Ecology

1.Nyakeri, E. M., Mwirichia, R. & Boga, H. Isolation and characterization of enzyme-producing bacteria from Lake Magadi, an extreme soda lake in Kenya. J. Microbiol. Exp. 6(2), 57–68 (2018).
Google Scholar 
2.Grant, W. D. Alkaline environments and biodiversity. In Extremophiles (eds Gerday, E. C. & Glansdorff, N.) (UNESCO, Eolss Publishers, 2006).
Google Scholar 
3.Jones, B. E. & Grant, W. D. Microbial diversity and ecology of alkaline environments. In Adaptation to Exotic Environments (ed. Seckbach, J.) 177–190 (Kluwer Academic Publishers, 2000).
Google Scholar 
4.Antony, C. P. et al. Microbiology of Lonar Lake and other soda lakes. J. Int. Soc. Microb. Ecol. 7(3), 468–476 (2013).
Google Scholar 
5.Boros, E. & Kolpakova, M. A review of the defining chemical properties of soda lakes and pans: An assessment on a large geographic scale of Eurasian inland saline surface waters. PLoS ONE 13(8), e0202205 (2018).PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
6.Grant, W. D. & Jones, B. E. Bacteria, archaea and viruses of soda lakes. In Soda lakes of East Africa (ed. Schagerl, M.) 97–148 (Springer p, 2016).
Google Scholar 
7.Lanzén, A. et al. Surprising prokaryotic and eukaryotic diversity, community structure and biogeography of Ethiopian soda lakes. PLoS ONE 8(8), e72577 (2013).ADS 
PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
8.Asao, M., Pinkart, H. C. & Madigan, M. T. Diversity of extremophilic purple phototrophic bacteria in Soap Lake, a Central Washington (USA) Soda Lake. Environ. Microbiol. 13(8), 2146–2157 (2011).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
9.Kulp, T. R. et al. Dissimilatory arsenate and sulfate reduction in sediments of two hypersaline, arsenic-rich soda lakes: Mono and Searles Lakes, California. Appl. Environ. Microbiol. 72(10), 6514–6526 (2006).ADS 
CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
10.Sorokin, D. Y. & Kuenen, J. G. Chemolithotrophic haloalkaliphiles from soda lakes. FEMS Microbiol. Ecol. 52(3), 287–295 (2005).CAS 
PubMed 
Article 

Google Scholar 
11.Groth, I. et al. Bogoriella caseilytica gen. nov., sp. Nov., a new alkaliphilic actinomycete from a soda lake in Africa. Int. J. Syst. Evol. Microbiol. 47(3), 788–794 (1997).CAS 

Google Scholar 
12.Glombitza, C. et al. Sulfate reduction controlled by organic matter availability in deep sediment cores from the saline, alkaline Lake Van (Eastern Anatolia, Turkey). Front. Microbiol. 4, 1–11 (2013).Article 

Google Scholar 
13.Bilgili, A. et al. Van Gölü’nden avlanan inci kefali örneklerinde arsenik düzeyleri. Turk. J. Vet. Anim. Sci. 23(2), 367–371 (1999).MathSciNet 

Google Scholar 
14.Kremer, B., Kaźmierczak, J. & Kempe, S. Authigenic replacement of cyanobacterially precipitated calcium carbonate by aluminium-silicates in giant microbialites of Lake Van (Turkey). Sedimentology 66(1), 285–304 (2019).CAS 
Article 

Google Scholar 
15.Reimer, A., Landmann, G. & Kempe, S. Lake Van, Eastern Anatolia, hydrochemistry and history. Aquat. Geochem. 15(1–2), 195–222 (2009).CAS 
Article 

Google Scholar 
16.Tomonaga, Y. et al. Porewater salinity reveals past lake-level changes in Lake Van, the Earth’s largest soda lake. Sci. Rep. 7(1), 1–10 (2017).CAS 
Article 

Google Scholar 
17.Pecoraino, G., Dlessandro, W. & Inguaggiato, S. The other side of the coin: Geochemistry of alkaline lakes in volcanic areas. In Volcanic Lakes (eds Rouwet, D. et al.) 219–237 (Springer, 2015).Chapter 

Google Scholar 
18.Kaden, H. et al. Impact of lake level change on deep-water renewal and oxic conditions in deep saline Lake Van. Turkey. Water Resour. Res. https://doi.org/10.1029/2009WR008555 (2010).ADS 
Article 

Google Scholar 
19.Landmann, G. & Kempe, S. Annual deposition signal versus lake dynamics: Microprobe analysis of Lake Van (Turkey) sediments reveals missing varves in the period 11.2–10.2 ka BP. Facies 51(1–4), 135–145 (2005).Article 

Google Scholar 
20.Degens, E. T. et al. A geological study of Lake Van, eastern Turkey. Geol. Rundsch. 73(2), 701–734 (1984).ADS 
CAS 
Article 

Google Scholar 
21.Duckworth, A. W. et al. Phylogenetic diversity of soda lake alkaliphiles. FEMS Microbiol. Ecol. 19(3), 181–191 (1996).CAS 
Article 

