Ecology

1.Lovejoy, T. E. & Hannah, L. Biodiversity and Climate Change: Transforming the Biosphere (Yale University Press, 2005).
Google Scholar 
2.Bellard, C., Berttelsmeier, C., Leadley, P., Thuiller, W. & Courchamp, F. Impacts of climate change on the future of biodiversity. Ecol. Lett. 15, 365–377 (2012).PubMed 
PubMed Central 
Article 

Google Scholar 
3.Hawkins, B. A. et al. Energy, water, and broad scale geographic patterns of species richness. Ecology 84, 3105–3117 (2003).Article 

Google Scholar 
4.Pearce, A. & Feng, M. Observation of warming on the western Australia continental shelf. Mar. Freshwater Res. 58, 914–920 (2007).Article 

Google Scholar 
5.Ridgway, K. R. Long-term trend and decadal variability of the southward penetration of the East Australian Current. Geophys. Res. Lett. 34, L13613 (2007).ADS 

Google Scholar 
6.Chen, L., Huang, J. G., Ma, Q. & Hanninen, H. Long-term changes in the impacts of global warming on leaf phenology of four temperature tree species. Glob. Change Biol. 25(3), 997–1004 (2018).ADS 
Article 

Google Scholar 
7.Harley, C. D. G. et al. The impacts of climate change in coastal marine systems. Ecol. Lett. 9, 228–241 (2006).ADS 
PubMed 
Article 

Google Scholar 
8.Poloczanska, E. S. et al. Climate change and Australian marine life. Oceanogr. Mar. Biol. 45, 407–478 (2007).
Google Scholar 
9.Maltby, K. M. et al. Projected impacts of warming seas on commercially fished species at a biogeographic boundary of the European continental shelf. J. Appl. Ecol. 57, 2222–2233 (2019).Article 

Google Scholar 
10.Melzner, F., Buchholz, B., Wolf, F., Panknin, U. & Wall, M. Ocean winter warming induced starvation of predator and prey. Proc. R. Soc. B 287, 20200970 (2020).PubMed 
PubMed Central 
Article 

Google Scholar 
11.He, H. et al. Turning up the heat: Warming influences plankton biomass and spring phenology in subtropical waters characterized by extensive fish omnivory. Oecologia 194, 251–265 (2020).ADS 
PubMed 
Article 

Google Scholar 
12.Pagès-Escolà, M. et al. Divergent responses to warming of two common co-occurring Mediterranean bryozoans. Sci. Rep. 8, 17455 (2018).ADS 
PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
13.Gómez-Gras, D. et al. Response diversity in Mediterranean coralligenous assemblages facing climate change: Insights from a multispecific thermotolerance experiment. Ecol. Evol. 9(7), 4168–4180 (2019).PubMed 
PubMed Central 
Article 

Google Scholar 
14.Huret, M., Bourriau, P., Doray, M., Gohin, F., Petitgas, P. Survey timing vs. ecosystem scheduling: Degree-days to underpin observed interannual variability in marine ecosystems. Progr. Oceanogr. 166, 30–40 (2018).15.Strelkov, P., Katolikova, M. & Väinolä, R. Temporal change of the Baltic sea-North Sea mussle hybrid zone over two decades. Mar. Biol. 164, 1–14 (2017).Article 

Google Scholar 
16.Chiba, S. et al. Temperature and zooplankton size structure: Climate control and basin-scale comparison in the North Pacific. Ecol. Evol. 5(4), 968–978 (2015).PubMed 
PubMed Central 
Article 

Google Scholar 
17.Wernberg, T. et al. Seaweed communities in retreat from ocean warming. Curr. Biol. 21, 1–5 (2011).Article 
CAS 

Google Scholar 
18.Block, S. E., Olesen, E. & Krause-Jensen, D. Life history events of eelgrass Zostera marina L. populations across gradients of latitude and temperature. Mar. Ecol. Progr. Ser. 590, 79–93 (2018).ADS 
Article 

Google Scholar 
19.Cure, K. et al. Spatiotemporal patterns of abundance and ecological requirements of a labrid’s juveniles reveal conditions for establishment success and range shift capacity. J. Exp. Mar. Biol. Ecol. 500, 34–45 (2018).Article 

Google Scholar 
20.Smale, D. A. et al. Environmental factors influencing primary productivity of the forest-forming kelp Laminaria hyperborea in the northeast Atlantic. Sci. Rep. 10, 12161 (2020).ADS 
CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
21.Ruiz, J. M. et al. Experimental evidence of warming-induced flowering in the Mediterranean seagrass Posidonia oceanica. Mar. Pollut. Bull. 134, 49–54 (2018).CAS 
PubMed 
Article 

Google Scholar 
22.Rasconi, S., Winter, K. & Kainz, M. J. Temperature increase and fluctuation induce phytoplankton biodiversity loss—Evidence from a multi-seasonal mesocosm experiment. Ecol. Evol. 7, 2936–2946 (2017).PubMed 
PubMed Central 
Article 

Google Scholar 
23.Smale, D. A., Wernberg, T., Yunnie, A. L. E. & Vance, T. The rise of Laminaria ochroleuca in the Western English Channel (UK) and preliminary comparisons with its competitor and assemblage dominant Laminaria hyperborea. Mar. Ecol. 36, 1033–1044 (2015).ADS 
Article 

Google Scholar 
24.Pansch, C. & Hibenthal, C. A new mesocosm system to study the effects of environmental variability on marine species and communities. Limnol. Oceanogr. Methods 17, 145–162 (2019).Article 

Google Scholar 
25.Doo, S. S. The challenges of detecting and attributing ocean acidification impacts on marine ecosystems. ICES J. Mar. Sci. 77, 2411–2422 (2020).Article 

Google Scholar 
26.Kim, J.-H. et al. Global warming offsets the ecophysiological stress of ocean acidification on temperate crustose coralline algae. Mar. Pollut. Bull. 157, 111324 (2020).CAS 
PubMed 
Article 

Google Scholar 
27.Bonaviri, C., Graham, M., Gianguzza, P. & Shears, N. T. Warmer temperatures reduce the influence of an important keystone predator. J. Anim. Ecol. 86, 490–500 (2017).PubMed 
Article 

Google Scholar 
28.Carr, L. A., Gittman, R. K. & Bruno, J. F. Temperature influences herbivory and algal biomass in the Galápagos Islands. Front. Mar. Sci. 5, 279 (2018).Article 

Google Scholar 
29.De Frenne, P. et al. Latitudinal gradients as natural laboratories to infer species’ responses to temperature. J. Ecol. 101, 784–795 (2013).Article 

Google Scholar 
30.Behrenfeld, M. J. Climate-mediated dance of the plankton. Nat. Clim. Change 4(10), 880–887 (2014).ADS 
Article 

Google Scholar 
31.Behrenfeld, M. J. et al. Climate-driven trends in contemporary ocean productivity. Nature 444(7120), 752–755 (2006).ADS 
CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
32.Bricaud, A., Morel, A., Babin, M., Allali, K. & Hervè, C. Variations of light absorption by suspended particles with chlorophyll a concentration in oceanic waters: Analysis and implications for bio-optical models. J. Geophys. Res. 103, 31033–31044 (1998).ADS 
Article 

Google Scholar 
33.Jaud, T., Dragon, A. C., Garcia, J. V. & Guinet, C. Relationship between chlorophyll a concentration, light attenuation and diving depth of the southern elephant seal Mirounga leonina. PLoS ONE 7(10), e47444 (2012).ADS 
CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
34.Dunstan, P. K. et al. Global patterns of change and variation in sea surface temperature and chlorophyll a. Sci. Rep. 8, 14624 (2018).ADS 
PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
35.Sanford, E. & Kelly, M. W. Local adaptation of marine invertebrates. Annu. Rev. Mar. Sci. 3, 509–535 (2011).ADS 
Article 

Google Scholar 
36.Oliver, T. A. & Palumbi, S. R. Do fluctuating temperature environments elevate coral thermal tolerance?. Coral Reefs 30, 429–440 (2011).ADS 
Article 

