L., Kaláb, O. & Plášek, V. Modeling the distribution of rare and interesting moss species of the family Orthotrichaceae (Bryophyta) in Tajikistan and Kyrgyzstan. Acta Soc. Bot. Pol. 86(2), 3543. https://doi.org/10.5586/asbp.3543 (2017).Article 

Google Scholar 
20.Łubek, A., Kukwa, M., Czortek, P. & Jaroszewicz, B. Impact of Fraxinus excelsior dieback on biota of ash-associated lichen epiphytes at the landscape and community level. Biodivers. Conserv. 29, 431–450. https://doi.org/10.1007/s10531-019-01890-w (2020).Article 

Google Scholar 
21.Łubek, A., Kukwa, M., Jaroszewicz, B. & Czortek, P. Identifying mechanisms shaping lichen functional diversity in a primeval forest. For. Ecol. Manag. 475, 118434. https://doi.org/10.1016/j.foreco.2020.118434 (2020).Article 

Google Scholar 
22.Barkman, J. J. Phytosociology and Ecology of Cryptogamic Epiphytes. Including a Taxonomic Survey and Description of Their Vegetation Units in Europe, Van Gorcum, Comp (N. V Assen, 1958).
Google Scholar 
23.Green, T. G. A. & Lange, O. L. Photosynthesis in poikilohydric plants: A comparison of lichens and bryophytes. In Ecophysiology of Photosynthesis (eds Schulze, E.-D. & Caldwell, M. M.) 319–341 (Springer-Verlag, 1995).Chapter 

Google Scholar 
24.Scheidegger, C., Wolseley, P. A. & Landolt, R. Towards conservation of lichens. Forest. Snow Landsc. Res. 75, 285–433 (2000).
Google Scholar 
25.Tønsberg, T. & Høiland, K. A study of the macrolichen flora on the sand-dune areas on Lista, SW Norway. Nor. J. Bot. 27, 131–134 (1980).
Google Scholar 
26.Thiet, R. K., Doshas, A. & Smith, S. M. Effects of biocrusts and lichen-moss mats on plant productivity in a US sand dune ecosystem. Plant Soil 377(1), 235–244 (2014).CAS 
Article 

Google Scholar 
27.Vaz, A. S., Marques, J. & Honrado, J. P. Patterns of lichen diversity in coastal sand-dunes of northern Portugal. Bot. Complut. 38, 89–96 (2014).Article 

Google Scholar 
28.Antoninka, A., Bowker, M. A., Reed, S. C. & Doherty, K. Production of greenhouse-grown biocrust mosses and associated cyanobacteria to rehabilitate dryland soil function. Restor. Ecol. 24(3), 324–335 (2016).Article 

Google Scholar 
29.Jüriado, I., Kämärä, M.-L. & Oja, E. Environmental factors and ground disturbance affecting the composition of species and functional traits of ground layer lichens on grey dunes and dune heaths of Estonia. Nord. J. Bot. 34(2), 244–255 (2016).Article 

Google Scholar 
30.Balogh, R. et al. Mosses and lichens in dynamics of acidic sandy grasslands: Specific response to grazing exclosure. Acta Biol. Plant. Agriensis 5(1), 30 (2017).
Google Scholar 
31.Concostrina-Zubiri, L., Arenas, J. M., Martínez, I. & Escudero, A. Unassisted establishment of biological soil crusts on dryland road slopes. Web Ecol. 19(1), 39–51 (2019).Article 

Google Scholar 
32.Kubiak, D. & Oszyczka, P. Non-forested vs forest environments: The effect of habitat conditionson host tree parameters and the occurrence of associated epiphytic lichens. Fungal Ecol. 47, 100957 (2020).Article 

Google Scholar 
33.Gradstein, S. R. & Sporn, S. G. Land-use change and epiphytic bryophyte diversity in the Tropics. Nova Hedwigia 138, 311–323 (2010).
Google Scholar 
34.Guevara, S., Purata, S. E. & Van der Maarel, E. The role of remnant forest trees in tropical secondary succession. Vegetatio 66, 77–84 (1986).
Google Scholar 
35.Sillett, S. C., Gradstein, S. R. & Griffin, D. Bryophyte diversity of Ficus tree crowns from cloud forest and pasture in Costa Rica. Bryologist 98(2), 251–260 (1995).Article 

Google Scholar 
36.Werner, F., Homeier, J. & Gradstein, S. R. Diversity of vascular epiphytes on isolated remnant trees in the montane forest belt of southern Ecuador. Ecotropica 11, 21–40 (2005).
Google Scholar 
37.Lara, F., Garilleti, R. & Mazimpaka, V. Orthotrichum karoo (Orthotrichaceae), a new species with hyaline-awned leaves from southwestern Africa. Bryologist 112(1), 194–201 (2009).Article 

Google Scholar 
38.Lara, F. & Mazimpaka, V. Ma´s sobre la presencia de Orthotrichum acuminatum en la Península Ibérica. Cryptog. Bryol. Lichenol. 13(4), 349–354 (1992).
Google Scholar 
39.Garilleti, R., Lara, F. & Mazimpaka, V. Orthotrichum anodon (Orthotrichaceae, Bryopsida), a new species from California, and its relationships with other Orthotricha sharing puckered capsule mouths. Bryologist 109(2), 188–196 (2006).Article 

Google Scholar 
40.Hallingbäck, T. & Hodgetts, N. Mosses Liverworts and Hornworts. Status survey and conservation action plan for bryophytes (Cambridge University Press, 2000).
Google Scholar 
41.Belinchón, R., Martínez, I., Escudero, A., Aragón, G. & Valladares, F. Edge effects on epiphytic communities in a Mediterranean Quercus pyrenaica forest. J. Veg. Sci. 18, 81–90. https://doi.org/10.1111/j.1654-1103.2007.tb02518.x (2007).Article 

Google Scholar 
42.Boudreault, C., Gauthier, S. & Bergeron, Y. Epiphytic lichens and bryophytes on Populus Tremuloides along a chronosequence in the Southwestern Boreal Forest of Quebec, Canada. Bryologist 103, 725–738. https://doi.org/10.1639/0007-2745(2000)103[0725:ELABOP]2.0.CO;2 (2009).Article 

Google Scholar 
43.Rambo, T. Structure and composition of corticolous epiphyte communities in a Sierra Nevada old-growth mixed-conifer forest. Bryologist 113, 55–71. https://doi.org/10.1639/0007-2745-113.1.55 (2010).Article 

Google Scholar 
44.Plášek, V., Nowak, A., Nobis, M., Kusza, G. & Kochanowska, K. Effect of 30 years of road traffic abandonment on epiphytic moss diversity. Environ. Monit. Assess. 186, 8943–8959. https://doi.org/10.1007/s10661-014-4056-3 (2014).CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
45.Skoupá, Z., Ochyra, R., Guo, S. L., Sulayman, M. & Plášek, V. Distributional novelties for Lewinskya, Nyholmiella and Orthotrichum (Orthotrichaceae) in China. Herzogia 30, 58–73. https://doi.org/10.13158/heia.30.1.2017.58 (2017).Article 

Google Scholar 
46.Skoupá, Z., Ochyra, R., Guo, S.-L., Sulayman, M. & Plášek, V. Three remarkable additions of Orthotrichum species (Orthotrichaceae) to the moss flora of China. Herzogia 31, 88–100. https://doi.org/10.13158/099.031.0105 (2018).Article 

Google Scholar 
47.Gradstein, R. et al. Bryophytes of Mount Patuha, West Java, Indonesia. Reinwardtia 13(2), 107–123 (2010).
Google Scholar 
48.Saat, A., Talib, M. S., Harun, N., Hamzah, Z. & Wood, A. K. Spatial variability of arsenic and heavy metals in a highland tea plantation using lichens and mosses as bio-monitors. Asian J. Nat. Appl. Sci. 5(1), 10–21 (2016).
Google Scholar 
49.Fick, S. E. & Hijmans, R. J. WorldClim 2: New 1km spatial resolution climate surfaces for global land areas. Int. J. Climatol. 37(12), 4302–4315 (2017).Article 

Google Scholar 
50.Wirth, V. Ökologische Zeigerwerte von Flechten. Herzogia 23(2), 229–248 (2010).Article 

Google Scholar 
51.Ellenberger, H. et al. Zeigerwerte von Planzen in Mitteleuropa. Scr. Geobot. 18, 1–248 (1991).
Google Scholar 
52.Smith, C. W. et al. The Lichens of Great Britain and Ireland 1046 (British Lichen Society, 2009).
Google Scholar 
53.Hodgetts, N. et al. An annotated checklist of bryophytes of Europe, Macaronesia and Cyprus. J. Bryol. 42(1), 1–116. https://doi.org/10.1080/03736687.2019.1694329 (2020).Article 

Google Scholar 
54.Pancho, J. V. Some bryophytes in tea plantations, Pagilaran Central Java. Biotrop. Bull. 11, 279–282 (1979).
Google Scholar 
55.Tan, B. C. et al. Mosses of Gunung Halimun National Park, West Java, Indonesia. Reinwardtia 12, 205–214 (2006).
Google Scholar 
56.Ohsawa, M. Weeds of tea plantations. In Biology and Ecology of Weeds. Geobotany Vol. 2 (eds Holzner, W. & Numata, M.) (Springer, 1982).
Google Scholar 
57.Gradstein, R. et al. Bryophytes of Mount Patuha, West Java, Indonesia. Reinwardtia 13, 107–123 (2010).
Google Scholar 
58.Whitelaw, M. & Burton, M. A. S. Diversity and distribution of epiphytic bryophytes on Bramley’s Seedling trees in East of England apple orchards. Glob. Ecol. Conserv. 4, 380–387. https://doi.org/10.1016/j.gecco.2015.07.014 (2015).Article 

Google Scholar 
59.Söderström, L. Bryophytes and decaying wood – a comparison between manager and natural forest. Holarc. Ecol. 14, 121–130 (1991).
Google Scholar 
60.Cieśliński, S. et al. Relikty lasu puszczańskiego, In Białowieski Park Narodowy (1921–1996) w badaniach geobotanicznych. Phytocoenosis, 8 (N.S.), Seminarium Geobotanicum (ed. Faliński, J. B.) 4, 47–64 (1996).61.Vanderpoorten, A., Engels, P. & Sotiaux, A. Trends in diversity and abundance of obligate epiphytic bryophytes in a highly managed landscape. Ecography 27, 567–576 (2004).Article 