Google Scholar 
22.Sorokin, D. Y. et al. Microbial diversity and biogeochemical cycling in soda lakes. Extremophiles 18(5), 791–809 (2014).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
23.Zargar, K. et al. Identification of a novel oxidase gene, arxA, in the haloalkaliphilic, arsenite-oxidizing bacterium Alkalilimnicola echrlichii strain MLHE-1. J. Bacteriol. 192, 3755–3762 (2010).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
24.Zargar, K. et al. ArxA, a new clade of arsenite oxidase within the DMSO reductase family of molybdenum oxidoreductases. Environ. Microbiol. 14(7), 1635–1645 (2012).CAS 
PubMed 
Article 

Google Scholar 
25.Zorz, J. K. et al. A shared core microbiome in soda lakes separated by large distances. Nat. Commun. 10(1), 1–10 (2019).CAS 
Article 

Google Scholar 
26.Matyugina, E. & Belkova, N. Distribution and diversity of microbial communities in meromictic soda Lake Doroninskoe (Transbaikalia, Russia) during winter. Chin. J. Oceanol. Limn. 33(6), 1378 (2015).ADS 
CAS 
Article 

Google Scholar 
27.Liu, D. et al. Use of PCR primers derived from a putative transcriptional regulator gene for species-specific determination of Listeria monocytogenes. Int. J. Food Microbiol. 91, 297–304 (2004).CAS 
PubMed 
Article 

Google Scholar 
28.Camacho, C. et al. BLAST+: Architecture and applications. BMC Bioinform. 10, 421 (2009).Article 
CAS 

Google Scholar 
29.Ionescu, D. et al. Microbial and chemical characterization of underwater fresh water springs in the Dead Sea. PLoS ONE 7, e38319 (2012).ADS 
CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
30.Klindworth, A. et al. Evaluation of general 16S ribosomal RNA gene PCR primers for classical and next-generation sequencing-based diversity studies. Nucleic Acids Res. 41, e1 (2013).CAS 
PubMed 
Article 

Google Scholar 
31.Ondov, B. et al. Interactive metagenomic visualization in a web browser. BMC Bioinform. 12, 385 (2011).Article 

Google Scholar 
32.Pruesse, E. et al. SINA: Accurate high throughput multiple sequence alignment of ribosomal RNA genes. Bioinformatics 28(14), 1823–1829 (2012).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
33.Quast, C. et al. The silva ribosomal RNA gene database project: Improved data processing and webbased tools. Nucleic Acids Res. 41, D590–D596 (2013).CAS 
PubMed 
Article 

Google Scholar 
34.Rognes, T. et al. Vsearch: A versatile open source tool for metagenomics. Peer J. 4, e2584 (2016).PubMed 
PubMed Central 
Article 

Google Scholar 
35.Hammer, Ø., Harper, D. A. & Ryan, P. D. Past: Paleontological statistics software package for education and data analysis. Palaeontol. Electron. 4(1), 1–9 (2001).
Google Scholar 
36.Duckworth, A. W. et al. Halomonas magadii sp. Nov., a new member of the genus Halomonas, isolated from a soda lake of the East African Rift Valley. Extremophiles 4(1), 53–60 (2000).MathSciNet 
CAS 
PubMed 
PubMed Central 

Google Scholar 
37.Xin, H. et al. Natronobacterium nitratireducens sp. nov., a aloalkaliphilic archaeon isolated from a soda lake in China. Int. J. Syst. Evol. Microbiol. 51(5), 1825–1829 (2001).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
38.Joshi, A. et al. Nitrincola tapanii sp. nov., a novel alkaliphilic bacterium from An Indian Soda Lake. Int. J. Syst. Evol. Microbiol. 70(2), 1106–1111 (2020).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
39.Menes, R. J. et al. Bacillus natronophilus sp. nov., an alkaliphilic bacterium isolated from a soda lake. Int. J. Syst. Evol. Microbiol. 70(1), 562–568 (2020).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
40.Vavourakis, C. D. et al. A metagenomics roadmap to the uncultured genome diversity in hypersaline soda lake sediments. Microbiome. 6(1), 1–18 (2018).Article 

Google Scholar 
41.Yigit, A. et al. Determination of water quality by ion characterization of Van Lake Water. Iğdır Univ. J. Inst. Sci. Tech. 7(4), 169–179. https://doi.org/10.21597/jist.2017.210 (2017).Article 

Google Scholar 
42.Bilgili, A. et al. The natural quality of Van Lake and the levels of some heavy metals in grey mullet (Chalcalburus tariehi, Pallas 1811) samples taken from this lake. Ankara Üniv Vet Fak Dergisi 42, 445–450 (1995).
Google Scholar 
43.Demir Yetis, A. & Ozguven, A. Investigation of heavy metal pollution in surface waters of the Van Lake Edremit coast. Uludağ Univ. J. Fac. Eng. 25(2), 831–847. https://doi.org/10.17482/uumfd.752460 (2020).Article 