Google Scholar 
37.Baumann, H. & Conover, D. O. Adaptation to climate change: Contrasting patterns of thermal-reaction-norm evolution in Pacific versus Atlantic silversides. Proc. R. Soc. B 278(1716), 2265–2273 (2011).PubMed 
PubMed Central 
Article 

Google Scholar 
38.Castillo, K. D., Ries, J. B., Weiss, J. M. & Lima, F. P. Decline of forereef corals in response to recent warming linked to history of thermal exposure. Nat. Clim. Change 2(10), 756–760 (2012).Article 

Google Scholar 
39.Thomas, M. K., Kremer, C. T., Klausmeier, C. T. & Litchman, E. A global pattern of thermal adaptation in marine phytoplankton. Science 338, 6110 (2012).Article 
CAS 

Google Scholar 
40.Chefaoui, R. M., Duarte, C. M. & Serrao, E. A. Dramatic loss of seagrass habitat under projected climate change in the Mediterranean Sea. Glob. Change Biol. 24(10), 4919–4928 (2018).ADS 
Article 

Google Scholar 
41.Duarte, B. et al. Climate change impacts on seagrass meadows and macroalgal forests: an integrative perspective on acclimation and adaptation potential. Front. Mar. Sci. 5, 190 (2018).Article 

Google Scholar 
42.Hemminga, M. A. & Duarte, C. M. Seagrass Ecology (Cambridge University Press, 2000).Book 

Google Scholar 
43.Larkum, A. W. D., Orth, R. J. & Duarte, C. M. Seagrasses: Biology, Ecology and Conservation (Springer, 2006).
Google Scholar 
44.Fourqurean, J. W. et al. Seagrass ecosystems as a globally significant carbon stock. Nat. Geosci. 5(7), 505–509 (2012).ADS 
CAS 
Article 

Google Scholar 
45.Fonseca, M. S. & Cahalan, J. A. A preliminary evaluation of wave attenuation by four species of seagrass. Estuar. Coast. Shelf Sci. 35, 565–576 (1992).ADS 
Article 

Google Scholar 
46.Fonseca, M. S. & Koehl, M. A. R. Flow in Seagrass canopies: the influence of patch width. Estuar. Coast. Shelf Sci. 67, 1–9 (2006).ADS 
Article 

Google Scholar 
47.Telesca, L. et al. Seagrass meadows (Posidonia oceanica) distribution and trajectories of change. Sci. Rep. 5, 12505 (2015).ADS 
CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
48.Marbà, N. & Duarte, C. Mediterranean warming triggers seagrass (Posidonia oceanica) shoot mortality. Glob. Change Biol. 16, 2366–2375 (2010).49.Beca-Carretero, P., Guiheneuf, F., Krause-Jensen, D. & Stengel, D. B. Seagrass fatty acid profiles as a sensitive indicator of climate settings across seasons and latitudes. Mar. Environ. Res. 161, 105075 (2020).CAS 
PubMed 
Article 

Google Scholar 
50.Marín-Guirao, L., Ruiz, J., Dattolo, E., Garcia-Munoz, R. & Procaccini, G. Physiological and molecular evidence of differential short-term heat tolerance in Mediterranean seagrasses. Sci. Rep. 6, 28615 (2016).ADS 
PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
51.Marín-Guirao, L., Entrambasaguas, L., Dattolo, E., Ruiz, J. M. & Procaccini, G. Mechanisms of resistance to intense warming events in an iconic seagrass species. Front. Plant Sci. 8, 1142 (2017).PubMed 
PubMed Central 
Article 

Google Scholar 
52.Tutar, O., Marín-Guirao, L., Ruiz, J. M. & Procaccini, G. Antioxidant response to heat stress in seagrasses. A gene expression study. Mar. Environ. Res. 132, 94–102 (2017).CAS 
PubMed 
Article 

Google Scholar 
53.Marín-Guirao, L., Entrambasaguas, L., Ruiz, J. M. & Procaccini, G. Heat-stress induced flowering can be a potential adaptive response to ocean warming for the iconic seagrass Posidonia oceanica. Mol. Ecol. 28, 2486–2501 (2019).PubMed 
Article 

Google Scholar 
54.Peirano, A. et al. Phenology of the Mediterranean seagrass Posidonia oceanica (L.) Delile: Medium and long-term cycles and climate inferences. Aquat. Bot. 94(2), 77–92 (2011).Article 

Google Scholar 
55.Walker, L. R., Wardle, D. A., Bardgett, R. D. & Clarkson, B. D. The use of chronosequences in studies of ecological succession and soil development. J. Ecol. 98(4), 725–736 (2010).Article 

Google Scholar 
56.Shaltaut, M. & Omstedt, A. Recent sea surface temperature trends and future scenarios for the Mediterranean. Oceanologia 56(3), 441–443 (2014).
Google Scholar 
57.Adloff, F. et al. Mediterranean sea response to climate change in an ensemble of twenty first century scenarios. Clim. Dyn. 45, 2775–2802 (2015).Article 

Google Scholar 
58.E.C. Marine Strategy Framework Directive 2008/56/EC of the European Parliament and of the Council, of 17 June 2008, establishing a framework for Community action in the field of marine environmental policy (Marine Strategy Framework Directive). OJEU 164, 19–40 (2008).59.Montefalcone, M. Ecosystem health assessment using the Mediterranean seagrass Posidonia oceanica: A review. Ecol. Indic. 9, 595–604 (2009).Article 

Google Scholar 
60.Steinacher, M. et al. Projected 21st century decrease in marine productivity: A multi-model analysis. Biogeosciences 7, 979–1005 (2010).ADS 
CAS 
Article 

Google Scholar 
61.Taucher, J. & Oschlies, A. Can we predict the direction of marine primary production change under global warming?. Geophys. Res. Lett. 38, LO2603 (2011).ADS 
Article 
CAS 

Google Scholar 
62.Dutkiewicz, S. et al. Ocean colour signature of climate change. Nat. Commun. 10, 578 (2019).ADS 
CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
63.Kim, G.-U., Seo, K.-H. & Chen, D. Climate change over the Mediterranean and current destruction of marine ecosystem. Sci. Rep. 9, 18813 (2019).ADS 
CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
64.Kimball, S., Angert, A. L., Huxman, T. E. & Venable, D. L. Contemporary climate change in the Sonoran Desert favors cold-adapted species. Glob. Change Biol. 16, 1555–1565 (2010).ADS 
Article 

Google Scholar 
65.Graae, B. J. et al. On the use of weather data in ecological studies along altitudinal and latitudinal gradients. Oikos 121, 3–19 (2012).Article 

Google Scholar 
66.Pergent, G., Pergent-Martini, C. & Boudouresque, C. F. Utilisation de l’herbier a Posidonia oceanica comme indicateur biologique de la qualite du milieu littoral en Mediterranee: etat des connaissances. Mesogee 54, 3–27 (1995).
Google Scholar 
67.Pergent-Martini, C. & Pergent, G. Spatio-temporal dynamics of Posidonia oceanica beds near a sewage outfall (Mediterranean, France). in Seagrass Biology: Proceeding of an International Workshop, Rottnest Island, Australia, 25–29 January 1996. Faculty of Sciences, the University of Western Australia Publications: Nedlands, Australia, pp. 299–306 (Kuo, J., Phillips, R. C., Walker, D. I., Kirkman, H. eds.) (1996).68.Scardi, M., Chessa, L. A., Fresi, E., Pais, A. & Serra, S. Optimizing interpolation of shoot density data from a Posidonia oceanica seagrass bed. Mar. Ecol. 27, 339–349 (2006).ADS 
Article 

Google Scholar 
69.Kun-Seop, L., Sang, R. P. & Young, K. K. Effects of irradiance, temperature and nutrients on growth dynamics of seagrasses: A review. J. Exp. Mar. Biol. Ecol. 350(1), 144–175 (2007).
Google Scholar 
70.Molenaar, H., Barthélémy, D., de Reffye, P., Meinesz, A. & Mialet, I. Modelling architecture and growth patterns of Posidonia oceanica. Aquat. Bot. 66, 85–99 (2000).Article 

Google Scholar 
71.Olesen, B., Enrìquez, S., Duarte, C. M. & Sand-Jensen, K. Depth-acclimation of photosynthesis, morphology and demography of Posidonia oceanica and Cymodocea nodosa in the Spanish Mediterranean Sea. Mar. Ecol. Progr. Ser. 236, 89–97 (2002).ADS 
Article 