Google Scholar 
62.Ódor, P., van Dort, K., Aude, E., Heilmann-Clausen, J. & Christensen, M. Diversity and composition of dead wood inhabiting bryophyte communities in European beech forest. Biol. Soc. Esp. Briol. 26–27, 85–102 (2005).
Google Scholar 
63.Friedel, A., Oheimb, G. V., Dengler, J. & Härdtle, W. Species diversity and species composition of epiphytic bryophytes and lichens: A comparison of managed and unmanaged beech forests in NE Germany. Feddes Repert. 117(1–2), 172–185 (2006).Article 

Google Scholar 
64.Wolski, G. J. Siedliskowe Uwarunkowania Występowania Mszaków w Rezerwatach Przyrody Chroniących Jodłę Pospolitą w Polsce Środkowej (Praca doktorska wykonana w Katedrze Geobotaniki i Ekologii Roślin UŁ, 2013).
Google Scholar 
65.Fudali, E. & Wolski, G. J. Ecological diversity of bryophytes on tree trunks in protected forests (a case study from Central Poland). Herzogia 28(1), 91–107 (2015).Article 

Google Scholar 
66.Shi, X.-M. et al. Epiphytic bryophytes as bio-indicators of atmospheric nitrogen deposition in a subtropical montane cloud forest: Response patterns, mechanism, and critical load. Environ. Pollut. 229, 932–941. https://doi.org/10.1016/j.envpol.2017.07.077 (2017).CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
67.Cornelissen, J. H. C. & Gradstein, S. R. On the occurrence of bryophytes and macrolichens in different lowland rain forest types of Mabura Hill, Guyana. Trop. Bryol. 3, 29–35. https://doi.org/10.11646/bde.3.1.4 (1990).Article 

Google Scholar 
68.Lyons, B., Nadkarni, N. M. & North, M. P. Spatial distribution and succession of epiphytes on Tsuga heterophylla (western hemlock) in an old-growth Douglas-fir forest. Can. J. Bot. 78(7), 957–968. https://doi.org/10.1139/cjb-78-7-957 (2000).Article 

Google Scholar 
69.Cornelissen, J. H. C. & Steege, H. T. Distribution and ecology of epiphytic bryophytes and lichens in dry evergreen forest of Guyana. J. Trop. Ecol. 5, 131–150. https://doi.org/10.1017/S0266467400003400 (1989).Article 

Google Scholar 
70.Woods, C. L., Cardelús, C. L., Dewalt, S. J. & Piper, F. Microhabitat associations of vascular epiphytes in a wet tropical forest canopy. J. Ecol. 103(2), 421–430. https://doi.org/10.1111/1365-2745.12357 (2015).Article 

Google Scholar 
71.Sporn, S. G., Bos, M. M., Kessler, M. & Gradstein, S. R. Vertical distribution of epiphytic bryophytes in an Indonesian rainforest. Biodivers. Conserv. 19(3), 745–760. https://doi.org/10.1007/s10531-009-9731-2 (2010).Article 

Google Scholar 
72.Czerepko, J. et al. How sensitive are epiphytic and epixylic cryptogams as indicators of forest naturalness? Testing bryophyte and lichen predictive power in stands under different management regimes in the Białowieża forest. Ecol. Indic. 125, 107532. https://doi.org/10.1016/j.ecolind.2021.107532 (2021).Article 

Google Scholar 
73.Putna, S. & Mězaka, A. Preferences of epiphytic bryophytes for forest stand and substrate in North-East Latvia. Folia Cryptog. Estonica 51, 75–83 (2014).Article 

Google Scholar 
74.Manakyan, V. A. Results of bryological studies in Armenia. Arctoa 5, 15–33 (1995).Article 

Google Scholar 
75.Redfearn, P. L., Tan, B. C. & He, S. A newly updated and annotated checklist of Chines mosses. J. Hattori Bot. Lab. 79, 163–357 (1996).
Google Scholar 
76.Kürschner, H. Bryophyte Flora of the Arabian Peninsula and Socotra. Bryophytorum Bibliotheca (JCramer in der Gebrüder Borntraeger Verlagsbuchhandlung, 2000).
Google Scholar 
77.Higuchi, M. & Nishimura, N. Mosses of Pakistan. J. Hattori Bot. Lab. 93, 273–291 (2003).
Google Scholar 
78.Ignatov, M. S., Afonina, O. M. & Ignatova, E. A. Check-list of mosses of East Europe and North Asia. Arctoa 15, 1–130. https://doi.org/10.15298/arctoa.15.01 (2006).Article 

Google Scholar 
79.Sabovljević, M. et al. Check-list of the mosses of SE Europe. Phytol. Balcan. 14(2), 207–244 (2008).
Google Scholar 
80.Dandotiya, D., Govindapyari, H., Suman, S. & Uniyal, P. L. Checklist of the bryophytes of India. Arch. Bryol. 88, 71–72 (2011).
Google Scholar 
81.Hodgetts, N. G. Checklist and Country Status of European bryophytes—Towards a New Red List for Europe. Irish Wildlife Manuals, No. 84. (National Parks and Wildlife Service, Department of Arts, Heritage and the Gaeltacht, 2011). https://www.hdl.handle.net/2262/73373.82.Kürschner, H. & Frey, W. Liverworts, Mosses and Hornworts of Southwest Asia (Marchantiophyta, Bryophyta, Anthoceroptophyta). Nova Hedwigia 139, 179–180 (2011).
Google Scholar 
83.Suzuki, T. A revised new catalog of the mosses of Japan. Hattoria 7, 9–223. https://doi.org/10.18968/hattoria.7.0_9 (2016).Article 

Google Scholar 
84.Kürschner, H. & Frey, W. Liverworts, mosses and hornworts of Afghanistan—our present knowledge. Acta Mus. Siles. Sci. Natur. 68, 11–24 (2019).
Google Scholar 
85.Brotherus, V. F. Enumeratio muscorum Caucasi. Acta Soc. Sci. Fenn. 19, 1–170 (1892).
Google Scholar 
86.Chikovani, N. & Svanidze, T. Checklist of bryophyte species of Georgia. Braun-Blanquetia 34, 97–116. https://doi.org/10.13158/heia.26.1.2013.213 (2004).Article 

Google Scholar 
87.Doroshina, G. Y. New moss records from Georgia. 1. Arctoa 19, 281 (2010).
Google Scholar 
88.Sohrabi, M., Ahti, T. & Urbanavichus, G. Parmelioid lichens of Iran and the caucasus Region. Mycol. Balc. 4, 21–30 (2007).
Google Scholar 
89.Hawksworth, D. L., Blanco, O., Divakar, P. K., Ahti, T. & Crespo, A. A first checklist of parmelioid and similar lichens in Europe and some adjacent territories, adopting revised generic circumscriptions and with indications of species distributions. Lichenologist 40(1), 1–21. https://doi.org/10.1017/S0024282908007329 (2008).Article 

Google Scholar 
90.Syrek, M. & Kukwa, M. Taxonomy of the lichen Cladonia rei and its status in Poland. Biologia 63(4), 493–497. https://doi.org/10.2478/s11756-008-0092-1 (2008).Article 

Google Scholar 
91.Burgaz, A. R., Ahti, T., Inashvili, T., Batsatsashvili, K. & Kupradze, I. Study of georgian Cladoniaceae. Bot. Complut. 42, 19–55. https://doi.org/10.5209/BOCM.61367 (2018).Article 

Google Scholar 
92.Fałtynowicz, W. The lichens, lichenicolous and allied fungi of Poland. An annotated checklist. In Biodiversity of Poland (ed. Mirek, A.) 1–435 (W. Szafer Institute of Botany, Polish Academy of Sciences, 2003).
Google Scholar 
93.Plášek, V., Sawicki, J., Ochyra, R., Szczecińska, M. & Kulik, T. New taxonomical arrangement of the traditionally conceived genera Orthotrichum and Ulota (Orthotrichaceae, Bryophyta). Acta Mus. Sil. 64, 169–174. https://doi.org/10.1515/cszma-2015-0024 (2015).Article 

Google Scholar 
94.Lara, F. et al. Lewinskya, a new genus to accommodate the phaneroporous and monoicous taxa of Orthotrichum (Bryophyta, Orthotrichaceae). Cryptog. Bryol. 37, 361–382. https://doi.org/10.7872/cryb/v37.iss4.2016.361 (2016).Article 

Google Scholar 
95.Sawicki, J. et al. Mitogenomic analyses support the recent division of the genus Orthotrichum (Orthotrichaceae, Bryophyta). Sci. Rep. 7, 4408. https://doi.org/10.1038/s41598-017-04833-z (2017).ADS 
CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
96.Kürschner, H., Batsatsashvili, K. & Parolly, G. Noteworthy additions to the bryophyte flora of Georgia. Herzogia 26, 213–216. https://doi.org/10.13158/heia.26.1.2013.213 (2013).Article 

Google Scholar 
97.Ellis, L. T. et al. New national and regional bryophyte records, 49. J. Bryol. 38(4), 327–347 (2016).Article 

Google Scholar 
98.Ellis, L. T. et al. New national and regional bryophyte records, 51. J. Bryol. 39(2), 177–190 (2017).Article 

Google Scholar 
99.Eckstein, J., Garilleti, R. & Lara, F. Lewinskya transcaucasica (Orthotrichaceae, Bryopsida) sp. nov. A contribution to the bryophyte flora of Georgia. J. Bryol. 40(1), 31–38. https://doi.org/10.1080/03736687.2017.1365218 (2018).Article 

Google Scholar 
100.Eckstein, J. & Zündorf, H.-J. Orthotrichaceous mosses (Orthotricheae, Orthotrichaceae) of the Genera Lewinskya, Nyholmiella, Orthotrichum, Pulvigera and Ulota Contributions to the bryophyte flora of Georgia 1. Cryptog. Bryol. 38(4), 365–382. https://doi.org/10.7872/cryb/v38.iss4.2017.365 (2017).Article 