Google Scholar 
44.Ersoy Omeroglu, E. & Karaboz, I. Characterization and arsenic-tolerance potential of Halomonas sp. from Van Lake, Turkey. VI Congress of Macedonian Microbiologists With International Participation, 30 May–6 June, Abstract Book, pp. 200–201 (2018).45.Ersoy Omeroglu, E. Evaluation of arsenic pollution and the effect of arsenic on Branchybacterium paraconglomeratum in Van Lake. 1st World Conference On Sustaninable Life Sciences WOCOLS 2019, 30 June–7 July, Abstract Book, p. 17 (2019).46.Reimer, A. Hydrochemie und Geochemie der Sedimente und Porenwa¨sser des hochalkalinen Van Sees in der Osttu¨rkei. Dissertation, Facult Geosci Univ Hamburg, 136 pp, unpublished, (1995).47.Kempe, S. et al. Largest known microbialites discovered in Lake Van, Turkey. Nature 349, 605–608 (1991).ADS 
Article 

Google Scholar 
48.Kazmierczak, J. & Kempe, S. Modern terrestrial analogues for the carbonate globules in Martian meteorite ALH84001. Naturwissenschaften 90, 167–172 (2003).ADS 
CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
49.Lopez-Garcia, P. et al. Bacterial diversity and carbonate precipitation in the microbialites of the highly alkaline Lake Van, Turkey. Extremophiles 9, 263–274 (2005).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
50.Poyraz, N. & Mutlu, M. B. Characterization of microbial populations of Lake Van by 16S metagenomics study. ESTUJST-A. 9(1), 80–88 (2020).
Google Scholar 
51.Poyraz, N. & Mutlu, B. M. Alkaliphilic bacterial diversity of Lake Van/Turkey. Biological Biodivers. Conserv. 10(1), 92–103 (2017).
Google Scholar 
52.Sen, F. et al. Endemic fish species of Van Lake basin. YYU J. Agr. Sci. 28, 63–70 (2018).
Google Scholar 
53.Danulat, E. & Kempe, S. Nitrogenous waste excretion at extremely alkaline pH: The story of Chalcalburnus tarichi (Cyprinidae), endemic to Lake Van, Eastern Turkey. Fish Physiol. Biochem. 9, 377–386 (1992).CAS 
PubMed 
Article 

Google Scholar 
54.Bostanci, D. & Polat, N. Age and growth of Alburnus tarichi (Güldenstädt, 1814): An endemic fish species of Lake Van (Turkey). J. Appl. Ichthyol. 27, 1346–1349 (2011).Article 

Google Scholar 
55.Burger, J. et al. Armenian Gull (Larus armenicus). Handbook of the Birds of the World Alive, Lynx Edicions, Barcelona (2015).56.Oremland, R. S. et al. Methanogenesis in Big Soda Lake, Nevada: An alkaline, moderately hypersaline desert lake. Appl. Environ. Microbiol. 43, 462–468 (1982).ADS 
CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
57.Iversen, N. et al. Big Soda Lake (Nevada): 3: Pelagic methanogenesis and anaerobic methane oxidation. Limnol. Oceanogr. 32, 804–814 (1987).ADS 
CAS 
Article 

Google Scholar 
58.Oremland, R. S. et al. The microbial arsenic cycle in Mono Lake, California. FEMS Microb. Ecol. 48, 15–27 (2004).CAS 
Article 

Google Scholar 
59.Sorokin, D. Y. et al. Microbial thiocyanate utilization under highly alkaline conditions. Appl. Environ. Microbiol. 67, 528–538 (2001).ADS 
CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
60.Sorokin, D. Y. et al. Thioalkalimicrobium aerophilum gen. nov., sp. nov. and Thioalkalimicrobium sibiricum sp. nov., and Thioalkalivibrio versutus gen. nov., sp. nov., Thioalkalivibrio nitratis sp. nov. and Thioalkalivibrio denitrificans sp. nov., novel obligately alkaliphilic and obligately chemolithoautotrophic sulfur-oxidizing bacteria from soda lakes. Int. J. Syst. Evol. Microbiol. 51, 565–580 (2001).CAS 
PubMed 
Article 

Google Scholar 
61.Sorokin, D. Y. et al. Thioalkalivibrio thiocyanooxidans sp. nov. and Thioalkalivibrio paradoxus sp. nov., novel alkaliphilic, obligately autotrophic, sulfur-oxidizing bacteria from the soda lakes able to grow with thiocyanate. Int. J. Syst. Evol. Microbiol. 52, 657–664 (2002).CAS 
PubMed 
Article 

Google Scholar 
62.Gorlenko, V. M. et al. Ectothiorhodospira variabilis sp. nov., an alkaliphilic and halophilic purple sulfur bacterium from soda lakes. Int. J. Syst. Evol. Microbiol. 69, 558–564 (2009).
Google Scholar 
63.Mwirichia, R. et al. Bacterial diversity in the haloalkaline Lake Elmenteita, Kenya. Curr. Microbiol. 62, 209–221 (2011).CAS 
PubMed 
Article 

Google Scholar 
64.Mesbah, N. M. et al. Novel and unexpected prokaryotic diversity in water and sediments of the alkaline, hypersaline lakes of the Wadi an Natrun, Egypt. Microbial Ecol. 54, 598–616 (2007).CAS 
Article 

Google Scholar 
65.Flandroy, L. et al. The impact of human activities and lifestyles on the interlinked microbiota and health of humans and of ecosystems. Sci. Total Environ. 627, 1018–1038 (2018).ADS 
CAS 
PubMed 
Article 