Google Scholar 
72.Ralph, P. J., Durako, M. J., Enriquez, S., Collier, C. J. & Doblin, M. A. Impact of light limitation on seagrasses. J. Exp. Mar. Biol. Ecol. 350, 176–193 (2007).Article 

Google Scholar 
73.Ekstam, B. Ramet size equalization in a clonal plant, Phragmites australis. Oecologia 104, 440–446 (1995).ADS 
CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
74.Van Kleunen, M., Fischer, M. & Schmid, B. Effects of intraspecific competition on size variation and reproductive allocation in a clonal plant. Oikos 94, 515–524 (2001).Article 

Google Scholar 
75.Campagne, C. S., Salles, J. M., Boissery, P. & Deter, J. The seagrass Posidonia oceanica: Ecosystem services identification and economic evaluation of goods and benefits. Mar. Pollut. Bull. 97, 391–400 (2015).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
76.Nordlund, L. M., Koch, E. W., Barbier, E. B. & Creed, J. C. Seagrass ecosystem services and their variability across genera and geographical regions. PLoS ONE 1(10), e0163091 (2016).Article 
CAS 

Google Scholar 
77.Repolho, T. et al. Seagrass ecophysiological performance under ocean warming and acidification. Sci. Rep. 7, 41443 (2017).ADS 
CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
78.Adams, M. P. et al. Predicting seagrass decline due to cumulative stressors. Environ. Model. Softw. 130, 104717 (2020).Article 

Google Scholar 
79.Pazzaglia, J., Reusch, T. B. H., Terlizzi, A., Marín-Guirao, L. & Procaccini, G. Phenotypic plasticity under rapid global changes: The intrinsic force for future seagrasses survival. Evol. Appl. 00, 1–21 (2021).
Google Scholar 
80.Olita, A., Ribotti, A., Fazioli, L., Perilli, A. & Sorgente, R. Surface circulation and upwelling in the Sardinia Sea: A numerical study. Cont. Shelf Res. 71, 95–108 (2013).ADS 
Article 

Google Scholar 
81.Pinardi, N. et al. Mediterranean Sea large-scale low-frequency ocean variability and water mass formation rates from 1987 to 2007: A retrospective analysis. Prog. Oceanogr. 132, 318–332 (2015).ADS 
Article 

Google Scholar 
82.Smale, D. A. & Wernberg, T. Satellite-derived SST data as a proxy for water temperature in nearshore benthic ecology. Mar. Ecol. Progr. Ser. 387, 27–37 (2009).ADS 
Article 

Google Scholar 
83.Giraud, G. Contribution à la description et à la phénologie quantitative des herbiers de Posidonia oceanica (L.) Delile. Thèse de Doctorat de Spécialité en Océanologie, Université d’Aix-Marseille, Marseille (1977).84.Pergent, G. Lepidochronological analyses of the seagrass Posidonia oceanica (L.) Delile: a standardized approach. Aquat. Bot. 37, 39–54 (1990).85.Pagès, J. F. et al. Indirect interactions in seagrasses: Fish herbivores increase predation risk to sea urchins by modifying plant traits. Funct. Ecol. 26, 1015–1023 (2012).Article 

Google Scholar 
86.Zuur, A. F., Leno, E. N. & Elphick, C. S. A protocol for data exploration to avoid common statistical problems. Methods Ecol. Evol. 1, 3–14 (2010).Article 

Google Scholar 
87.R Core Team. R: A Language and Environment for Statistical Computing. (R Foundation for Statistical Computing, 2018).88.Venables, W. N. & Ripley, B. D. Modern Applied Statistics with S 4th edn. (Springer, 2002).MATH 
Book 