Google Scholar 
101.Schäfer-Verwimp, A. Orthotrichum Hedw. In Die Moose Baden-Württembergs. Band 2: Spezieller Teil (Bryophytina II, Schistostegales bis Hypnobryales) (eds Nebel, M. & Philippi, G.) 170–197 (Eugen Ulmer, 2001).
Google Scholar 
102.Lara, F. & Garilleti, R. Orthotrichum Hedw. In Flora briofítica Ibérica (eds Guerra, J. & Brugués, C. M.) 50–135 (Universidad de Murcia Sociedad Española de Briología, 2014).
Google Scholar 
103.Lewinsky, J. The genus Orthotrichum Hedw. (Orthotrichaceae, Musci) in Southeast Asia. A taxonomic revision. J. Hattori Bot. Lab. 72, 1–88 (1992).
Google Scholar 
104.Schäfer-Verwimp, A. & Gruber, J. P. Orthotrichum (Orthotrichaceae, Bryopsida) in Pakistan. Trop. Bryol. 21, 1–9. https://doi.org/10.11646/bde.21.1.2 (2002).Article 

Google Scholar 
105.Draper, I., Mazimpaka, V., Albertos, B., Garilleti, R. & Lara, F. A survey of the epiphytic bryophyte flora of the Rif and Tazzeka Mountains (northern Morocco). J. Bryol. 27, 23–34. https://doi.org/10.1179/174328205X40554 (2005).Article 

Google Scholar 
106.Brassard, G. R. Orthotrichum stramineum new to North America. Bryologist 87, 168 (1984).Article 

Google Scholar 
107.Lewinsky-Haapasaari, J. & Long, D. G. Orthotrichum stramineum Hornsch. new to China. J. Bryol. 19, 350–352. https://doi.org/10.1179/jbr.1996.19.2.350 (1996).Article 

Google Scholar 
108.Plášek, V. et al. A synopsis of Orthotrichum s. lato (Bryophyta, Orthotrichaceae) in China, with distribution maps and a key to determination. Plants 10, 499. https://doi.org/10.3390/plants10030499 (2021).Article 
PubMed 
PubMed Central 

Google Scholar  More

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

Google Scholar 
2.Tews, J. et al. Animal species diversity driven by habitat heterogeneity/diversity: The importance of keystone structures—Animal species diversity driven by habitat heterogeneity. J. Biogeogr. 31, 79–92 (2004).Article 

Google Scholar 
3.Graham, N. A. J. & Nash, K. L. The importance of structural complexity in coral reef ecosystems. Coral Reefs 32, 315–326 (2013).ADS 
Article 

Google Scholar 
4.Reimchen, T. E. Substratum heterogeneity, crypsis, and colour polymorphism in an intertidal snail (Littorina mariae). Can. J. Zool. 57, 1070–1085 (1979).Article 

Google Scholar 
5.Petren, K. & Case, T. J. Habitat structure determines competition intensity and invasion success in gecko lizards. Proc. Natl. Acad. Sci. 95, 11739–11744 (1998).ADS 
CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
6.Gratwicke, B. & Speight, M. R. The relationship between fish species richness, abundance and habitat complexity in a range of shallow tropical marine habitats. J. Fish Biol. 66, 650–667 (2005).Article 

Google Scholar 
7.Williams, S. E., Marsh, H. & Winter, J. Spatial scale, species diversity, and habitat structure: Small mammals in Australian tropical rain forest. Ecology 83, 1317–1329 (2002).Article 

Google Scholar 
8.Renoult, J. P., Kelber, A. & Schaefer, H. M. Colour spaces in ecology and evolutionary biology. Biol. Rev. 92, 292–315 (2017).PubMed 
Article 

Google Scholar 
9.Cuthill, I. C. et al. The biology of color. Science 357, eaan0221 (2017).PubMed 
Article 
CAS 

Google Scholar 
10.Guilford, T. & Dawkins, M. S. Receiver psychology and the evolution of animal signals. Anim. Behav. 42, 1–14 (1991).Article 

Google Scholar 
11.Crook, A. C. Colour patterns in a coral reef fish is background complexity important?. J. Exp. Mar. Biol. Ecol. 217, 237–252 (1997).Article 

Google Scholar 
12.Marshall, J. Communication and camouflage with the same ‘bright’ colours in reef fishes. Philos. Trans. R. Soc. Lond. B. Biol. Sci. 355, 1243–1248 (2000).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
13.Seehausen, O. et al. Speciation through sensory drive in cichlid fish. Nature 455, 620–626 (2008).ADS 
CAS 
PubMed 
Article 

Google Scholar 
14.Wilkins, L., Marshall, N. J., Johnsen, S. & Osorio, D. Modelling colour constancy in fish: Implications for vision and signalling in water. J. Exp. Biol. 219, 1884–1892 (2016).PubMed 

Google Scholar 
15.Osorio, D. & Vorobyev, M. A review of the evolution of animal colour vision and visual communication signals. Vis. Res. 48, 2042–2051 (2008).CAS 
PubMed 
Article 

Google Scholar 
16.Caley, J. & St John, J. Refuge availability structures assemblages of tropical reef fishes. J. Anim. Ecol. 45, 414–428 (1996).Article 

Google Scholar 
17.Connolly, S. R., Hughes, T. P., Bellwood, D. R. & Karlson, R. H. Community structure of corals and reef fishes at multiple scales. Science 309, 1363–1365 (2005).ADS 
CAS 
PubMed 
Article 

Google Scholar 
18.Allen, G. R. & Steene, R. Indo-Pacific Coral Reef Field Guide (Tropical Reef Research, 1994).
Google Scholar 
19.Bellwood, D. R. Regional-scale assembly rules and biodiversity of coral reefs. Science 292, 1532–1535 (2001).ADS 
CAS 
PubMed 
Article 

Google Scholar 
20.Humann, P., DeLoach, N., Allen, G. & Steene, G. Reef Fish Identification: Tropical Pacific (New World Publications, 2015).
Google Scholar 
21.Barneche, D. R. et al. Body size, reef area and temperature predict global reef-fish species richness across spatial scales. Glob. Ecol. Biogeogr. 28, 315–327 (2019).Article 

Google Scholar 
22.Brandl, S. J., Goatley, C. H. R., Bellwood, D. R. & Tornabene, L. The hidden half: ecology and evolution of cryptobenthic fishes on coral reefs: Cryptobenthic reef fishes. Biol. Rev. 93, 1846–1873 (2018).PubMed 
Article 

Google Scholar 
23.Carr, M. H., Anderson, T. W. & Hixon, M. A. Biodiversity, population regulation, and the stability of coral-reef fish communities. Proc. Natl. Acad. Sci. 99, 11241–11245 (2002).ADS 
CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
24.Hixon, M. A. 60 years of coral reef fish ecology: Past, present, future. Bull. Mar. Sci. 87, 727–765 (2011).Article 

Google Scholar 
25.Stuart-Smith, R. D. et al. Integrating abundance and functional traits reveals new global hotspots of fish diversity. Nature 501, 539–542 (2013).ADS 
CAS 
PubMed 
Article 

Google Scholar 
26.Froese, R. & Pauly, D. FishBase. World Wide Web electronic publication. http://www.fishbase.org (2019).27.Marshall, N. J., Jennings, K., McFarland, W. N., Loew, E. R. & Losey, G. S. Visual biology of Hawaiian coral reef fishes. II. Colors of Hawaiian coral reef fish. Copeia 2003, 455–466 (2003).Article 

Google Scholar 
28.Merilaita, S. Visual background complexity facilitates the evolution of camouflage. Evolution 57, 1248–1254 (2003).PubMed 
Article 

Google Scholar 
29.Matz, M. V., Lukyanov, K. A. & Lukyanov, S. A. Family of the green fluorescent protein: Journey to the end of the rainbow. BioEssays 24, 953–959 (2002).CAS 
PubMed 
Article 

Google Scholar 
30.Alieva, N. O. et al. Diversity and evolution of coral fluorescent proteins. PLoS ONE 3, e2680 (2008).ADS 
PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
31.Salih, A., Larkum, A., Cox, G., Kühl, M. & Hoegh-Guldberg, O. Fluorescent pigments in corals are photoprotective. Nature 408, 850–853 (2000).ADS 
CAS 
PubMed 
Article 

Google Scholar 
32.Veron, J., Stafford-Smith, M., DeVantier, L. & Turak, E. Overview of distribution patterns of zooxanthellate Scleractinia. Front. Mar. Sci. 1, 81 (2015).Article 

Google Scholar 
33.Matz, M. V., Marshall, N. J. & Vorobyev, M. Are corals colorful?. Photochem. Photobiol. 82, 345 (2006).CAS 
PubMed 
Article 

Google Scholar 
34.Marshall, N. J., Jennings, K., McFarland, W. N., Loew, E. R. & Losey, G. S. Visual biology of Hawaiian coral reef fishes. III. Environmental light and an integrated approach to the ecology of reef fish vision. Copeia 2003, 467–480 (2003).Article 

Google Scholar 
35.Neumeyer, C. Color vision in fishes and its neural basis. In Sensory Processing in Aquatic Environments (eds Collin, S. P. & Marshall, N. J.) 223–235 (Springer, 2003). https://doi.org/10.1007/978-0-387-22628-6_11.Chapter 

Google Scholar 
36.Oswald, F. et al. Contributions of host and symbiont pigments to the coloration of reef corals. FEBS J. 274, 1102–1122 (2007).CAS 
PubMed 
Article 

Google Scholar 
37.Schweikert, L. E., Fitak, R. R., Caves, E. M., Sutton, T. T. & Johnsen, S. Spectral sensitivity in ray-finned fishes: Diversity, ecology, and shared descent. J. Exp. Biol. https://doi.org/10.1242/jeb.189761 (2018).Article 
PubMed 

Google Scholar 
38.Veron, J. E. N., Stafford-Smith., M. G., Turak, E. & DeVantier, L. M. Corals of the World. www.coralsoftheworld.org (2020). Accessed April 2019.39.Weller, H. I. & Westneat, M. W. Quantitative color profiling of digital images with earth mover’s distance using the R package colordistance. PeerJ 7, e6398 (2019).PubMed 
PubMed Central 
Article 

Google Scholar 
40.Cox, K., Woods, M. & Reimchen, T. E. Coral species richness, coral hue, and reef fish richness across 74 ecoregions within four oceanic basins. Figshare https://doi.org/10.6084/m9.figshare.12317591 (2020).41.R Core Team. R: A Language and Environment for Statistical Computing (R Foundation for Statistical Computing, 2019).
Google Scholar 
42.The Ocean Agency & XL Catlin Seaview Survey. Coral Reef Image Bank. www.coralreefimagebank.org (2019). Accessed April 2019.43.Choat, J. H. & Bellwood, D. R. Reef fishes: Their history and evolution. In The Ecology of Fishes on Coral Reefs (ed. Sale, P. F.) 39–66 (Academic Press, 1991).Chapter 