Google Scholar 
66.Deshmukh, K. B. et al. Bacterial diversity of Lonar soda lake of India. Indian J. Microbiol. 51, 107–111 (2011).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
67.Zhao, D. et al. Abundant taxa and favorable pathways in the microbiome of soda-saline lakes in Inner Mongolia. Front. Microbiol. 11, 1740 (2020).PubMed 
PubMed Central 
Article 

Google Scholar 
68.Lavrentyeva, E. V. et al. Prokaryotic diversity in the biotopes of the Gudzhirganskoe saline lake (Barguzin Valley, Russia). Mikrobiologiya 89, 359–368 (2020).CAS 

Google Scholar 
69.Glaring, M. A. et al. Microbial diversity in a permanently cold and alkaline environment in Greenland. PLoS ONE 10, e0124863 (2015).PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
70.Tavormina, P. L. et al. Methyloprofundus sedimenti gen. nov., sp. nov., an obligate methanotroph from ocean sediment belonging to the ‘deep sea-1’clade of marine methanotrophs. Int. J. Syst. Evol. Microbiol. 65(1), 251–259 (2015).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
71.Sorokin, D. Y. et al. Dethiobacter alkaliphilus gen. nov. sp. nov., and Desulfurivibrio alkaliphilus gen. nov. sp. nov.: Two novel representatives of reductive sulfur cycle from soda lakes. Extremophiles 12, 431–439 (2008).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
72.Holmes, D. E. et al. Potential role of a novel psychrotolerant member of the family Geobacteraceae, Geopsychrobacter electrodiphilus gen. nov., sp. nov., in electricity production by a marine sediment fuel cell. Appl. Environ. Microbiol. 70, 6023–6030 (2005).ADS 
Article 
CAS 

Google Scholar 
73.Pikuta, E. V. et al. Proteocatella sphenisci gen. nov., sp. nov., a psychrotolerant, spore-forming anaerobe isolated from penguin guano. Int. J. Syst. Evol. Microbiol. 59, 2302–2307 (2009).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
74.Stams, A. J. M. & Hansen, T. A. Fermentation of glutamate and other compounds by Acidaminobacter hydrogenoformans gen. nov. sp. nov., an obligate anaerobe isolated from black mud: Studies with pure cultures and mixed cultures with sulfate-reducing and methanogenic bacteria. Arch. Microbiol. 137, 329–337 (1984).CAS 
Article 

Google Scholar 
75.Finegold, S. M. et al. Anaerofustis stercorihominis gen. nov., sp. nov., from human feces. Anaerobe 10, 41–45 (2004).CAS 
PubMed 
Article 

Google Scholar 
76.Matthies, C. et al. Anaerovorax odorimutans gen. nov., sp. nov., a putrescine-fermenting, strictly anaerobic bacterium. Int. J. Syst. Evol. Microbiol. 50, 1591–1594 (2000).CAS 
PubMed 
Article 

Google Scholar 
77.Higashiguchi, D. T. et al. Pilibacter termitis gen. nov., sp. nov., a lactic acid bacterium from the hindgut of the Formosan subterranean termite (Coptotermes formosanus). Int. J. Syst. Evol. Microbiol. 56, 15–20 (2006).CAS 
PubMed 
Article 

Google Scholar 
78.Labrenz, M. et al. Roseibaca ekhonensis gen. nov., sp. nov., an alkalitolerant and aerobic bacteriochlorophyll a-producing alphaproteobacterium from hypersaline Ekho Lake. Int. J. Syst. Evol. Microbiol. 59, 1935–1940 (2009).CAS 
PubMed 
Article 

Google Scholar 
79.Sorokin, D. Y. et al. Nitriliruptor alkaliphilus gen. nov., sp. nov., a deep-lineage haloalkaliphilic actinobacterium from soda lakes capable of growth on aliphatic nitriles, and proposal of Nitriliruptoraceae fam. Nov. and Nitriliruptorales ord. nov. Int. J. Syst. Evol. Microbiol. 59, 248–253 (2009).CAS 
PubMed 
Article 

Google Scholar 
80.Shahinpei, A. et al. Salinispirillum marinum gen. nov., sp. nov., a haloalkaliphilic bacterium in the family “Saccharospirillaceae”. Int. J. Syst. Evol. Microbiol. 64, 3610–3615 (2014).PubMed 
Article 
CAS 
PubMed Central 

Google Scholar 
81.Munson, M. A. et al. Buchnera gen. nov. and Buchnera aphidicola sp. nov., a taxon consisting of the mycetocyte-associated, primary endosymbionts of aphids. Int. J. Syst. Bacteriol. 41, 566–568 (1991).Article 

Google Scholar  More

1.United Nations Environment Programme. Making Peace With Nature (Tech. Rep, United Nations Environment Programme, 2021).2.Newbold, T. et al. Global effects of land use on local terrestrial biodiversity. Nature 520, 45–50. https://doi.org/10.1038/nature14324 (2015).ADS 
CAS 
Article 
PubMed 

Google Scholar 
3.Urban, M. C. Accelerating extinction risk from climate change. Science. (80-. ) 348, 571–573. https://doi.org/10.1126/science.aaa4984 (2015).ADS 
CAS 
Article 