Google Scholar  More

After 90 years of taxonomic uncertainties, using phenotypic, phylogenetic, and population genetics analyses, the two uncultivated fungi causing skin disease in humans and dolphins, long known as Lacazia loboi8, are now placed as separate species within the genus Paracoccidioides. Early studies using phenotypic or phylogenetic data alone erroneously placed these two fungal pathogens in different genera and species3,4,5,6,7,8,12,13,15,16,17,24,25. This trend persisted for years2,13,16,17,25. For instance, recent studies using several partial DNA sequences recovered from Brazilian humans with skin disease in phylogenetic analyses concluded that the genus Lacazia, the accepted name at that time, was an independent taxon from Paracoccidioides species16,24,25. Their phylogenetic data was correct, but their analyses missed the inclusion of DNA from the uncultivated pathogen causing skin disease in dolphins. This was an understandable mistake, since the collection and processing specimens from infected dolphins is highly regulated and the fact that the etiology of dolphins’ disease was long believed to be the same as that in humans, as shown in Fig. 1 and Table 1. Although P. cetii has numerous phenotypic differences with Paracoccidioides species (Table 1, Fig. 1), in the pass used to group them in separated clusters2,3,7,8, our data showed they share several phylogenetic features in common (Figs. 4, 5 and 6). With the addition of P. cetii DNA sequences, the phylogenetic support of closely related Paracoccidioides species dramatically changed. For example, P. loboi clustered in a monophyletic group sister to P. lutzii, even with the inclusion of homologous dimorphic Onygenales DNA sequences as outgroup (Figs. 4b, 5), whereas the support of monophyletic species within the genus weakened (Figs. 4, 5 and 6). More dolphin DNA sequences from different geographical locations must be sequenced to understand P. cetii´s true evolutionary traits.Several studies reported geographical cryptic speciation among Paracoccidioides species14,24,26,27,28. In those analyses the presence of at least five species within the genus, including P. lutzii, was found14,15,24,27,29,30. Recent genome sequencing in phylogenetic analysis tend to validate these findings26,28,29. Although the DNA sequences of P. loboi were used in some of the analyses, the human skin pathogen was always placed as an independent genus from that in Paracoccidioides species16,24,25. The placement of P. cetii sister to P. americana DNA sequences in this study, indicates the use of phenotypic or phylogenetic characteristics without the inclusion of anomalous species, can lead to inaccuracies in the taxonomic and phylogenetic classification of these type of microbes. For instance, our data, using several statistical tools, consistently showed the presence of different clusters within Paracoccidioides species. In our analyses, P. americana, P. cetii, P. lutzii, and P. loboi were placed in monophyletic groups sister to the remaining Paracoccidioides species (Figs. 2, 3, 4, 5 and 6). Therefore, the addition of P. cetii to the genus Paracoccidioides not only confirmed that the genus has indeed a high level of speciation but, indicates that the concept of species delimitation in this genus must be revisited12,31.Recently, Vilela et al.16, using phylogenetic analysis of five different genes, showed P. loboi shared the same ancestor with Paracoccidioides species. The results in our study support their proposal. The main obstacle of this hypothesis at that time was the phenotypic features of P. loboi (Fig. 1). However, if P. loboi and P. cetii (both uncultivated and subcutaneous pathogens) share the same ancestor with other Paracoccidioides species (cultivated and causing systemic infections), the likelihood that the ancestor of Paracoccidioides species could growth in culture, as previously suggested, is a strong possibility16. If this concept is correct, when in the evolutionary history of P. cetii and P. loboi they lost the capacity to grow in culture? What evolutionary pressure triggered such a change? Sadly, as is common in neglected pathogens such as P. cetii and P. loboi key questions such as these, remain without an answer. Interestingly, the uncultivated feature found in these two neglected fungi was also reported in a strain of Histoplasma capsulatum infecting monkeys, suggesting that an uncultivated ancestral trait in the Onygenales dimorphic fungi may be at work32. However, the evolutionary pressures that triggered such ancestral feature remains an enigma.The report of new human cases of paracoccidioidomycosis loboi acquired by traveling to endemic areas2,3,4,5,33,34,35,36, suggests P. loboi may has a similar phenotype (hyphae with conidia) to the one displayed by Paracoccidioides species in nature and in culture. Thus, it may be present in specific ecological niches in the endemic areas (around the Amazon basin and other Latin American big rivers)2,14,15,25. Therefore, it is possible P. cetii and P. loboi may have a phenotype in nature similar to that of Paracoccidioides species (hyphae with conidia). Under this scenario, both uncultivated pathogens display a mycelia form with conidia and the classic life cycle style of dimorphic fungi in nature25. As is the case in other dimorphic fungi, these propagules could then contact susceptible hosts (human, dolphins) switching from hyphae → yeast thus, causing subcutaneous infections. Perhaps due to abnormalities on the molecular mechanisms of yeast → hyphae conversion (mutations?), once the hyphae → yeast conversion occurs, it cannot longer switch back from yeast to hyphal phase. However, the yeast phase of both pathogens can infect other hosts, as had been demonstrated in accidental and experimental infection with yeast-like cells from infected humans and dolphins2,37,38,39,40,41,42. Despite attempts made by the Broad Institute (https://www.broadinstitute.org/fungal-genome-initiative/lacazia-loboi-sequencing), only fragmented genomic information is available for P. loboi, and the genome of P. cetii is yet to be sequence. We hypothesize that the genomes of both uncultivated pathogens may hide important genomic clues that could answer this and other evolutionary questions.Several P. cetii DNA sequences recovered from dolphins captured in Brazil, Cuba, Japan, and the USA are currently available in the database (Table S1)19,20,21,22,23. The complete ITS DNA sequences from Brazilian and Cuban dolphins with paracoccidioidomycosis ceti, showed high percentage of identify with the DNA sequences in this study (ITS = 100%) whereas the partial Gp43 DNA sequences from a Japanese dolphin (471 bp) had 98.62% identity with P. cetii DNA sequences from dolphins captured in the Americas. During Gp43 DNA alignment of Japanese and USA dolphins, a five nucleotides gap was consistently present in the DNA sequences of USA dolphins. Moreover, two additional 266 bp GP43 DNA sequences extracted from a Japanese dolphin (Lagenorhynhus obliquidens) with paracoccidioidomycosis ceti showing, 99.62% identity with P. brasiliensis (sensu lato). In our analyses, these two sequences (only 110 bp could be used) clustered also with P. brasiliensis (Fig. 4, red rectangle). However, the same DNA sequences clustered close to P. cetii in haplotype analysis indicating a fragile relationship (Fig. 3). If P. cetii DNA sequences from Japanese dolphins are accurate, the differences in the genetic makeup of these two populations of uncultivated pathogens is intriguing and deserve further analysis. Our data suggest P. cetii strains causing paracoccidioidomycosis ceti in Japanese and USA dolphins, likely are evolving into two different populations.According to Teixeira et al.24, the estimated time for genetic divergence in Paracoccidioides species was calculated around 33 million years. Although, others have questioned this result31, Carruthers et al.43, cautioned that the use of linage-specific data usually demonstrate approximate divergence time regardless of the number of loci interrogated. Nonetheless, according to these reports, Paracoccidioides species probably diverged from their ancestor from a fraction of a million of years (P. restrepiensis and P. venezuelensis) to 10–30 million of years (P. lutzii and P. brasiliensis, sensu lato)24,31. Conversely, dolphins evolved into aquatic mammals ~ 50 to 30 million years ago, around late Paleocene period (Eocene, Oligocene epochs)44. According to fossil records, South America at this time had a large body of water crossing from the north Atlantic Ocean to what is today Bolivia, Brazil, Ecuador, Colombia, Peru and Venezuela45, all endemic areas of these species3,4,5,24,26,29, that lasted for millions of years. A similar situation occurred in what is today the estuary of the Amazon River. The current location of Paracoccidioides species (including P. loboi), coincide with the locations of such geological periods, and then it is quite possible that during the time following these geological events, an ancestor of P. cetii first encountered dolphins entering these areas. Since humans came to South Americas only ~ 15,000-year ago46, likely the ancestor of Paracoccidioides species infected dolphin first and later humans. Whether this event had a role on the pathogenic capabilities of the genus to infect mammals is difficult to determine, nonetheless it is an intriguing possibility.Working with uncultivated pathogens infecting the skin of mammals is challenging. Not only because collecting specimens from these species (dolphins are protected species and human cases are located in poor remote rural areas) is extremely difficult, but because open lesions usually harbor numerous environmental contaminants, which in the past had led to erroneous conclusions on the classifications of these two anomalous pathogens2,8,15,16,25,47. Furthermore, these unusual fungi are not in the list of neglected pathogens, thus discouraging investigators to submit proposals to funding organizations. Previous studies using P. loboi in phenotypic or phylogenetic analyses placed this anomalous pathogen away from the genus Paracoccidioides2,4,15,16,25. This study found that the use of phenotypic or phylogenetic approaches without the inclusion of DNA from infected dolphins, likely led previous studies to flawed data15,16,25. Thus, the failure of including organisms sharing a common ancestor, based in phenotypic or phylogenetic traits alone, could result in incomplete or incorrect assessment of the investigated populations. This study showed that the interpretation of taxonomic and/or phylogenetic data could be affected by missing neighboring anomalous taxa. More

1.Singh, M. & Bhatia, H. S. Thermal time requirement for phenophases of apple genotypes in Kullu valley. J. Agrometeorol. 13(1), 46–49 (2011).
Google Scholar 
2.Amgain, L. P. Agro-meteorological indices in relation to phenology and yields of promising wheat cultivars in Chitwan, Nepal. J. Agric. Environ. 14, 111–120 (2013).Article 

Google Scholar 
3.Nawaz, R., Abbasi, N. A., Hafiz, I. A. & Khalid, A. Color-break effect on Kinnow (Citrus nobilis Lour x Citrus deliciosa Tenora) fruit‘s internal quality at early ripening stages under varying environmental conditions. Sci. Hortic. 256, 108514 (2019).Article 

Google Scholar 
4.Singh, M. & Jangra, S. Thermal indices and heat use cultivars in Himachal Himalay. Clim. Change 4(14), 224–234 (2018).
Google Scholar 
5.Singh, M., Niwas, R., Godara, A. K. & Khichar, M. L. Pheno-thermal response of plum genotypes in semi-arid region of Haryana. J. Agrometeorol 17(2), 230–233 (2015).
Google Scholar 
6.Nawaz, R., Abbasi, N. A., Hafiz, I. A. & Khalid, A. Impact of varying agrometeorological indices on peel color and composition of Kinnow fruit (Citrus nobilis Lour x Citrus deliciosa Tenora) grown at different ecological zones. J. Sci. Food Agric. 100(6), 2688–2704 (2020).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
7.Nawaz, R., Abbasi, N. A., Hafiz, I. A., Khan, M. F. & Khalid, A. Environmental variables influence the developmental stages of the citrus leafminer, infestation level and mined leaves physiological response of Kinnow mandarin. Sci. Rep. 11(1), 1–20 (2021).Article 
CAS 

Google Scholar 
8.Plett, S. Comparison of seasonal thermal indices for measurement of corn maturity in a prairie environment. Can. J. Plant Sci. 72(4), 1157–1162 (1992).Article 

Google Scholar 
9.Dalal, R. P. S., Kumar, A. & Singh, R. Agrometeorological-heat and energy use of Kinnow Mandarin (Citrus nobilis Lour* Citrus deliciosa Tenore). Int. J. Pure App. Biosci 5(2), 506–512 (2017).Article 

Google Scholar 
10.Nawaz, R., Abbasi, N. A., Hafiz, I. A. & Khalid, A. Impact of climate variables on growth and development of Kinnow fruit (Citrus nobilis Lour x Citrus deliciosa Tenora) grown at different ecological zones under climate change scenario. Sci. Hortic. 260, 108868 (2020).Article 

Google Scholar 
11.Nawaz, R., Abbasi, N. A., Hafiz, I. A. & Khalid, A. Increasing level of abiotic and biotic stress on Kinnow fruit quality at different ecological zones in climate change scenario. Environ. Exp. Bot. 171, 103936 (2020).CAS 
Article 

Google Scholar 
12.Nawaz, R., Abbasi, N. A., Hafiz, I. A. & Khalid, A. Impact of climate variables on fruit internal quality of Kinnow mandarin (Citrus nobilis Lour x Citrus deliciosa Tenora) in ripening phase grown under varying environmental conditions. Sci. Hortic. 265, 109235 (2020).CAS 
Article 