Google Scholar 
44.Jones, G. P., Barone, G., Sambrook, K. & Bonin, M. C. Isolation promotes abundance and species richness of fishes recruiting to coral reef patches. Mar. Biol. 167, 1–13 (2020).Article 
CAS 

Google Scholar 
45.Lirman, D. et al. Severe 2010 cold-water event caused unprecedented mortality to corals of the florida reef tract and reversed previous survivorship patterns. PLoS ONE 6, e23047 (2011).ADS 
CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
46.Habary, A., Johansen, J. L., Nay, T. J., Steffensen, J. F. & Rummer, J. L. Adapt, move or die: How will tropical coral reef fishes cope with ocean warming?. Glob. Change Biol. 23, 566–577 (2017).ADS 
Article 

Google Scholar 
47.Almany, G. R. & Webster, M. S. The predation gauntlet: Early post-settlement mortality in reef fishes. Coral Reefs 25, 19–22 (2006).ADS 
Article 

Google Scholar 
48.Brandl, S. J. et al. Demographic dynamics of the smallest marine vertebrates fuel coral reef ecosystem functioning. Science 364, 1189–1192 (2019).ADS 
CAS 
PubMed 
Article 

Google Scholar 
49.Coker, D. J., Wilson, S. K. & Pratchett, M. S. Importance of live coral habitat for reef fishes. Rev. Fish Biol. Fish. 24, 89–126 (2014).Article 

Google Scholar 
50.Coker, D. J., Pratchett, M. S. & Munday, P. L. Coral bleaching and habitat degradation increase susceptibility to predation for coral-dwelling fishes. Behav. Ecol. 20, 1204–1210 (2009).Article 

Google Scholar 
51.Sale, P. F. Maintenance of high diversity in coral reef fish communities. Am. Nat. 111, 337–359 (1977).Article 

Google Scholar 
52.Munday, P. L. Competitive coexistence of coral-dwelling fishes: The lottery hypothesis revisited. Ecology 85, 623–628 (2004).Article 

Google Scholar 
53.Hixon, M. A. Synergistic predation, density dependence, and population regulation in marine fish. Science 277, 946–949 (1997).CAS 
Article 

Google Scholar 
54.Endler, J. A. & Thery, M. Interacting effects of Lek placement, display behavior, ambient light, and color patterns in three neotropical forest-dwelling birds. Am. Nat. 148, 421–452 (1996).Article 

Google Scholar 
55.Reimchen, T. E. Shell colour ontogeny and tubeworm mimicry in a marine gastropod Littorina mariae. Biol. J. Linn. Soc. 36, 97–109 (1989).Article 

Google Scholar 
56.Sparks, J. S. et al. The covert world of fish biofluorescence: A phylogenetically widespread and phenotypically variable phenomenon. PLoS ONE 9, e83259 (2014).ADS 
PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
57.Allen, J. J., Akkaynak, D., Sugden, A. U. & Hanlon, R. T. Adaptive body patterning, three-dimensional skin morphology and camouflage measures of the slender filefish Monacanthus tuckeri on a Caribbean coral reef. Biol. J. Linn. Soc. 116, 377–396 (2015).Article 

Google Scholar 
58.Cheney, K. L., Skogh, C., Hart, N. S. & Marshall, N. J. Mimicry, colour forms and spectral sensitivity of the bluestriped fangblenny, Plagiotremus rhinorhynchos. Proc. R. Soc. B Biol. Sci. 276, 1565–1573 (2009).Article 

Google Scholar 
59.Stevens, M., Lown, A. E. & Denton, A. M. Rockpool gobies change colour for camouflage. PLoS ONE 9, e110325 (2014).ADS 
PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
60.Gilby, B. L. et al. Colour change in a filefish (Monacanthus chinensis) faced with the challenge of changing backgrounds. Environ. Biol. Fishes 98, 2021–2029 (2015).Article 

Google Scholar 
61.Barnett, J. B. & Cuthill, I. C. Distance-dependent defensive coloration. Curr. Biol. 24, R1157–R1158 (2014).CAS 
PubMed 
Article 

Google Scholar 
62.Vega Thurber, R. L. et al. Chronic nutrient enrichment increases prevalence and severity of coral disease and bleaching. Glob. Change Biol. 20, 544–554 (2014).ADS 
Article 

Google Scholar 
63.Hughes, T. P. et al. Global warming and recurrent mass bleaching of corals. Nature 543, 373–377 (2017).ADS 
CAS 
PubMed 
Article 

Google Scholar 
64.Ortiz, J.-C. et al. Impaired recovery of the great barrier reef under cumulative stress. Sci. Adv. 4, eaar6127 (2018).ADS 
PubMed 
PubMed Central 
Article 

Google Scholar 
65.Grottoli, A. G., Rodrigues, L. J. & Palardy, J. E. Heterotrophic plasticity and resilience in bleached corals. Nature 440, 1186–1189 (2006).ADS 
CAS 
PubMed 
Article 

Google Scholar 
66.Roff, G. et al. Porites and the Phoenix effect: Unprecedented recovery after a mass coral bleaching event at Rangiroa Atoll, French Polynesia. Mar. Biol. 161, 1385–1393 (2014).Article 

Google Scholar 
67.Adjeroud, M. et al. Recovery of coral assemblages despite acute and recurrent disturbances on a South Central Pacific reef. Sci. Rep. 8, 1–8 (2018).ADS 
CAS 
Article 

Google Scholar 
68.Tittensor, D. P. et al. Global patterns and predictors of marine biodiversity across taxa. Nature 466, 1098–1101 (2010).ADS 
CAS 
PubMed 
Article 

Google Scholar 
69.Soetaert, K. plot3D: Plotting Multi-Dimensional Data R package version 1.4. https://CRAN.R-project.org/package=plot3D (2021).70.Sarkar, D. Lattice: Multivariate Data Visualization with R (Springer, 2008).MATH 
Book 

Google Scholar 
71.Wickham, H. ggplot2: Elegant Graphics for Data Analysis (Springer, 2016).MATH 
Book 

Google Scholar 
72.Schneider, C. A., Rasband, W. S. & Eliceiri, K. W. NIH image to ImageJ: 25 years of image analysis. Nat. Methods 9, 671–675 (2012).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
73.Centore, P. sRGB centroids for the ISCC-NBS colour system. Munsell Colour Sci. Paint. 21, 1–21 (2016).
Google Scholar 
74.Kelly, K. L. Central notations for the revised ISCC-NBS color-name blocks. J. Res. Natl. Bur. Stand. 61, 427 (1958).Article 

Google Scholar 
75.Viechtbauer, W. Conducting meta-analyses in R with the metafor package. J. Stat. Softw. 36, 1–48 (2010).Article 

Google Scholar  More

1.McNaughton, S. J., Ruess, R. W. & Seagle, S. W. Large mammals and process dynamics in Aftican ecosystems. Bioscience 38, 794–800 (1988).Article 

Google Scholar 
2.Vanni, M. J. Nutrient cycling by animals in freshwater ecosystems. Annu. Rev. Ecol. Syst. 33, 341–370 (2002).Article 

Google Scholar 
3.Schmitz, O. J. et al. Animating the carbon cycle. Ecosystems 17, 344–359 (2014).CAS 
Article 

Google Scholar 
4.Doughty, C. E. et al. Global nutrient transport in a world of giants. Proc. Natl Acad. Sci. USA 113, 868–873 (2016).ADS 
CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
5.Allgeier, J. E., Burkepile, D. E. & Layman, C. A. Animal pee in the sea: consumer-mediated nutrient dynamics in the world’s changing oceans. Glob. Change Biol. 23, 2166–2178 (2017).ADS 
Article 

Google Scholar 
6.Duffy, J. E. Biodiversity and ecosystem function: the consumer connection. Oikos 99, 201–219 (2002).Article 

Google Scholar 
7.Cardinale, B. J. et al. Biodiversity loss and its impact on humanity. Nature 486, 59–67 (2012).ADS 
CAS 
Article 

Google Scholar 
8.Loreau, M. et al. Biodiversity and ecosystem functioning: current knowledge and future challenges. Science 294, 804–808 (2001).ADS 
CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
9.Hooper, D. U. et al. Effects of biodiversity on ecosystem functioning: a consensus of current knowledge. Ecol. Monogr. 75, 3–35 (2005).Article 

Google Scholar 
10.McIntyre, P. B., Jones, L. E., Flecker, A. S. & Vanni, M. J. Fish extinctions alter nutrient recycling in tropical freshwaters. Proc. Natl Acad. Sci. USA 104, 4461–4466 (2007).ADS 
CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
11.Pigot, A. L. et al. Macroevolutionary convergence connects morphological form to ecological function in birds. Nat. Ecol. Evolution 4, 230–239 (2020).Article 

Google Scholar 
12.Harvey, P. H. & Pagel, M. D. The Comparative Method in Evolutionary Biology. (Oxford University Press, 1991).13.Wiens, J. J. et al. Niche conservatism as an emerging principle in ecology and conservation biology. Ecol. Lett. 13, 1310–1324 (2010).PubMed 
Article 
PubMed Central 

Google Scholar 
14.Weeks, B., Claramunt, S. & Cracraft, J. Integrating systematics and biogeography to disentangle the roles of history and ecology in biotic assembly. J. Biogeogr. 43 (2016).15.Reiners, W. A. Complementary models for ecosystems. Am. Nat. 127, 59–73 (1986).Article 

Google Scholar 
16.Schreck, C. B. & Moyle, P. B. Methods for Fish Biology. (American Fisheries Society, 1990).17.Sterner, R. W. & Elser, J. J. Ecological Stoichiometry: The Biology of Elements from Molecules to the Biosphere. 429 (2002).18.Vaitla, B. et al. Predicting nutrient content of ray-finned fishes using phylogenetic information. Nat. Commun. 9, 3742 (2018).ADS 
PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
19.Gonzalez, A. L. et al. Ecological mechanisms and phylogeny shape invertebrate stoichiometry: a test using detritus-based communities across Central and South America. Funct. Ecol. 32, 2448–2463 (2018).Article 

Google Scholar 
20.Atkinson, C. L., van Ee, B. C. & Pfeiffer, J. M. Evolutionary history drives aspects of stoichiometric niche variation and functional effects within a guild. Ecology 101, e03100 (2020).PubMed 
Article 
PubMed Central 