Google Scholar 
4.Rosenberg, K. V. et al. Decline of the North American avifauna. Science. (80-. ) 366, 120–124. https://doi.org/10.1126/science.aaw1313 (2019).ADS 
CAS 
Article 

Google Scholar 
5.Jetz, W., Wilcove, D. S. & Dobson, A. P. Projected impacts of climate and land-use change on the global diversity of birds. PLoS Biol. 5, 1211–1219. https://doi.org/10.1371/journal.pbio.0050157 (2007).CAS 
Article 

Google Scholar 
6.Abrahamczyk, S. & Renner, S. S. The temporal build-up of hummingbird/plant mutualisms in North America and temperate South America. BMC Evol. Biol.https://doi.org/10.1186/s12862-015-0388-z (2015).Article 
PubMed 
PubMed Central 

Google Scholar 
7.Grant, V. & Grant, K. A. A Hummingbird-Pollinated Species of Boraginaceae in the Arizona Flora. Proc. Natl. Acad. Sci. 66, 917–919. https://doi.org/10.1073/pnas.66.3.917 (1970).ADS 
CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
8.Ratto, F. et al. Global importance of vertebrate pollinators for plant reproductive success: A meta-analysis. Front. Ecol. Environ. 16, 82–90. https://doi.org/10.1002/fee.1763 (2018).Article 

Google Scholar 
9.McGuire, J. A. et al. Molecular phylogenetics and the diversification of hummingbirds. Curr. Biol. 24, 910–916. https://doi.org/10.1016/j.cub.2014.03.016 (2014).CAS 
Article 
PubMed 

Google Scholar 
10.Sauer, J. R., Link, W. A., Fallon, J. E., Pardieck, K. L. & Ziolkowski, D. J. The North American breeding bird survey 1966–2011: Summary analysis and species accounts. N. Am. Fauna 79, 1–32. https://doi.org/10.3996/nafa.79.0001 (2013).Article 

Google Scholar 
11.Bairlein, F. Migratory birds under threat. Science (80-. ). 354, 547–548. https://doi.org/10.1126/science.aah6647 (2016).ADS 
CAS 
Article 

Google Scholar 
12.Battey, C. J. Ecological release of the Anna’s Hummingbird during a Northern range expansion. Am. Nat. 194, 306–315. https://doi.org/10.1086/704249 (2019).CAS 
Article 
PubMed 

Google Scholar 
13.Clark, C. J. EBird records show substantial growth of the Allen’s Hummingbird (Selasphorus sasin sedentarius) population in urban Southern California. Condor 119, 122–130. https://doi.org/10.1650/CONDOR-16-153.1 (2017).Article 

Google Scholar 
14.Sleeter, B. M. et al. Land-cover change in the conterminous United States from 1973 to 2000. Glob. Environ. Change 23, 733–748. https://doi.org/10.1016/j.gloenvcha.2013.03.006 (2013).Article 

Google Scholar 
15.Gallant, A. L., Loveland, T. R., Sohl, T. L. & Napton, D. E. Using an ecoregion framework to analyze land-cover and land-use dynamics. Environ. Manag.https://doi.org/10.1007/s00267-003-0145-3 (2004).Article 

Google Scholar 
16.Williamson, S. L. A Field Guide to Hummingbirds of North America (Peterson Field Guide Series) (Houghton Mifflin Company, 2002).
Google Scholar 
17.Panjabi, A. O. et al. Avian Conservation Assessment Database Handbook Version 2021. Tech. Rep. (Partners in Flight Technical Series, Bird Conservancy of the Rockies, 2021).
Google Scholar 
18.Gillespie, C., Contreras-Martinez, S., Bishop, C. & Alexander, J. Rufous Hummingbird: State of the Science and Conservation : simplebooklet.com. Tech. Rep., (Western Hummingbird Partnership, 2020).19.International Union for Conservation of Nature. IUCN Red List Categories and Criteria: Version 3.1. Tech. Rep. (IUCN Species Survival Commission, 2001).
Google Scholar 
20.Lehikoinen, A. Climate change, phenology and species detectability in a monitoring scheme. Popul. Ecol. 55, 315–323. https://doi.org/10.1007/s10144-012-0359-9 (2013).Article 

Google Scholar 
21.Massimino, D., Harris, S. J. & Gillings, S. Phenological mismatch between breeding birds and their surveyors and implications for estimating population trends. J. Ornithol. 162, 143–154. https://doi.org/10.1007/s10336-020-01821-5 (2021).Article 

Google Scholar 
22.McGrath, L. J., van Riper III, C. & Fontaine, J. J. Flower power: Tree flowering phenology as a settlement cue for migrating birds. J. Anim. Ecol. 78, 22–30. https://doi.org/10.1111/j.1365-2656.2008.01464.x (2009).Article 
PubMed 

Google Scholar 
23.Jones, T. & Cresswell, W. The phenology mismatch hypothesis: Are declines of migrant birds linked to uneven global climate change?. J. Anim. Ecol. 79, 98–108. https://doi.org/10.1111/j.1365-2656.2009.01610.x (2010).Article 
PubMed 