Google Scholar 
13.Nawaz, R., Abbasi, N. A., Hafiz, I. A., Khalid, A. & Ahmad, T. Economic analysis of citrus (Kinnow Mandarin) during on-year and off-year in the Punjab Province. Pakistan. J Hortic 5(250), 2376–3354 (2018).
Google Scholar 
14.Khalid, M. S., Malik, A. U., Saleem, B. A., Khan, A. S. & Javed, N. Horticultural mineral oil application and tree canopy management improve cosmetic fruit quality of Kinnow mandarin. Afr. J. Agric. Res. 7(23), 3464–3472 (2012).Article 

Google Scholar 
15.Nawaz, R. et al. Impact of climate change on kinnow fruit industry of Pakistan. Agrotechnology https://doi.org/10.4172/2168-9881.1000186 (2019).Article 

Google Scholar 
16.Mazhar, M. S., Malik, A. U., Jabbar, A., Malik, O. H. & Khan, M. N. Fruit blemishes caused by abiotic and biotic factors in Kinnow mandarin. Acta Hortic. 1120, 483–490 (2016).Article 

Google Scholar 
17.Solomon. Intergovernmental Panel on Climate Change (IPCC). Climate Change 2007: The Physical Science Basis. Contributions of Working Group I to the Fourth Assessment Report of the Intergovernmental Panel on Climate Change. (Cambridge University Press, 2007).18.Ullah, R., Shivakoti, G. P. & Ali, G. Factors effecting farmers’ risk attitude and risk perceptions: The case of Khyber Pakhtunkhwa, Pakistan. Int. J. Disast. Risk Reduct. 13, 151–157 (2015).Article 

Google Scholar 
19.Ward, N. L. & Masters, G. J. Linking climate change and species invasion: An illustration using insect herbivores. Glob. Change Biol. 13(8), 1605–1615 (2007).ADS 
Article 

Google Scholar 
20.Hellmann, J. J., Byers, J. E., Bierwagen, B. G. & Dukes, J. S. Five potential consequences of climate change for invasive species. Conserv. Biol. 22(3), 534–543 (2008).PubMed 
Article 
PubMed Central 

Google Scholar 
21.Bale, J. S. et al. Herbivory in global climate change research: Direct effects of rising temperature on insect herbivores. Glob. Change Biol. 8(1), 1–6 (2002).ADS 
Article 

Google Scholar 
22.Stocker, T.F. et al. Climate Change 2013: The Physical Science Basis. Contribution of Working Group I to the Fifth Assessment Report of IPCC the Intergovernmental Panel on Climate Change (2014).23.Jones, G. V., White, M. A., Cooper, O. R. & Storchmann, K. Climate change and global wine quality. Clim. Change. 73, 319–343 (2005).ADS 
Article 

Google Scholar 
24.Webb, L., Whetton, P. & Barlow, E. W. R. Modeled impact of future climate change on phenology of wine grapes in Australia. Aust. J. Grape Wine Res. 13, 165–175 (2007).Article 

Google Scholar 
25.Ferguson, J. J., Koch, K. E. & Huang, T. B. 240 Fruit removal effects on growth and carbon allocation in young citrus trees. HortScience 34(3), 483D – 483 (1999).Article 

Google Scholar 
26.Zekri, M. Factors affecting citrus production and quality, Citrus Industry. ifas.ufl.edu (2011).27.Ladaniya, M. S. Physico−chemical, respiratory and fungicide residue changes in wax coated mandarin fruit stored at chilling temperature with intermittent warming. J. Food Sci. Technol. 48(2), 150–158 (2011).PubMed 
Article 
PubMed Central 

Google Scholar 
28.Monselise, S. P. & Goldschmidt, E. E. Alternate bearing in fruit trees. Hort. Rev. (Am. Soc. Hort. Sci.) 4, 128–173 (1982).
Google Scholar 
29.Garcia-Luis, A., Fornes, F. & Guardiola, J. L. Leaf carbohydrates and flower formation in Citrus. J. Am. Soc. Hort. Sci. 120, 222–227 (1995).CAS 
Article 

Google Scholar 
30.Dalezios, N. R., Loukas, A. & Bampzelis, D. Assessment of NDVI and agrometeorological indices for major crops in central Greece. Phys. Chem. Earth,Parts A/B/C 27(23–24), 1025–1029 (2002).ADS 
Article 

Google Scholar 
31.Dalezios, N. R., Loukas, A. & Bampzelis, D. The role of agrometeorological and agrohydrological indices in the phenology of wheat in central Greece. Phys. Chem. Earth Parts A/B/C 27(23–24), 1019–1023 (2002).ADS 
Article 

Google Scholar 
32.Schmidt, D. et al. Base temperature, thermal time and phyllochron of escarole cultivation. Hortic. Bras. 36(4), 466–472 (2018).Article 

Google Scholar 
33.Forland, E. J., Skaugen, T. E., Benestad, R. E., Hanssen-Bauer, I. & Tveito, O. E. Variations in thermal growing, heating, and freezing indices in the Nordic Arctic, 1900–2050. Arct. Antarct. Alp. Res. 36(3), 347–356 (2004).Article 

Google Scholar 
34.Gavilan, R. G. The use of climatic parameters and indices in vegetation distribution. A case study in the Spanish Sistema Central. Int. J. Biometeorol. 50(2), 111–120 (2005).ADS 
PubMed 
Article 

Google Scholar 
35.Schwartz, M. D., Ahas, R. & Aasa, A. Onset of spring starting earlier across the Northern Hemisphere. Glob. Change Biol. 12(2), 343–351 (2006).ADS 
Article 

Google Scholar 
36.Kaleem, S., Hassan, F. & Saleem, A. Influence of environmental variations on physiological attributes of sunflower. Afr. J. Biotechnol. 8(15) (2009).37.Monselise, S. P., Brosh, P. & Costo, J. Off-season bloom in ‘Temple’ orange repressed by Gibberellin [Treatment]. HortScience (1981).38.Davies, F. S. & Albrigo, L. G. Citrus Crop Production Science in Agriculture (CAB International, 1994).
Google Scholar 
39.Wheaton, T. A. Alternate bearing of citrus. Proc. Int. Semin. Citric. 1, 224–228 (1992).
Google Scholar 
40.Flore, J. A. & Lakso, A. N. Environmental and physiological regulation of photosynthesis in fruit crops. Hortic. Rev. 11, 111–157 (1986).
Google Scholar 
41.Goldschmidt, E. E. Carbohydrate supply as a critical factor for citrus fruit development and productivity. Hort. Sci. 34, 1020–1024 (1999).
Google Scholar 
42.Stander, O. P. J. 2018. Critical factors concomitant to the physiological development of alternate bearing in citrus (Citrus spp.) (Doctoral dissertation, Stellenbosch: Stellenbosch University) (2018).43.Iglesias, D. J. et al. Physiology of citrus fruiting. Braz. J. Plant. Physiol. 19(4), 333–362 (2007).CAS 
Article 

Google Scholar 
44.Scholefield, P. B., Oag, D. R. & Sedgley, M. The relationship between vegetative and reproductive development in the mango in northern Australia. Aust. J. Agric. Res. 37(4), 425–433 (1986).Article 

Google Scholar 
45.Goldschmidt, E. E. & Golomb, A. The carbohydrate balance of alternate-bearing citrus trees and the significance of reserves for flowering and fruiting. J. Am. Soc. Hort. Sci. 107, 206–208 (1982).
Google Scholar 
46.Hodgson, R. W. & Cameron, S. H. Studies on the bearing behavior of the “Fuerte” avocado variety. Calif. Avocado Soc. Yrbk. 1935, 150–165 (1935).
Google Scholar 
47.Seyyednejad, M., Ebrahimzadeh, H. & Talaie, A. Carbohydrate content in olive Zard cv and alternate bearing pattern. Int. Sugar J. 103(1226), 84–87 (2001).CAS 

Google Scholar 
48.Chacko, E. K., Reddy, Y. T. N. & Ananthanarayanan, T. V. Studies on the relationship between leaf number and area and fruit development in mango (Mangifera indica L). J. Hort. Sci. 57, 483–492 (1982).Article 