Google Scholar 
21.Schluter, D. The Ecology of Adaptive Radiation. (OUP Oxford, 2000).22.Allgeier, J. E., Wenger, S. & Layman, C. A. Taxonomic identity best explains variation in body nutrient stoichiometry in a diverse marine animal community. Sci. Rep. 10, 13718 (2020).ADS 
CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
23.Allgeier, J. E., Wenger, S. J., Schindler, D. E., Rosemond, A. D. & Layman, C. A. Metabolic theory and taxonomic identity predict nutrient cycling in a diverse food web. Proc. Natl Acad. Sci. USA 112, 2640–2647 (2015).Article 
CAS 

Google Scholar 
24.Odum, H. T. & Odum, E. P. Trohic structure and productivity of a windward coral reef community on Eniwetok Atoll. Ecol. Monogr. 25, 291–320 (1955).Article 

Google Scholar 
25.Hatcher, B. G. Coral reef primary productivity—a beggars banquet. Trends Ecol. Evolut. 3, 106–111 (1988).CAS 
Article 

Google Scholar 
26.Deangelis, D. L. Energy-flow, nutrient cycling, and ecosystem resilience. Ecology 61, 764–771 (1980).Article 

Google Scholar 
27.Allgeier, J. E., Valdivia, A., Cox, C. & Layman, C. A. Fishing down nutrients on coral reefs. Nat. Commun. 7, 1–5 (2016).Article 
CAS 

Google Scholar 
28.Allgeier, J. E., Layman, C. A., Mumby, P. J. & Rosemond, A. D. Consistent nutrient storage and supply mediated by diverse fish communities in coral reef ecosystems. Glob. Change Biol. 20, 2459–2472 (2014).ADS 
Article 

Google Scholar 
29.Allgeier, J. E., Layman, C. A., Mumby, P. J. & Rosemond, A. D. Biogeochemical implications of biodiversity loss across regional gradients of coastal marine ecosystems. Ecol. Monogr. 85, 132 (2015).Article 

Google Scholar 
30.Bellwood, D. R. & Wainwright, P. C. CHAPTER 1—The History and Biogeography of Fishes on Coral Reefs. in Coral Reef Fishes (ed Sale, P. F.) 5–32 (Academic Press, 2002). https://doi.org/10.1016/B978-012615185-5/50003-7.31.Littler, M. M., Littler, D. S. & Titlyanov, E. A. Comparisons of N- and P-limited productivity between high granitic islands versus low carbonate atolls in the Seychelles Archipelago: a test of the relative-dominance paradigm. Coral Reefs 10, 199–209 (1991).ADS 
Article 

Google Scholar 
32.Haßler, K. et al. Provenance of nutrients in submarine fresh groundwater discharge on Tahiti and Moorea, French Polynesia. Appl. Geochem. 100, 181–189 (2019).Article 
CAS 

Google Scholar 
33.Carew, J. L. & Mylroie, J. E. Geology of the Bahamas. Geol. Hydrogeol. Carbonate Isl. 54, 91–139 (1997).CAS 
Article 

Google Scholar 
34.Allgeier, J. E., Rosemond, A. D., Mehring, A. S. & Layman, C. A. Synergistic nutrient co-limitation across a gradient of ecosystem fragmentation in subtropical mangrove-dominated wetlands. Limnol. Oceanogr. 55, 2660–2668 (2010).ADS 
CAS 
Article 

Google Scholar 
35.Koch, M. S. & Madden, C. J. Patterns of primary production and nutrient availability in a Bahamas lagoon with fringing mangroves. Mar. Ecol. Prog. Ser. 219, 109–119 (2001).ADS 
CAS 
Article 

Google Scholar 
36.Hendrixson, H. A., Sterner, R. W. & Kay, A. D. Elemental stoichiometry of freshwater fishes in relation to phylogeny, allometry and ecology. J. Fish. Biol. 70, 121–140 (2007).Article 

Google Scholar 
37.Vanni, M. J., Flecker, A. S., Hood, J. M. & Headworth, J. L. Stoichiometry of nutrient recycling by vertebrates in a tropical stream: linking species identity and ecosystem processes. Ecol. Lett. 5, 285–293 (2002).Article 

Google Scholar 
38.Vanni, M. J. & McIntyre, P. B. Predicting nutrient excretion of aquatic animals with metabolic ecology and ecological stoichiometry: a global synthesis. Ecology 97, 3460–3471 (2016).PubMed 
Article 
PubMed Central 

Google Scholar 
39.Sokal, R. R. The comparative method in evolutionary biology. (eds Paul H. Harvey, Mark D. Pagel) (Oxford University Press, New York, 1991). viii + 239 pp. ISBN 0-19-854640-8. $24.95 (paper). Am. J. Phys. Anthropol. 88, 405–406 (1992).40.Downs, K. N., Hayes, N. M., Rock, A. M., Vanni, M. J. & González, M. J. Light and nutrient supply mediate intraspecific variation in the nutrient stoichiometry of juvenile fish. Ecosphere 7, e01452 (2016).Article 

Google Scholar 
41.Sterner, R. W. & George, N. B. Carbon, nitrogen, and phosphorus stoichiometry of cyprinid fishes. Ecology 81, 127–140 (2000).Article 

Google Scholar 
42.Brown, W. L. Jr & Wilson, E. O. Character displacement. Syst. Biol. 5, 49–64 (1956).
Google Scholar 
43.Losos, J. B. Ecological character displacement and the study of adaptation. Proc. Natl Acad. Sci. USA 97, 5693–5695 (2000).ADS 
CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
44.Dayan, T. & Simberloff, D. Ecological and community-wide character displacement: the next generation. Ecol. Lett. 8, 875–894 (2005).Article 

Google Scholar 
45.Abrams, P. A. Evolution and the consequences of species introductions and deletions. Ecology 77, 1321–1328 (1996).Article 

Google Scholar 
46.Buchan, K. C. The Bahamas. Mar. Pollut. Bull. 41, 94–111 (2000).CAS 
Article 

Google Scholar 
47.Siu, G. et al. Shore fishes of french polynesia. Cybium 41 (2017).48.Miloslavich, P. et al. Marine biodiversity in the Caribbean: regional estimates and distribution patterns. PloS ONE 5, 119–126 (2010).Article 
CAS 

Google Scholar 
49.Schaus, M. H. & Vanni, M. J. Effects of gizzard shad on phytoplankton and nutrient dynamics: role of sediment feeding and fish size. Ecology 81, 1701–1719 (2000).Article 

Google Scholar 
50.Whiles, M. R., Huryn, A. D., Taylor, B. W. & Reeve, J. D. Influence of handling stress and fasting on estimates of ammonium excretion by tadpoles and fish: recommendations for designing excretion experiments. Limnol. Oceanogr. 7, 1–7 (2009).CAS 
Article 

Google Scholar 
51.Taylor, B. W. et al. Improving the fluorometric ammonium method: matrix effects, background fluorescence, and standard additions. J. North Am. Benthol. Soc. 26, 167–177 (2007).Article 

Google Scholar 
52.APHA. Standard Methods for the Examination of Water and Wastewater. American Public Health Association, American Water Works Association, and Water Pollution Control Federation. (1995).53.Mouillot, D. et al. Functional over-redundancy and high functional vulnerability in global fish faunas on tropical reefs. Proc. Natl Acad. Sci. USA 111, 13757–13762 (2014).ADS 
CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
54.Rabosky, D. L. et al. An inverse latitudinal gradient in speciation rate for marine fishes. Nature 559, 392–395 (2018).ADS 
CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
55.Chang, J., Rabosky, D. L., Smith, S. A. & Alfaro, M. E. An r package and online resource for macroevolutionary studies using the ray-finned fish tree of life. Methods Ecol. Evolut. 10, 1118–1124 (2019).Article 

Google Scholar 
56.Revell, L. J. phytools: an R package for phylogenetic comparative biology (and other things). Methods Ecol. Evolut. 3, 217–223 (2012).Article 

Google Scholar 
57.Hadfield, J. D. & Nakagawa, S. General quantitative genetic methods for comparative biology: phylogenies, taxonomies and multi-trait models for continuous and categorical characters. J. Evolut. Biol. 23, 494–508 (2010).CAS 
Article 

Google Scholar 
58.Hadfield, J. D. MCMC methods for multi-response generalized linear mixed models: the MCMCglmm R Package. J. Stat. Softw. 33, 1–22 (2010).Article 

Google Scholar 
59.Nakagawa, S. & Schielzeth, H. A general and simple method for obtaining R 2 from generalized linear mixed-effects models. Methods Ecol. Evolut. 4, 133–142 (2013).Article 

Google Scholar 
60.Gelman, A. & Hill, J. Data Analysis Using Regression. (Cambridge University Press, 2007).61.Kembel, S. W. et al. Picante: R tools for integrating phylogenies and ecology. Bioinformatics 26, 1463–1464 (2010).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
62.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, 595–602 (2011).PubMed 
Article 
PubMed Central 

Google Scholar  More

The research was carried out on the basis of direct measurements in the surroundings of four selected working coal-fired power plants and four working coking plants. The samples of suspended dust PM10, respirable fraction PM2.5 and submicron particulate matter PM1 were collected in the surroundings of power generation facilities and in the surroundings of coking plants.Location of measurement pointsThe location of the measurement points was selected in southern Poland, around the selected four working coal-fired power plants and four working coking plants. The sampling points in the surroundings of the power plant (P1, P2, P3 and P4) and the coking plant (K1, K2, K3 and K4) were located at the distance of approximately 2 km to the north-east from the respective object (Fig. 1).Figure 1Location of the sampling sites (the map was generated based on data from the BDL18 website).Full size imageThe location of the measurement points was a compromise, taking into account the representativeness of the receptor, the possibility to connect the testing equipment and the consent of the property owners. To eliminate the impact of a heating season, and especially that of low emissions, presented in the studies by19, the measurement sessions were carried out only in the summer season. The samples of particulate matter were collected on a weekly basis, with 4 sessions at one site. The methodology applied in this work is presented in20,21. The location of measurement sites:

point P1: 50° 08′ 37.87″ N; 18° 32′ 15.76″ (Golejów—a suburban district of Rybnik in the Śląskie Voivodeship, in the vicinity of a working power plant with a capacity of 1775 MW; population:

2 300);

point P2: 50° 45′ 35.41″ N; 17° 56′ 20.43″ E (Świerkle—a rural area in the Opolskie Voivodeship (Dobrzeń Wielki commune) near a working power plant with a capacity of 1,492 MW; population: 520);

point P3: 50° 12′ 33.46″ N; 19° 28′ 28.77″ E (Czyżówka—rural area in the Małopolskie Voivodeship (commune of Trzebinia) near a working power plant with a capacity of 786 MW; population: 700);

point P4: 50° 13′ 48.90″ N; 19° 13′ 24.45″ E (suburbs of Jaworzno (Śląskie Voivodeship) in the vicinity of a 1,345 MW power plant; number of inhabitants: 95 500);

K1 point: 50° 10′ 11.36″ N; 18° 40′ 34.35″ E (Czerwionka—Leszczyny in the Śląskie Voivodeship, in the vicinity of a small coking plant; number of inhabitants: 27 300);

K2 point: 50° 3′ 19.76″ N; 18° 30′ 21.69″ E (Popielów—a suburban district of Rybnik in the Śląskie Voivodeship, surrounded by a small working coking plant; population:3 300);

K3 point: 50° 21′ 24.08″ N; 19° 21′ 37.46″ E (Łęka—Dąbrowa Górnicza district, in the Śląskie Voivodeship, surrounded by a large coking plant; number of inhabitants: 700);

K4 point: 50° 21′ 0.47″ N; 18° 53′ 15.44″ E (Bytom—a city in the Śląskie Voivodeship, a small coking plant located on the outskirts of the city; population: 174 700).