Google Scholar 
24.Courter, J. R. Changes in spring arrival dates of rufous hummingbirds (Selasphorus rufus) In Western North America in the past century. Wilson J. Ornithol. 129, 535–544. https://doi.org/10.1676/16-133.1 (2017).Article 

Google Scholar 
25.Rooney, T. Deer impacts on forest ecosystems: A North American perspective. Forestry 74, 201–208. https://doi.org/10.1093/forestry/74.3.201 (2001).Article 

Google Scholar 
26.Côté, S. D., Rooney, T. P., Tremblay, J.-P., Dussault, C. & Waller, D. M. Ecological impacts of deer overabundance. Annu. Rev. Ecol. Evol. Syst. 35, 113–147. https://doi.org/10.2307/annurev.ecolsys.35.021103.30000006 (2004).Article 

Google Scholar 
27.Decalesta, D. S. Effect of white-tailed deer on songbirds within managed forests in Pennsylvania. J. Wildl. Manag. 58, 711–718 (1994).Article 

Google Scholar 
28.English, S. G. et al. Neonicotinoid pesticides exert metabolic effects on avian pollinators. Sci. Rep. 11, 2914. https://doi.org/10.1038/s41598-021-82470-3 (2021).ADS 
CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
29.Bishop, C. A. et al. Determination of neonicotinoids and butenolide residues in avian and insect pollinators and their ambient environment in Western Canada (2017, 2018). Sci. Total Environ. 737, 139386. https://doi.org/10.1016/j.scitotenv.2020.139386 (2020).ADS 
CAS 
Article 
PubMed 

Google Scholar 
30.Graves, E. E. et al. Analysis of insecticide exposure in California hummingbirds using liquid chromatography-mass spectrometry. Environ. Sci. Pollut. Res. 26, 15458–15466. https://doi.org/10.1007/s11356-019-04903-x (2019).CAS 
Article 

Google Scholar 
31.Hill, G. E., Sargent, R. R. & Sargent, M. B. Recent change in the winter distribution of Rufous Hummingbirds. Auk 115, 240–245. https://doi.org/10.2307/4089135 (1998).Article 

Google Scholar 
32.Smith, A. C. & Edwards, B. P. M. North American Breeding Bird Survey status and trend estimates to inform a wide range of conservation needs, using a flexible Bayesian hierarchical generalized additive model. Condor 123, 1–16. https://doi.org/10.1093/ornithapp/duaa065 (2021).Article 

Google Scholar 
33.Wilson, S. et al. Prioritize diversity or declining species? Trade-offs and synergies in spatial planning for the conservation of migratory birds in the face of land cover change. Biol. Conserv. 239, 108285. https://doi.org/10.1016/j.biocon.2019.108285 (2019).Article 

Google Scholar 
34.Toledo-Aceves, T., Meave, J. A., González-Espinosa, M. & Ramírez-Marcial, N. Tropical montane cloud forests: Current threats and opportunities for their conservation and sustainable management in Mexico. J. Environ. Manag. 92, 974–981. https://doi.org/10.1016/j.jenvman.2010.11.007 (2011).Article 

Google Scholar 
35.Hansen, M. C. et al. High-resolution global maps of 21st-century forest cover change. Science (80-. ). 342, 850–853. https://doi.org/10.1126/SCIENCE.1244693 (2013).ADS 
CAS 
Article 

Google Scholar 
36.Westerling, A. L. Increasing western US forest wildfire activity: Sensitivity to changes in the timing of spring. Philos. Trans. R. Soc. B Biol. Sci.https://doi.org/10.1098/RSTB.2015.0178 (2016).Article 

Google Scholar 
37.Neeraja, U. V., Rajendrakumar, S., Saneesh, C. S., Dyda, V. & Knight, T. M. Fire alters diversity, composition, and structure of dry tropical forests in the Eastern Ghats. Ecol. Evol. 11, 6593–6603. https://doi.org/10.1002/ECE3.7514 (2021).CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
38.Courter, J. R., Johnson, R. J., Bridges, W. C. & Hubbard, K. G. Assessing migration of Ruby-throated Hummingbirds (Archilochus colubris) at broad spatial and temporal scales at broad spatial and temporal scales. Auk 130, 107–117. https://doi.org/10.1525/auk.2012.12058 (2013).Article 

Google Scholar 
39.Greig, E. I., Wood, E. M. & Bonter, D. N. Winter range expansion of a hummingbird is associated with urbanization and supplementary feeding. Proc. R. Soc. B Biol. Sci.https://doi.org/10.1098/rspb.2017.0256 (2017).Article 

Google Scholar 
40.Jepson, W. L. & Hickman, J. C. The Jepson manual: Higher plants of California (University of California Press, 1993).
Google Scholar 
41.Scarfe, A. & Finlay, J. C. Rapid second nesting by Anna’s Hummingbird near its Northern breeding limit. West. Birds 32, 131–133 (2001).
Google Scholar 
42.Bibby, C. J., Burgess, N. D. & Hill, D. A. Bird Census Techniques (Academic Press, 1992).
Google Scholar 
43.Thogmartin, W. E. et al. A review of the population estimation approach of the North American landbird conservation plan. Auk 123, 892–904. https://doi.org/10.1093/auk/123.3.892 (2006).Article 