Google Scholar 
49.Nishikawa, F., Iwasaki, M., Fukamachi, H. & Matsumoto, H. The effect of fruit bearing on low-molecular-weight metabolites in stems of Satsuma Mandarin (Citrus unshiu Marc.). Hortic. J. 85(1), 23–29 (2016).CAS 
Article 

Google Scholar 
50.Verreynne, J. S. & Lovatt, C. J. The effect of crop load on budbreak influences return bloom in alternate bearing ‘Pixie’mandarin. J. Am. Soc. Hortic. Sci. 134(3), 299–307 (2009).Article 

Google Scholar 
51.Dovis, V. L. et al. Roots are important sources of carbohydrates during flowering and fruiting in ‘Valencia’sweet orange trees with varying fruit load. Sci. Hortic. 174, 87–95 (2014).CAS 
Article 

Google Scholar 
52.Martínez-Alcántara, B. et al. Carbon utilization by fruit limits shoot growth in alternate-bearing citrus trees. J. Plant Physiol. 176, 108–117 (2015).PubMed 
Article 
CAS 
PubMed Central 

Google Scholar 
53.Monerri, C. et al. Relation of carbohydrate reserves with the forthcoming crop, flower formation and photosynthetic rate, in the alternate bearing ‘Salustiana’ sweet orange (Citrus sinensis L.). Sci. Hortic. 129(1), 71–78 (2011).CAS 
Article 

Google Scholar 
54.Khan, S. R. A. Citrus Quality to meet Global Demand. Pakissan.com. http://www.pakissan.com/english/agri.overview/citrus.quality.to.meet.global.demand (2008).55.Moss, G. I., Bellamy, J. & Bevington, K. B. Controlling biennial bearing. Austral. Citrus News 50, 6–7 (1974).
Google Scholar 
56.Davis, K., Stover, E. & Wirth, F. Economic of fruit thinning: A review focusing on apple and citrus Production and marketing reports. Hort. Technol. 14(2), 282–289 (2004).Article 

Google Scholar 
57.Usman, M., Ashraf, I., Chaudhary, K. M. & Talib, U. Factors impeding citrus supply chain in central Punjab, Pakistan. Int. J. Agric. Ext. 6, 01–05 (2018).Article 

Google Scholar 
58.Ghafoor, U., Muhammad, S. & Chaudhary, K. M. Constrains in availability of inputs and information to citrus (Kinnow) growers of tehsil Toba Tek Singh, Pakistan. J. Agric. Sci. 45(4), 520–522 (2008).
Google Scholar 
59.Choudhary, D., Singh, R., Dagar, C. S., Kumar, A. & Singh, S. Temperature based agrometeorological indices for Indian mustard under different growing environments in western Haryana, India. Int. J. Curr. Microbiol. App. Sci. 7(1), 1025–1035 (2018).Article 

Google Scholar 
60.Hardy, S. & Khurshid, T. Calculating heat units for citrus. In Primefacts (NSW Department of Primary Industries, 2007).
Google Scholar 
61.Bootsma, A., Anderson, D. & Gameda, S. Potential impacts of climate change on agroclimatic indices in southern regions of Ontario and Quebec. Tech. Bull. ECORC Contrib. 03–284, 69–92 (2004).
Google Scholar 
62.Gordeev, A. V., Kleschenko, A. D., Chernyakov, B. A. & Sirotenko, O. D. Bioclimatic Potential of Russia: Theory and Practice (Tovarischestvo nauchnykh izdanyi KMK, 2006) ((in Russian)).
Google Scholar 
63.Karing, P., Kallis, A. & Tooming, H. Adaptation principles of agriculture to climate change. Climate Res. 12(2–3), 175–183 (1999).ADS 
Article 

Google Scholar 
64.Chen, C. S. Digital computer simulation of heat units and their use for predicting plant maturity. Int. J. Biometeorol. 17(4), 329–335 (1973).ADS 
Article 

Google Scholar 
65.Darby, H. M. & Lauer, J. G. Harvest date and hybrid influence on corn forage yield, quality, and preservation. Agron. J. 94(3), 559–566 (2002).Article 

Google Scholar 
66.Cesaraccio, C., Spano, D., Duce, P. & Snyder, R. L. An improved model for determining degree-day values from daily temperature data. Int. J. Biometeorol. 45(4), 161–169 (2001).ADS 
CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
67.Fealy, R. & Fealy, R. M. The spatial variation in degree days derived from locational attributes for the 1961 to 1990 period. Ir. J. Agric. Food Res. 47, 1–11 (2008).
Google Scholar 
68.Dolkar, D. et al. Effect of meteorological parameters on plant growth and fruit quality of Kinnow mandarin. Indian J. Agric. Sci. 88(7), 1004–1012 (2018).
Google Scholar 
69.Ferree, D. C. & Warrington, I. J. (eds) Apples: Botany, Production, and Uses (CABI, 2003).
Google Scholar 
70.Moretti, C. L., Mattos, L. M., Calbo, A. G. & Sargent, S. A. Climate changes and potential impacts on postharvest quality of fruit and vegetable crops: A review. Food Res. Int. 43(7), 1824–1832 (2010).CAS 
Article 

Google Scholar 
71.Chelong, I. A. & Sdoodee, S. Pollen viability, pollen germination and pollen tube growth of shogun (Citrus reticulate Blanco) under climate variability in southern Thailand. J. Agric. Technol 8, 2297–2307 (2012).
Google Scholar 
72.García-Tejero, I. et al. Positive impact of regulated deficit irrigation on yield and fruit quality in a commercial citrus orchard [Citrus sinensis (L.) Osbeck, cv. salustiano]. Agric. Water Manag. 97(5), 614–622 (2010).Article 

Google Scholar 
73.Zekri, M. & Rouse, R. E. Citrus Problems in the Home Landscape (University of Florida Cooperative Extension Service, 2002).
Google Scholar 
74.Barkhordarian, A., Saatchi, S. S., Behrangi, A., Loikith, P. C. & Mechoso, C. R. A recent systematic increase in vapor pressure deficit over tropical South America. Sci. Rep. 9(1), 1–12 (2019).CAS 
Article 

Google Scholar 
75.Li, M., Yao, J., Guan, J. & Zheng, J. Observed changes in vapor pressure deficit suggest a systematic drying of the atmosphere in Xinjiang of China. Atmos. Res. 248, 105199 (2020).Article 

Google Scholar 
76.Carnicer, J., Barbeta, A., Sperlich, D., Coll, M. & Peñuelas, J. Contrasting trait syndromes in angiosperms and conifers are associated with different responses of tree growth to temperature on a large scale. Front. Plant Sci. 4, 409 (2013).PubMed 
PubMed Central 
Article 

Google Scholar 
77.Hatfield, J. L. & Prueger, J. H. Temperature extremes: Effect on plant growth and development. Weather Clim. Extrem. 10, 4–10 (2015).Article 

Google Scholar 
78.Brodribb, T. J. & McAdam, S. A. Passive origins of stomatal control in vascular plants. Science 331(6017), 582–585 (2011).ADS 
CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
79.Mott, K. A. & Peak, D. Testing a vapour-phase model of stomatal responses to humidity. Plant Cell Environ. 36(5), 936–944 (2013).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
80.Allen, L. H. & Vu, J. C. Carbon dioxide and high temperature effects on growth of young orange trees in a humid, subtropical environment. Agric. For. Meteorol. 149(5), 820–830 (2009).ADS 
Article 

Google Scholar 
81.Novick, K. A. et al. The increasing importance of atmospheric demand for ecosystem water and carbon fluxes. Nat. Clim. Change 6(11), 1023–1027 (2016).ADS 
CAS 
Article 

Google Scholar 
82.De Carcer, P. S., Signarbieux, C., Schlaepfer, R., Buttler, A. & Vollenweider, P. Responses of antinomic foliar traits to experimental climate forcing in beech and spruce saplings. Environ. Exp. Bot. 140, 128–140 (2017).Article 

Google Scholar 
83.Dai, A. Increasing drought under global warming in observations and models. Nat. Clim. Change 3(1), 52–58 (2013).ADS 
Article 