The state of air pollution with particulate matter in the area investigated in the study is affected by various local sources of pollution emissions. At the measurement sites P1, P2, P3 and P4, the emissions are mainly from power plant chimneys, but also from auxiliary processes, i.e. coal storage and its transport. In addition, the recorded emissions are also influenced by other industrial plants operating in the vicinity of the measurement sites, domestic and municipal sector and the impact of automotive industry. The measurement sites K1, K2, K3 and K4 involve primarily the emissions accompanying the processes of coal coking as well as auxiliary processes, i.e. coal deposition, its transmission, management of products and post-production wastes. Additionally, they are affected by the emissions from industrial plants and low emission sources operating in this area, as well as the emission from the combustion of solid fuels for domestic or municipal purposes, as well as by the automotive industry.Sampling processThe samples of suspended dust (PM10), respirable fraction (PM2.5) and submicron particulate matter (PM1) were collected using the Dekati PM10 cascade impactor serial No. 6648 by Dekati (Finland) with the air flow rate of (1.8 {mathrm{m}}^{3}/mathrm{h}). The impactor Dekati PM10 guarantees the collection of dust samples for three cutpoint diameters: 10 μm, 2.5 μm and 1 μm. For the sampling at the first, second and third stages of the impactor, polycarbonate filters were used (Nuclepore 800 203, with the diameter of 25 mm, by Whatman International Ltd., Maidstone, UK). At the fourth stage, the dust was collected on a Teflon filter for particles ≤ 1 μm in diameter (Pall Teflo R2PJ047, 47 mm in diameter, by Pall International Ltd., New York, NY, USA). The average volume of air passing through the filters was approximately 300 m3. The impactor’s capture efficiency was characterized by the uncertainty below 2.8%. The mass of dust collected at the individual stages of the impactor was determined by the gravimetric method, and it was referenced to the volume of passed air (left(mathrm{mu g}/{mathrm{m}}^{3}right)) according to the PN-EN1234122. All impactor samples were analysed by inductively coupled plasma mass spectrometry (ICP-MS).The samples were collected at a height of 1.5 m from the ground, i.e. in the breathing zone for people. The respective dust fractions were collected in 7-day cycles from 28 May to 24 September 2014 (16 weeks) in the surroundings of four working coal-fired power plants and from 4 May to 28 August 2015 (16 weeks) in the surroundings of four working coking plants. The measurement campaign comprised four measurement sessions separately for each sampling site. One session comprised dust sampling at each stage of the Dekati PM10 cascade impactor and filters used for reference. The filters were taken back after study period and labeled during the collection process in the field and stored in the plastic containers for safe transportation and storage in laboratory for further analysis.In each measurement session, blind filters were stored at the sampling site, but they were not subjected to exposure. The sample data were corrected from these blanks. The length of the measurement cycles was conditioned by the need to collect an appropriate amount of research material (with the aerodynamic diameter of the dust grains  10 μm). Analogous (7-day) periods of dust sampling were used in the studies by4,23.Polycarbonate and Teflon filters were conditioned before and after dust collection at a temperature of 20 ± 1 °C (relative humidity 50%(pm ) 5%) for 48 h, and then weighed on a microbalance with an accuracy of 1 (mathrm{mu g}) (MXA5/1, by RADWAG, Poland).Taking into account the measurement sessions at four sites in the surroundings of the power plant (P1 (div) P4) and at four sites in the surroundings of the coking plant (K1 (div) K4), the aggregate number of samples exceeded 450.Chemical analysisThe qualitative and quantitative analysis of the obtained solutions was performed by inductively coupled plasma mass spectrometry using an ICP-MS instrument (NexION 300D, PerkinElmer, Inc., Waltham, MA, USA). For all elements determined simultaneously, the same parameters of the instrument were used, which are presented in the publications20,21,24.As standards for the determination of 75As, 111Cd, 59Co, 53Cr, 200Hg, 55Mn, 60Ni, 206Pb, 121Sb and 82Se, we applied the 1000 (mathrm{mu g}/{mathrm{cm}}^{3}) CertPUR ICP multi-element standard solution VI for ICP-MS by Merck, Germany. Ten repetitions were performed for all samples. The determined limits of detection (LOD) were based on 10 independent measurements for blank test. For the results obtained in that way, the mean value and the value of the standard deviation SD were calculated. The values of LOD for individual elements were determined on the basis of the dependence (1):$$mathrm{LOD}= {mathrm{x}}_{mathrm{sr}}+ 3mathrm{SD}$$
(1)

where: xśr—mean concentration value of the element, (mathrm{g}/{mathrm{dm}}^{3}), SD—standard deviation.The determination correctness of the content of the elements was verified with the use of certified reference materials: European Reference Material ERM-CZ120 and Standard Reference Material SRM 1648a (National Institute of Standards and Technology, USA). The recovery with the use of the said certified reference materials was respectively as follows: As (111% for ERM-CZ120 and 96% for SRM 1648a), Cd (97% and 105%), Co (108% and 97%), Cr (103% and 94%), Mn (106% and 100%), Ni (107% and 102%), Pb (107% and 105%) and Sb (99% and 91%). The certified reference materials did not contain Hg or Se. More

1.Suttle, C. A. Marine viruses — major players in the global ecosystem. Nat. Rev. Microbiol. 5, 801–812 (2007).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
2.Rohwer, F. & Thurber, R. V. Viruses manipulate the marine environment. Nature 459, 207–212 (2009).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
3.Simmonds, P. et al. Virus taxonomy in the age of metagenomics. Nat. Rev. Microbiol. 15, 161–168 (2017).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
4.Fuhrman, J. A. Marine viruses and their biogeochemical and ecological effects. Nature 399, 541–548 (1999).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
5.Suttle, C. A. Viruses in the sea. Nature 437, 356–361 (2005).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
6.Jiang, S., Steward, G., Jellison, R., Chu, W. & Choi, S. Abundance, distribution, and diversity of viruses in alkaline, hypersaline Mono Lake, California. Microb. Ecol. 47, 9–17 (2004).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
7.Williamson, K. E., Fuhrmann, J. J., Wommack, K. E. & Radosevich, M. Viruses in soil ecosystems: an unknown quantity within an unexplored territory. Annu. Rev. Virol. 4, 201–219 (2017).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
8.Cai, L. et al. Active and diverse viruses persist in the deep sub-seafloor sediments over thousands of years. ISME J. 13, 1857–1864 (2019).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
9.Wei, M. & Xu, K. New insights into the virus-to-prokaryote ratio (VPR) in marine sediments. Front. Microbiol. 11, 1102 (2020).PubMed 
PubMed Central 
Article 

Google Scholar 
10.Wilhelm, S. W. & Suttle, C. A. Viruses and nutrient cycles in the sea. BioScience 49, 781–788 (1999).Article 

Google Scholar 
11.Brussaard, C. P. D. et al. Global-scale processes with a nanoscale drive: the role of marine viruses. ISME J. 2, 575–578 (2008).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
12.Howard-Varona, C. et al. Phage-specific metabolic reprogramming of virocells. ISME J. 14, 881–895 (2020).PubMed 
PubMed Central 
Article 

Google Scholar 
13.Nee, S. & Maynard Smith, J. The evolutionary biology of molecular parasites. Parasitology 100, S5–S18 (1990).PubMed 
Article 
PubMed Central 

Google Scholar 
14.Hambly, E. & Suttle, C. A. The viriosphere, diversity, and genetic exchange within phage communities. Curr. Opin. Microbiol. 8, 444–450 (2005).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
15.Sullivan, M. B. et al. Prevalence and evolution of core photosystem II genes in marine cyanobacterial viruses and their hosts. PLoS Biol. 4, e234 (2006).PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
16.Holmes, E. C. What does virus evolution tell us about virus origins? J. Virol. 85, 5247–5251 (2011).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
17.Wolf, Y. I. et al. Origins and evolution of the global RNA virome. mBio 9, e02329-18 (2018).PubMed 
PubMed Central 
Article 

Google Scholar 
18.Kuhn, J. H. et al. Classify viruses-the gain is worth the pain. Nature 566, 318–320 (2019).PubMed 
Article 
PubMed Central 

Google Scholar 
19.Record, N. R., Talmy, D. & Våge, S. Quantifying tradeoffs for marine viruses. Front. Mar. Sci. https://doi.org/10.3389/fmars.2016.00251 (2016). Investigates trade-offs in phenotypes of marine viruses that may influence virus population dynamics and biogeography.Article 

Google Scholar 
20.Domingo, E. et al. Basic concepts in RNA virus evolution. FASEB J. 10, 859–864 (1996).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
21.Solé, R. V., Ferrer, R., González-García, I., Quer, J. & Domingo, E. Red queen dynamics, competition and critical points in a model of RNA virus quasispecies. J. Theor. Biol. 198, 47–59 (1999).PubMed 
Article 
PubMed Central 