Google Scholar 
44.Carter, M. F., Hunter, W. C., Pashley, D. N. & Rosenberg, K. V. Setting conservation priorities for landbirds in the United States: The partners in flight approach. Auk 117, 541–548. https://doi.org/10.1093/auk/117.2.541 (2000).Article 

Google Scholar 
45.Sauer, J. R. & Link, W. A. Analysis of the North American breeding bird survey using hierarchical models. Auk 128, 87–98. https://doi.org/10.1525/auk.2010.09220 (2011).Article 

Google Scholar 
46.Sauer, J. R., Niven, D. K., Pardieck, K. L., Ziolkowski, D. J. & Link, W. A. Expanding the North American Breeding Bird Survey analysis to include additional species and regions. J. Fish Wildl. Manag. 8, 154–172. https://doi.org/10.3996/102015-JFWM-109 (2017).Article 

Google Scholar 
47.Stanton, J. C., Blancher, P., Rosenberg, K. V., Panjabi, A. O. & Thogmartin, W. E. Estimating uncertainty of North American landbird population sizes. Avian Conserv. Ecol.https://doi.org/10.5751/ACE-01331-140104 (2019).Article 

Google Scholar 
48.Schuster, R. et al. Optimizing the conservation of migratory species over their full annual cycle. Nat. Commun.https://doi.org/10.1038/s41467-019-09723-8 (2019).Article 
PubMed 
PubMed Central 

Google Scholar 
49.Johnston, A. et al. Abundance models improve spatial and temporal prioritization of conservation resources. Ecol. Appl. 25, 1749–1756. https://doi.org/10.1890/14-1826.1 (2015).Article 
PubMed 

Google Scholar 
50.Robbins, C., Bystrak, D. & Geissler, P. The Breeding Bird Survey: Its First Fifteen Years, 1965–1979. Tech. Rep. (U.S. Fish and Wildlife Service, 1986).51.R Core Team. R: A language and environment for statistical computing (Version 4.0.3) [Computer software] (2020).52.Smith, A. C., Hudson, M.-A., Aponte, V. & Francis, C. North American Breeding Bird Survey—Canadian Trends Website. Data-version 2017 (2019).53.Edwards, B. P. M. & Smith, A. C. bbsBayes: An R package for hierarchical Bayesian analysis of North American breeding bird survey data. J. Open Res. Softw.https://doi.org/10.5334/JORS.329 (2021).Article 

Google Scholar 
54.North American Bird Conservation Initiative. Bird Conservation Region Descriptions. Tech. Rep. (U. S. Fish and Wildlife Service, 2000).
Google Scholar  More