Google Scholar 
84.Franks, P. J., Cowan, I. R. & Farquhar, G. D. The apparent feedforward response of stomata to air vapour pressure deficit: Information revealed by different experimental procedures with two rainforest trees. Plant Cell Environ. 20(1), 142–145 (1997).Article 

Google Scholar 
85.Grossiord, C. et al. Plant responses to rising vapor pressure deficit. New Phytol. 226(6), 1550–1566 (2020).PubMed 
Article 
PubMed Central 

Google Scholar 
86.do Carmo Araújo, S. A. et al. Photosynthetic characteristics of dwarf elephant grass (Pennisetum purpureum Schum.) genotypes, under stress water. Acta Sci. Anim. Sci. 32(1), 1–7 (2010).
Google Scholar 
87.Shirke, P. A. & Pathre, U. V. Influence of leaf-to-air vapour pressure deficit (VPD) on the biochemistry and physiology of photosynthesis in Prosopis juliflora. J. Exp. Bot. 55(405), 2111–2120 (2004).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
88.Ribeiro, R. V., Machado, E. C., Santos, M. G. & Oliveira, R. F. Photosynthesis and water relations of well-watered orange plants as affected by winter and summer conditions. Photosynthetica 47(2), 215–222 (2009).Article 

Google Scholar 
89.Ribeiro, R. V., Machado, E. C., Santos, M. G. & Oliveira, R. F. Seasonal and diurnal changes in photosynthetic limitation of young sweet orange trees. Environ. Exp. Bot. 66(2), 203–211 (2009).CAS 
Article 

Google Scholar 
90.Wong, S. C., Cowan, I. R. & Farquhar, G. D. Stomatal conductance correlates with photosynthetic capacity. Nature 282(5737), 424–426 (1979).ADS 
Article 

Google Scholar 
91.Bevington, K. B. & Castle, W. S. Annual root growth pattern of young citrus trees in relation to shoot growth, soil temperature, and soil water content. J. Am. Soc. Hortic. Sci. 110(6), 840–845 (1985).
Google Scholar 
92.Khurshid, T. & Hutton, R. J. Heat unit mapping a decision support system for selection and evaluation of citrus cultivars. In International Symposium on Harnessing the Potential of Horticulture in the Asian-Pacific Region 694, 265–269 (2004).93.Dalal, R. P. S. & Raj Singh, A. K. ,. Prevailing weather condition impact on different phenophases of Kinnow Mandarin (Citrus nobilis Lour* Citrus deliciosa Tenore). Int. J. Pure App. Biosci 5(2), 497–505 (2017).Article 

Google Scholar 
94.Koshita, Y. Effect of temperature on fruit color development. In Abiotic Stress Biology in Horticultural Plants 47–58 (Springer, 2015).
Google Scholar 
95.Sastry, P. S. N. & Chakravarty, N. V. K. Energy summation indices for wheat crop in India. Agric. Meteorol. 27, 45–48 (1982).Article 

Google Scholar 
96.Moriondo, M., Giannakopoulos, C. & Bindi, M. Climate change impact assessment: The role of climate extremes in crop yield simulation. Clim. Change 104(3), 679–701 (2011).ADS 
Article 

Google Scholar 
97.Hilgeman, R. H., Dunlap, J. A. & Sharp, P. O. Effect of time of harvest of ‘Valencia’ oranges in Arizona on fruit grade and size and yield, the following year. Proc. Amer. Soc. Hort. Sci. 90, 103–109. Fruit Load Limits Root Growth, Summer Vegetative Shoot Development, and Flowering in Alternatebearing ‘Nadorcott’ Mandarin Trees (1967).98.Dalal, R. P. S., Beniwal, B. S. & Sehrawat, S. K. Seasonal variation in growth, leaf physiology and fruit development in Kinnow, a Mandarin Hybrid. J. Plant Stud. 2(1), 72–77 (2013).
Google Scholar 
99.Bower, J. P. The Pre-and post -Harvest Application Potential for Crop- Set TM and ISR2000TM on Citrus. http://en.engormix.com/MAagriculture/articles/th-pre (2007).100.Sharma, N., Sharma, S. & Niwas, R. Thermal time and phenology of citrus in semi-arid conditions. J. Pharmacogn. Phytochem. 6(5), 27–30 (2017).
Google Scholar 
101.Goldschmidt, E. E. & Koch, K. E. Citrus. In Photoassimilate Distribution in Plants and Crops: Source-Sink Relations (eds Zaminski, E. & Schaffer, A. A.) 797–823 (Marcel Dekker, 1996).
Google Scholar 
102.Munoz-Fambuena, N. et al. Fruit regulates seasonal expression of flowering genes in alternate-bearing ‘Moncada’ mandarin. Ann. Bot. 108, 511–519 (2011).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
103.Shalom, L. et al. Fruit load induces changes in global gene expression and in abscisic acid (ABA) and indole acetic acid (IAA) homeostasis in citrus buds. J. Exp. Bot. 65(12), 3029–3044 (2014).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
104.Smith, P. F. Collapse of ‘Murcott’ tangerine trees [Root starvation]. J. Am. Soc. Hortic. Sci. 101, 23–25 (1976).CAS 

Google Scholar 
105.Koshita, Y., Takahara, T., Ogata, T. & Goto, A. Involvement of endogenous plant hormones (IAA, ABA, GAs) in leaves and flower bud formation of satsuma mandarin (Citrus unshiu Marc). Sci. Hortic. 79(3–4), 185–194 (1999).CAS 
Article 

Google Scholar 
106.Whiley, A. W., Rasmussen, T. S. & Wolstenholme, B. N. Delayed harvest effects on yield, fruit size and starch cycling in avocado (Persea americana Mill.) in subtropical environments. I. the early-maturing cv. Fuerte. Sci. Hortic. 66(1–2), 23–34 (1996).CAS 
Article 

Google Scholar 
107.Syvertsen, J. P. & Lloyd, J. J. Citrus. Handb. Environ. Physiol. Fruit Crops 2, 65–99 (1994).
Google Scholar 
108.Scholefield, P. B., Sedgley, M. & Alexander, D. M. Carbohydrate cycling in relation to shoot growth, floral initiation and development and yield in the avocado. Sci. Hortic. 25(2), 99–110 (1985).Article 

Google Scholar 
109.Shalom, L. et al. Alternate bearing in citrus: Changes in the expression of flowering control genes and in global gene expression in on-versus off-crop trees. PLoS ONE 7(10), e46930 (2012).ADS 
CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
110.der Merwe, V. & Schalk, I. Studies on the Phenology and Carbohydrate Status of Alternate Bearing ‘Nadorcott’mandarin trees (Doctoral dissertation, Stellenbosch: Stellenbosch University, 2012).111.Ward, D. L. Factors affecting Pre-harvest Fruit Drop of Apple. Ph.D thesis. Virginia Polytechnic Institute and State University 143 (2004).112.Blanusa, T., Else, M. A., Davies, W. J. & Atkinson, C. J. Regulation of sweet cherry fruit abscission: The role of photo-assimilation, sugars and abscisic acid. J. Hortic. Sci. Biotechnol. 81(4), 613–620 (2006).CAS 
Article 

Google Scholar 
113.Nartvaranant, P., Sornsanid, K. & Nuanpraluk, S. Preharvest Fruit Drop and Seasonal Variation of Plant Nutrient in ‘Thongdee’and ‘Khao Nam Pleung’pummelo on Nakhon Chaisri-Mae Klong river basin regions. Research Project Report (Thailand Research Fund, 2010).
Google Scholar 
114.Ruiz, R., Garcıa-Luis, A., Monerri, C. & Guardiola, J. L. Carbohydrate availability in relation to fruitlet abscission in Citrus. Ann. Bot. 87(6), 805–812 (2001).CAS 
Article 

Google Scholar 
115.Atkinson, C. J. The effects of phloem girdling on the abscission of Prunus avium L. fruits. J. Hortic. Sci. Biotechnol. 77(1), 22–27 (2002).Article 

Google Scholar 
116.Spiegel-Roy, P. & Goldschmidt, E. E. The Biology of Citrus (Cambridge University Press, 1996).Book 