Google Scholar 
22.Stern, A. & Sorek, R. The phage-host arms race: shaping the evolution of microbes. Bioessays 33, 43–51 (2011).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
23.Daugherty, M. D. & Malik, H. S. Rules of engagement: molecular insights from host-virus arms races. Annu. Rev. Genet. 46, 677–700 (2012).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
24.Tegally, H. et al. Sixteen novel lineages of SARS-CoV-2 in South Africa. Nat. Med. 27, 440–446 (2021).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
25.Lederberg, J. in Emerging Viruses (ed. Morse, S. S.) 3–9 (Oxford University Press, 1993).26.Baltimore, D. Expression of animal virus genomes. Microbiol. Mol. Biol. Rev. 35, 235–241 (1971).CAS 

Google Scholar 
27.Coutinho, F. H., Edwards, R. A. & Rodríguez-Valera, F. Charting the diversity of uncultured viruses of archaea and bacteria. BMC Biol. 17, 109 (2019).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
28.King, A. M. Q., Adams, M. J., Carstens, E. B. & Lefkowitz, E. J. (eds) Virus Taxonomy. 163–173 (Elsevier, 2012).29.Forterre, P. The virocell concept and environmental microbiology. ISME J. 7, 233–236 (2013). Among the first reports articulating the viewpoint that infected cells undergoing active virus replication should be recognized as the ‘living form’ of a virus known as a virocell.CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
30.Lowen, A. C. Constraints, drivers, and implications of influenza A virus reassortment. Annu. Rev. Virol. 4, 105–121 (2017).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
31.Mahner, M. & Kary, M. What exactly are genomes, genotypes and phenotypes? And what about phenomes? J. Theor. Biol. 186, 55–63 (1997).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
32.Edwards, K. F. & Steward, G. F. Host traits drive viral life histories across phytoplankton viruses. Am. Nat. 191, 566–581 (2018). Examines the inter-relationships between virus traits and their consequences for population dynamics and the evolution of burst size.PubMed 
Article 
PubMed Central 

Google Scholar 
33.Flint, S. J., Racaniello, V. R., Rall, G. F., Skalka, A. M. & Enquist, L. W. Principles of Virology 4th Edn (Wiley, 2015).34.Ghabrial, S. A., Castón, J. R., Jiang, D., Nibert, M. L. & Suzuki, N. 50-plus years of fungal viruses. Virology 479–480, 356–368 (2015).PubMed 
Article 
CAS 
PubMed Central 

Google Scholar 
35.Dunigan, D. D. et al. Chloroviruses lure hosts through long-distance chemical signaling. J. Virol. 93, e01688-18 (2019).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
36.Anantharaman, K. et al. Sulfur oxidation genes in diverse deep-sea viruses. Science 344, 757–760 (2014).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
37.Mann, N. H., Cook, A., Millard, A., Bailey, S. & Clokie, M. Bacterial photosynthesis genes in a virus. Nature 424, 741 (2003). Shows how the virus genome interacts with the host to facilitate virus reproduction.CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
38.Mavrich, T. N. & Hatfull, G. F. Evolution of superinfection immunity in cluster A mycobacteriophages. mBio 10, e00971-19 (2019).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
39.Marine, R. L., Nasko, D. J., Wray, J., Polson, S. W. & Wommack, K. E. Novel chaperonins are prevalent in the virioplankton and demonstrate links to viral biology and ecology. ISME J. 11, 2479–2491 (2017).PubMed 
PubMed Central 
Article 

Google Scholar 
40.ICTV. Virus Taxonomy: The ICTV Report on Virus Classification and Taxon Nomenclature. https://talk.ictvonline.org/ictv-reports/ictv_9th_report/ (2019).41.Ojosnegros, S. et al. Viral genome segmentation can result from a trade-off between genetic content and particle stability. PLoS Genet 7, e1001344 (2011).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
42.Belshaw, R., Pybus, O. G. & Rambaut, A. The evolution of genome compression and genomic novelty in RNA viruses. Genome Res. 17, 1496–1504 (2007).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
43.Van Etten, J. L., Agarkova, I. V. & Dunigan, D. D. Chloroviruses. Viruses 12, 20 (2020).Article 
CAS 

Google Scholar 
44.Iranzo, J. & Manrubia, S. C. Evolutionary dynamics of genome segmentation in multipartite viruses. Proc. Biol. Sci. 279, 3812–3819 (2012).PubMed 
PubMed Central 

Google Scholar 
45.Kellogg, C. A. & Paul, J. H. Degree of ultraviolet radiation damage and repair capabilities are related to G+C content in marine vibriophages. Aquat. Microb. Ecol. 27, 13–20 (2002).Article 

Google Scholar 
46.Violle, C. et al. Let the concept of trait be functional! Oikos 116, 882–892 (2007).Article 

Google Scholar 
47.Edwards, K. F., Steward, G. F. & Schvarcz, C. R. Making sense of virus size and the tradeoffs shaping viral fitness. Ecol. Lett. 24, 363–373 (2021).PubMed 
Article 
PubMed Central 

Google Scholar 
48.Bonachela, J. A. & Levin, S. A. Evolutionary comparison between viral lysis rate and latent period. J. Theor. Biol. 345, 32–42 (2014).PubMed 
Article 
PubMed Central 

Google Scholar 
49.Yashchenko, V. V., Gavrilova, O. V., Rautian, M. S. & Jakobsen, K. S. Association of Paramecium bursaria Chlorella viruses with Paramecium bursaria cells: ultrastructural studies. Eur. J. Protistol. 48, 149–159 (2012).PubMed 
Article 
PubMed Central 

Google Scholar 
50.DeLong, J. P., Al-Ameeli, Z., Duncan, G., Van Etten, J. L. & Dunigan, D. D. Predators catalyze an increase in chloroviruses by foraging on the symbiotic hosts of zoochlorellae. Proc. Natl Acad. Sci. USA 113, 13780–13784 (2016).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
51.Wang, I.-N. Lysis timing and bacteriophage fitness. Genetics 172, 17–26 (2006).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
52.Smith, C. & Fretwell, S. The optimal balance between size and number of offspring. Am. Nat. 108, 499–506 (1974).Article 

Google Scholar 
53.You, L., Suthers, P. F. & Yin, J. Effects of Escherichia coli physiology on growth of phage T7 In vivo and in silico. J. Bacteriol. 184, 1888–1894 (2002).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
54.Swan, B. K. et al. Prevalent genome streamlining and latitudinal divergence of planktonic bacteria in the surface ocean. Proc. Natl Acad. Sci. USA 110, 11463–11468 (2013).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
55.Hellweger, F. L. Carrying photosynthesis genes increases ecological fitness of cyanophage in silico. Environ. Microbiol. 11, 1386–1394 (2009).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
56.Schenk, H. & Sieber, M. Bacteriophage can promote the emergence of physiologically sub-optimal host phenotypes. bioRxiv https://doi.org/10.1101/621524 (2019).Article 

Google Scholar 
57.Howard-Varona, C. et al. Multiple mechanisms drive phage infection efficiency in nearly identical hosts. ISME J. 12, 1605–1618 (2018).PubMed 
PubMed Central 
Article 

Google Scholar 
58.Zimmerman, A. E. et al. Metabolic and biogeochemical consequences of viral infection in aquatic ecosystems. Nat. Rev. Microbiol. 18, 21–34 (2020).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
59.Grove, J. & Marsh, M. The cell biology of receptor-mediated virus entry. J. Cell Biol. 195, 1071–1082 (2011).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
60.McFadden, G., Mohamed, M. R., Rahman, M. M. & Bartee, E. Cytokine determinants of viral tropism. Nat. Rev. Immunol. 9, 645–655 (2009).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
61.Bernheim, A. & Sorek, R. The pan-immune system of bacteria: antiviral defence as a community resource. Nat. Rev. Microbiol. 18, 113–119 (2020).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
62.Nussenzweig, P. M. & Marraffini, L. A. Molecular mechanisms of CRISPR-Cas immunity in bacteria. Annu. Rev. Genet. 54, 93–120 (2020).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
63.Hampton, H. G., Watson, B. N. J. & Fineran, P. C. The arms race between bacteria and their phage foes. Nature 577, 327–336 (2020). An overview of the mechanisms and phenotypes related to phage infection and host defence mechanisms.CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
64.Samson, J. E., Magadán, A. H., Sabri, M. & Moineau, S. Revenge of the phages: defeating bacterial defences. Nat. Rev. Microbiol. 11, 675–687 (2013).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
65.Flores, C. O., Meyer, J. R., Valverde, S., Farr, L. & Weitz, J. S. Statistical structure of host–phage interactions. Proc. Natl Acad. Sci. USA 108, E288–E297 (2011). Demonstrates the role of virus host range in generating community-wide patterns of host–phage interactions.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
66.Regoes, R. R. & Bonhoeffer, S. The HIV coreceptor switch: a population dynamical perspective. Trends Microbiol. 13, 269–277 (2005).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
67.Atkinson, D., Ciotti, B. J. & Montagnes, D. J. Protists decrease in size linearly with temperature: ca. 2.5% C-1. Proc. R. Soc. Lond. B 270, 2605–2611 (2003).Article 

Google Scholar 
68.Falkowski, P. G. in Primary Productivity in the Sea (ed. Falkowski, P. G.) 99–119 (Springer, 1980).69.Salsbery, M. E. & DeLong, J. P. The benefit of algae endosymbionts in Paramecium bursariais temperature dependent. Evol. Ecol. Res. 19, 669–678 (2018).
Google Scholar 
70.Kimmance, S. A., Atkinson, D. & Montagnes, D. J. S. Do temperature–food interactions matter? Responses of production and its components in the model heterotrophic flagellate Oxyrrhis marina. Aquat. Microb. Ecol. 42, 63–73 (2006).Article 

Google Scholar 
71.Maat, D. S., van Bleijswijk, J. D. L., Witte, H. J. & Brussaard, C. P. D. Virus production in phosphorus-limited Micromonas pusilla stimulated by a supply of naturally low concentrations of different phosphorus sources, far into the lytic cycle. FEMS Microbiol. Ecol. 92, fiw136 (2016).PubMed 
Article 
CAS 
PubMed Central 

Google Scholar 
72.Amla, D. V., Rowell, P. & Stewart, W. D. P. Metabolic changes associated with cyanophage N-1 infection of the cyanobacterium Nostoc muscorum. Arch. Microbiol. 148, 321–327 (1987).CAS 
Article 