The participants of the on-farm trialsThe farmers taking part in the trials own between 0.3 and 40 ha. Most of them were smallholders (less than 2 ha) and used to plant vegetable fields of around 300 m2 per crop. Two out of 233 participating farmers are female, farmers’ age ranges from 24 to 68 years. All farmers learned agriculture from their parents, 70% are literate. Farmers and fields were visited 10–12 times per trial. In 2018, we started with 112 farmer fields, but some farmers did not follow strictly the obligatory agricultural practices (e.g., concerning fertilizer, irrigation, harvest), some lost the entire or parts of fields (e.g., by flood, grazing livestock), therefore all assessments concerning 2018 include 99 farmer fields. In 2019, we started with 136 farmer fields, two farmers did not follow the agreed farming practices, so assessments for 2019 are based on 134 farmer fields.The design of participatory field trialsWe conducted 14 trials in 2018 and 17 in 2019, each trial encompasses five FAP fields and three control fields in neighbouring villages. Minimum distance between FAP fields and between FAP and control fields was two thousand metres for nearly all fields, at least more than one thousand metres. In the mountainous region we used pumpkin, zucchini and faba bean as main crops (two years), in oasis okra and zucchini (two years), faba bean and pumpkin (2019), in the semi-arid region melon, zucchini, pumpkin, eggplant and faba bean (two years) and in the region with adequate rainfall tomato, faba bean, zucchini and eggplant (two years) and pumpkin (2019). The main crops were selected by farmers and agricultural advisors of the respective regions, MHEP by farmers of the respective trials and researchers.Field size was 300 m2 as recommended for smallholders5 with a 75% zone for the main crop in both, FAP and control. Except for okra, the 75% zone had four cultivars with four replications in a randomized system as recommended as enhanced practice by farmers in the pilot project in Morocco27. For okra only two cultivars are available in Morocco and trials used only seeds accessible also for farmers. FAP fields employed the 25% zones for habitat enhancement, whereas control fields had the main crop also in this zone. We used coriander, basil, cumin, dill, anise, celery, sunflower, canola, flax, zucchini, okra, melon, tomato, green pepper, cucumber, Armenian cucumber, eggplant, chia, arugula, watermelon, pumpkin, grass pea, cultivated lupinus, alfalfa, clover, vetch, faba bean and wild lupinus as MHEP, per trial between four and eight different MHEP. As faba bean starts flowering in end of February in Morocco, MHEP were partly forage crops as they flower early. MHEP were seeded in a way that around 2/3 flowered at the same time as the main crop and 1/3 before or after to prolong the foraging season on site for flower visitors. The habitat enhancement zones included also nesting and water support out of local materials, e.g., hollow stems, wood and dry mud with holes.Field managementIn oasis, all fields were irrigated by gravity flow, in the other sites all farmers used drip irrigation. The amount of dung used is based on farmers’ decision and varies per region: semi-arid region 500 kg/300 m2, mountainous region 1000 kg/300 m2, oasis 1500 kg/300 m2 and region with adequate rainfall 3000 kg/300 m2. Soil analysis was conducted for all fields but does not explain the income gaps between FAP and control. Pesticides (mainly neonicotinoids and broad-spectrum insecticides) were prohibited during trials. In some urgent cases with permission of the plant protection specialist, one foliar insecticide application for pest management was accepted when pest density reached the economic threshold.Insect sampling and methods to analyse the dataThe taxa richness of flower visitors was assessed by a combination of transect net samplings and pan trappings. In each field, insects were sampled four times, once before the flowering of the main crop, twice during its flowering and once afterwards. Each sampling took two days for each trial (four fields per day). Two sets of three pan traps (blue, yellow and white) were located in each field at the beginning of the first day of sampling and were collected the second day after 24 h. The samplings in 75% zones consisted of walking along two twenty eight metres transect lines for five min each. In the 25% zones flower visitors were collected once along an 80 m transect line around the 75% zone for ten minutes. The flower visitors were collected and kept separately per MHEP, but the respective time needed was recorded and added to the transect. The insects were collected using both sweep nets and insect vacuums. All flower visitors were collected except Apis mellifera, Bombus terrestris and Xylocopa pubescens that were identified visually on site. The collected insects were first fainted with ethyl acetate and afterwards placed inside killing jars filled with cyanide, afterwards pinned and labelled. Wild bees were identified to the genus level using the most recent key for wild bees in Europe52. The other flower visitors were identified to genus level or to family level. Significance concerning diversity was measured by Wilcoxon test53.In the 75% zones, pest insects, predators and parasitoid wasps were collected four times. Per farmer field, four one-square-metre quadrates were randomly selected, within the quadrates ten randomly selected plants were beaten five times, so in total we used 320 crop samples per trial. In the 25% zones, the beating method was similarly used for each MHEP (five sample plants per MHEP). Specimen were collected in plastic bags and kept in plastic tubes containing 70% ethanol for conservation. Abundance of pests was estimated by counting the number (i) recorded on each sample crop. Pest reduction was calculated by the rate of pest reduction (%) using the following formula: % = (1− AFAP(i) / AControl(i)) × 100, where AFAP (i) is the average of the abundance in the FAP plot; AControl (i) is the average of the abundance in the control plot54.Economic assessmentsThe economic assessments use the same calculation as the pilot projects5,27: the number of fruits was counted and weighed. Investment costs in FAP and control fields are the same in the 75% zones. The income from the 75% zones was assessed by multiplying total weight with market price per kg. The income from the 25% zones of control fields was assessed by total produce weight multiplied by market price per kg; investment costs were deducted. The income of the 25% zone of FAP fields was computed by multiplying total weight with market price per kg of MHEP minus respective investment costs and minus 100 MAD (1.5 person days per FAP field) as calculated labour costs for harvesting MHEP, though in our trials, farmers harvested themselves.SimulationsThe simulation of potential FAP impacts on food security and sparing natural land for pollinator and biodiversity protection is based on following assumptions. Basis is the total production (2016–2017 differentiated per crop; provided by the Moroccan Ministry of Agriculture on request) for faba bean (share of harvested crop with green pods as in the experiments, 105,760 ton in 10,205 ha), zucchini and pumpkin (179,519 ton in 7539 ha), melon (618,588 ton in 20,163 ha), eggplant (52,966 ton in 1885 ha) and tomato (1,293,761 ton in 15,888 ha). We did not include okra due to lack of national production data. For the simulation on potential increase of production through smallholders (≤ 2 ha), we use 13% as share of smallholders in North Africa for vegetable production49. For the simulation of the land-saving potential through smallholders, we used 11% (North Africa, share of smallholders’ land for food crops)55.The formula used for the simulation on the potential FAP impacts on food security (PIFS) is:$${text{PIFS}}, = ,left( {{text{SSP}}*left( {{{1}} – upmu } right)} right), + ,left( {{text{SSP }}*upmu } right){text{ }}*left( {{text{1}}, + ,left( {{text{GFT }}*{text{TE}}} right)} right) – {text{SSP}}$$PIFS: Potential increase in crop production because of FAP (t), SSP: Smallholders’ share of production in (t), GFT: FAP production gain in farm trials (%), µ: the share of smallholder-producers adopting FAP, TE: Technology effectiveness.The GFT employed is 85,2% which represents the average FAP production gain of the vegetables used in the simulation process. For µ we used either 10%, 30% or 50% and for TE we assumed that smallholder-producers gain either 50% or 70% of the total production gain achieved in on-farm trials with smallholder-farmers since farmers will adapt MHEP and their planting to their personal preferences.The formula used for the simulation of potential land saving (PLS):$${text{PLS}} = (({text{SAP}} * {text{PIFS}})/{text{SSP}})-{text{SAP}}$$PLS: Potential land saving in ha, SAP: Smallholders’ area of production in ha. More

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