Google Scholar 
73.Hadas, H., Einav, M., Fishov, I. & Zaritsky, A. Bacteriophage T4 development depends on the physiology of its host Escherichia coli. Microbiology 143, 179–185 (1997).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
74.Demory, D. et al. Temperature is a key factor in Micromonas–virus interactions. ISME J. 11, 601–612 (2017). Shows the effect of temperature on the kinetics, phenotypes and life history strategies of prasinoviruses.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
75.Schachtele, C. F., Oman, R. W. & Anderson, D. L. Effect of elevated temperature on deoxyribonucleic acid synthesis in bacteriophage φ29-infected Bacillus amyloliquefaciens. J. Virol. 6, 430–437 (1970).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
76.Choua, M., Heath, M. R., Speirs, D. C. & Bonachela, J. A. The effect of viral plasticity on the persistence of host-virus systems. J. Theor. Biol. 498, 110263 (2020).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
77.Ni, T. & Zeng, Q. Diel infection of cyanobacteria by cyanophages. Front. Mar. Sci. https://doi.org/10.3389/fmars.2015.00123 (2016).Article 

Google Scholar 
78.Sakowski, E. G. et al. Ribonucleotide reductases reveal novel viral diversity and predict biological and ecological features of unknown marine viruses. Proc. Natl Acad. Sci. USA 111, 15786–15791 (2014). Demonstrates that genomic features in the viral replicon (that is, module of genes responsible for viral genome replication) may predict the biogeographical distribution of viruses.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
79.Reeson, A. F. et al. Effects of phenotypic plasticity on pathogen transmission in the field in a Lepidoptera-NPV system. Oecologia 124, 373–380 (2000).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
80.Stearns, S. C. The evolutionary significance of phenotypic plasticity. BioScience 39, 436–445 (1989).Article 

Google Scholar 
81.Leggett, H. C., Benmayor, R., Hodgson, D. J. & Buckling, A. Experimental evolution of adaptive phenotypic plasticity in a parasite. Curr. Biol. 23, 139–142 (2013).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
82.Oppenheim, A. B., Kobiler, O., Stavans, J., Court, D. L. & Adhya, S. Switches in bacteriophage lambda development. Annu. Rev. Genet. 39, 409–429 (2005).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
83.Erez, Z. et al. Communication between viruses guides lysis–lysogeny decisions. Nature 541, 488–493 (2017). Demonstrates the use of communication peptides that determine lysogeny in temperate phages.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
84.Weitz, J. S., Li, G., Gulbudak, H., Cortez, M. H. & Whitaker, R. J. Viral invasion fitness across a continuum from lysis to latency. Virus Evol. 5, vez006 (2019).PubMed 
PubMed Central 
Article 

Google Scholar 
85.Labonté, J. M. et al. Single cell genomics indicates horizontal gene transfer and viral infections in a deep subsurface Firmicutes population. Front. Microbiol. 6, 349 (2015).PubMed 
PubMed Central 

Google Scholar 
86.Koskella, B. & Brockhurst, M. A. Bacteria–phage coevolution as a driver of ecological and evolutionary processes in microbial communities. FEMS Microbiol. Rev. 38, 916–931 (2014).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
87.Meyer, J. R. et al. Repeatability and contingency in the evolution of a key innovation in phage lambda. Science 335, 428–432 (2012).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
88.Marston, M. F. et al. Rapid diversification of coevolving marine Synechococcus and a virus. Proc. Natl Acad. Sci. USA 109, 4544–4549 (2012). Demonstrates the rapid co-evolution of virus and host but highlights the challenge of identifying the critical phenotypes mediating the interaction.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
89.Frickel, J., Feulner, P. G. D., Karakoc, E. & Becks, L. Population size changes and selection drive patterns of parallel evolution in a host–virus system. Nat. Commun. 9, 1706 (2018).PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
90.Knowles, B. et al. Temperate infection in a virus–host system previously known for virulent dynamics. Nat. Commun. 11, 4626 (2020).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
91.Wang, I.-N., Dykhuizen, D. E. & Slobodkin, L. B. The evolution of phage lysis timing. Evol. Ecol. 10, 545–558 (1996).Article 

Google Scholar 
92.Abedon, S. T., Hyman, P. & Thomas, C. Experimental examination of bacteriophage latent-period evolution as a response to bacterial availability. Appl. Environ. Microbiol. 69, 7499–7506 (2003).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
93.Palkovacs, E. P. & Hendry, A. P. Eco-evolutionary dynamics: intertwining ecological and evolutionary processes in contemporary time. F1000 Biol. Rep. 2, 1 (2010).PubMed 
PubMed Central 
Article 

Google Scholar 
94.Brown, C. M., Lawrence, J. E. & Campbell, D. A. Are phytoplankton population density maxima predictable through analysis of host and viral genomic DNA content? J. Mar. Biol. Assoc. UK 86, 491–498 (2006).CAS 
Article 

Google Scholar 
95.Wommack, K. E. & Colwell, R. R. Virioplankton: viruses in aquatic ecosystems. Microbiol. Mol. Biol. Rev. 64, 69–114 (2000).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
96.Weitz, J. S. et al. A multitrophic model to quantify the effects of marine viruses on microbial food webs and ecosystem processes. ISME J. 9, 1352–1364 (2015).PubMed 
PubMed Central 
Article 

Google Scholar 
97.Poorvin, L., Rinta-Kanto, J. M., Hutchins, D. A. & Wilhelm, S. W. Viral release of iron and its bioavailability to marine plankton. Limnol. Oceanogr. 49, 1734–1741 (2004).CAS 
Article 

Google Scholar 
98.Shelford, E. J., Middelboe, M., Møller, E. F. & Suttle, C. A. Virus-driven nitrogen cycling enhances phytoplankton growth. Aquat. Microb. Ecol. 66, 41–46 (2012).Article 

Google Scholar 
99.Ankrah, N. Y. D. et al. Phage infection of an environmentally relevant marine bacterium alters host metabolism and lysate composition. ISME J. 8, 1089–1100 (2014).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
100.Jover, L. F., Effler, T. C., Buchan, A., Wilhelm, S. W. & Weitz, J. S. The elemental composition of virus particles: implications for marine biogeochemical cycles. Nat. Rev. Microbiol. 12, 519–528 (2014).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
101.Dawkins, R. The Extended Phenotype: The Long Reach of the Gene (Oxford University Press, 1999).102.Dawkins, R. Extended phenotype–but not too extended. A reply to Laland, Turner and Jablonka. Biol. Philosophy 19, 377–396 (2004).Article 

Google Scholar 
103.Ogata, H. Habitat alterations by viruses: strategies by Tupanviruses and others. Microbes Environ. 33, 117–119 (2018).PubMed 
PubMed Central 
Article 

Google Scholar 
104.Abrahão, J. et al. Tailed giant Tupanvirus possesses the most complete translational apparatus of the known virosphere. Nat. Commun. 9, 749 (2018).PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
105.Clark, H. F. & Wiktor, T. J. Plasticity of phenotypic characters of rabies-related viroses: spontaneous variation in the plaque morphology, virulence, and temperature-sensitivity characters of serially propagated Lagos bat and Mokola viruses. J. Infect. Dis. 130, 608–618 (1974).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
106.Abedon, S. T. & Culler, R. R. Optimizing bacteriophage plaque fecundity. J. Theor. Biol. 249, 582–592 (2007).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
107.Luo, E., Eppley, J. M., Romano, A. E., Mende, D. R. & DeLong, E. F. Double-stranded DNA virioplankton dynamics and reproductive strategies in the oligotrophic open ocean water column. ISME J. 14, 1304–1315 (2020).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
108.Bidle, K. D. Elucidating marine virus ecology through a unified heartbeat. Proc. Natl Acad. Sci. USA 111, 15606–15607 (2014).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
109.Schmidt, H. F., Sakowski, E. G., Williamson, S. J., Polson, S. W. & Wommack, K. E. Shotgun metagenomics indicates novel family A DNA polymerases predominate within marine virioplankton. ISME J. 8, 103–114 (2014).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
110.Nasko, D. J. et al. Family A DNA polymerase phylogeny uncovers diversity and replication gene organization in the virioplankton. Front. Microbiol. 9, 3053 (2018).PubMed 
PubMed Central 
Article 

Google Scholar 
111.Harrison, A. O., Moore, R. M., Polson, S. W. & Wommack, K. E. Reannotation of the ribonucleotide reductase in a cyanophage reveals life history strategies within the virioplankton. Front. Microbiol. 10, 134 (2019).PubMed 
PubMed Central 
Article 

Google Scholar 
112.Breitbart, M. Marine viruses: truth or dare. Annu. Rev. Mar. Sci. 4, 425–448 (2012).Article 

Google Scholar 
113.Hurwitz, B. L. & U’Ren, J. M. Viral metabolic reprogramming in marine ecosystems. Curr. Opin. Microbiol. 31, 161–168 (2016).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
114.Lindell, D., Jaffe, J. D., Johnson, Z. I., Church, G. M. & Chisholm, S. W. Photosynthesis genes in marine viruses yield proteins during host infection. Nature 438, 86–89 (2005).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
115.Rusconi, R., Garren, M. & Stocker, R. Microfluidics expanding the frontiers of microbial ecology. Annu. Rev. Biophys. 43, 65–91 (2014).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
116.Walker, G. M., Ozers, M. S. & Beebe, D. J. Cell infection within a microfluidic device using virus gradients. Sens. Actuators B Chem. 98, 347–355 (2004).CAS 
Article 

Google Scholar 
117.Cimetta, E. et al. Microfluidic-driven viral infection on cell cultures: theoretical and experimental study. Biomicrofluidics 6, 024127 (2012).PubMed Central 
Article 
CAS 

Google Scholar 
118.Xu, N. et al. A microfluidic platform for real-time and in situ monitoring of virus infection process. Biomicrofluidics 6, 034122 (2012).PubMed Central 
Article 
CAS 

Google Scholar 
119.Akin, D., Li, H. & Bashir, R. Real-time virus trapping and fluorescent imaging in microfluidic devices. Nano Lett. 4, 257–259 (2004).CAS 
Article 

Google Scholar 
120.Yu, J. Q. et al. Droplet optofluidic imaging for λ-bacteriophage detection via co-culture with host cell Escherichia coli. Lab. Chip 14, 3519–3524 (2014).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
121.Mashaghi, S. & van Oijen, A. M. Droplet microfluidics for kinetic studies of viral fusion. Biomicrofluidics 10, 024102 (2016).PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
122.Fischer, A. E. et al. A high-throughput drop microfluidic system for virus culture and analysis. J. Virol. Methods 213, 111–117 (2015).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar  More