Google Scholar 
117.Thind, S. K. & Kumar, K. Integrated management of fruit drop in Kinnow mandarin. Indian J Hort 65(4), 497–499 (2008).
Google Scholar 
118.Kumar, A., Avasthe, R. K., Pandey, B., Lepcha, B. & Rahman, H. Effect of fruit size and orchard location on fruit quality and seed traits of mandarin (Citrus reticulata) in Sikkim Himalayas. Indian J. Agric. Sci. 81(9), 821 (2011).CAS 

Google Scholar 
119.Ashraf, M. Y., Gul, A., Ashraf, M., Hussain, F. & Ebert, G. Improvement in yield and quality of Kinnow (Citrus deliciosa × Citrus nobilis) by potassium fertilization. J. Plant Nutr. 33, 1625–1637 (2010).CAS 
Article 

Google Scholar 
120.Ibrahim, M., Ahmad, N., Anwar, S. A. & Majeed, T. Effect of micronutrients on citrus fruit yield growing on calcareous soils. In Advances in Plant and Animal Boron Nutrition 179–182 (2007).121.Razi, M. F. D., Khan, I. A. & Jaskani, M. J. Citrus plant nutritional profile in relation to Huanglongbing prevalence in Pakistan. Pak. J. Agri. Sci. 48, 299–304 (2011).
Google Scholar 
122.Valiente, J. I. & Albrigo, L. G. Flower bud induction of sweet orange trees [Citrus sinensis (L.) Osbeck]: Effect of low temperatures, crop load, and bud age. J. Am. Soc. Hortic. Sci. 129(2), 158–164 (2004).Article 

Google Scholar 
123.Yakushiji, H. et al. Sugar accumulation enhanced by osmoregulation in satsuma mandarin fruit. J. Am. Soc. Hortic. Sci. 121, 466–472 (1996).CAS 
Article 

Google Scholar 
124.Holland, N., Menezes, H. C. & Lafuente, M. T. Carbohydrates as related to the heat induced chilling tolerance and respiratory rate of ‘Fortune’ mandarin fruit harvested at different maturity stages. Postharvest Biol. Technol. 25, 181–191 (2002).CAS 
Article 

Google Scholar 
125.Chelong, I. A. & Sdoodee, S. Effect of climate variability and degree-day on development, yield and quality of shogun (Citrus reticulata Blanco) in Southern Thailand. J. Nat. Sci. 47, 333–341 (2013).
Google Scholar 
126.Khalid, M. S. et al. Geographical location and agro-ecological conditions influence kinnow mandarin (Citrus nobilis × Citrus deliciosa) fruit quality. Int. J. Agric. Biol. 20, 647–654 (2018).Article 

Google Scholar 
127.Guardiola, J. L. & García-Luis, A. Increasing fruit size in Citrus. Thinning and stimulation of fruit growth. Plant Growth Regul. 31(1–2), 121–132 (2000).CAS 
Article 

Google Scholar 
128.Hield, H. Z. & Hilgeman, R. H. Alternate bearing and chemical fruit thinning of certain citrus varieties. Proc. Intl. Citrus Symp. 3, 1145–1153 (1969).
Google Scholar 
129.Verreynne, J. S. The Mechanism and Underlying Physiology Perpetuating Alternate Bearing in ‘Pixie’mandarin (Citrus reticulata Blanco) (University of California, 2005).
Google Scholar 
130.Sanginés de Cárcer, P. et al. Vapor–pressure deficit and extreme climatic variables limit tree growth. Glob. Change Biol. 24(3), 1108–1122 (2018).ADS 
Article 

Google Scholar  More

1.Milligan, R. J., Spence, G., Roberts, J. M. & Bailey, D. M. Fish communities associated with cold-water corals vary with depth and substratum type. Deep Sea Res. Part I Oceanogr. Res. Pap. 114, 43–54 (2016).ADS 
Article 

Google Scholar 
2.Anderson, T. J., Syms, C., Roberts, D. A. & Howard, D. F. Multi-scale fish-habitat associations and the use of habitat surrogates to predict the organisation and abundance of deep-water fish assemblages. J. Exp. Mar. Bio. Ecol. 379, 34–42 (2009).Article 

Google Scholar 
3.Agardy, T., di Sciara, G. N. & Christie, P. Mind the gap: Addressing the shortcomings of marine protected areas through large scale marine spatial planning. Mar. Policy 35, 226–232 (2011).Article 

Google Scholar 
4.Hinderstein, L. M. et al. Mesophotic coral ecosystems: Characterization, ecology, and management. Coral Reefs 29, 247–251 (2010).ADS 
Article 

Google Scholar 
5.Baldwin, C. C., Tornabene, L. & Robertson, D. R. Below the mesophotic. Sci. Rep. 8, 1–13 (2018).
Google Scholar 
6.Hoegh-Guldberg, O., Poloczanska, E. S., Skirving, W. & Dove, S. Coral reef ecosystems under climate change and ocean acidification. Front. Mar. Sci. 4, 158 (2017).Article 

Google Scholar 
7.Rocha, L. A. et al. Mesophotic coral ecosystems are threatened and ecologically distinct from shallow water reefs. Science 361(6399), 281–284 (2018).ADS 
CAS 
PubMed 
Article 

Google Scholar 
8.Cerrano, C. et al. Temperate mesophotic ecosystems: gaps and perspectives of an emerging conservation challenge for the Mediterranean Sea. Eur. Zool. J. 86, 370–388 (2019).Article 

Google Scholar 
9.Williams, J., Jordan, A., Harasti, D., Davies, P. & Ingleton, T. Taking a deeper look: Quantifying the differences in fish assemblages between shallow and mesophotic temperate rocky reefs. PLoS ONE 14, e0206778 (2019).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
10.Lesser, M. P., Slattery, M. & Leichter, J. J. Ecology of mesophotic coral reefs. J. Exp. Mar. Bio. Ecol. 375, 1–8 (2009).Article 

Google Scholar 
11.Armstrong, R. A., Pizarro, O. & Roman, C. Underwater robotic technology for imaging mesophotic coral ecosystems. in Mesophotic Coral Ecosystems. 973–988. (Springer, 2019).12.Stoner, A. W., Ryer, C. H., Parker, S. J., Auster, P. J. & Wakefield, W. W. Evaluating the role of fish behavior in surveys conducted with underwater vehicles. Can. J. Fish. Aquat. Sci. 65, 1230–1243 (2008).Article 

Google Scholar 
13.Durden, J. M. et al. Perspectives in visual imaging for marine biology and ecology: From acquisition to understanding. Oceanogr. Mar. Biol. Annu. Rev. 54, 1–72 (2016).
Google Scholar 
14.Stevens, T. & Connolly, R. M. Local-scale mapping of benthic habitats to assess representation in a marine protected area. Mar. Freshw. Res. 56, 111–123 (2005).Article 

Google Scholar 
15.Bernard, A. T. et al. New possibilities for research on reef fish across the continental shelf of South Africa. S. Afr. J. Sci. 110, 1–5. https://doi.org/10.1590/sajs.2014/a0079 (2014).Article 

Google Scholar 
16.Rees, S. E., Foster, N. L., Langmead, O., Pittman, S. & Johnson, D. E. Defining the qualitative elements of Aichi Biodiversity Target 11 with regard to the marine and coastal environment in order to strengthen global efforts for marine biodiversity conservation outlined in the United Nations Sustainable Development Goal 14. Mar. Policy 93, 241–250 (2018).Article 

Google Scholar 
17.South African National Biodiversity Institute. South Africa Announces New Marine Protected Area Network. https://www.sanbi.org/media/south-africa-announces-new-marine-protected-area-network/. (2018).18.der Bank, V., Harris, L., Atkinson, L., Kirkman, S., & Karenyi, N. Marine Realm in South African National Biodiversity Assessment 2018 Technical Report. Vol. 4 (South African National Biodiversity Institute, 2019).19.Sink, K. The marine protected areas debate: Implications for the proposed Phakisa marine protected areas network. S. Afr. J. Sci. 112, 9–10 (2016).Article 

Google Scholar 
20.Turpie, J. K., Beckley, L. E. & Katua, S. M. Biogeography and the selection of priority areas for conservation of South African coastal fishes. Biol. Conserv. 92, 59–72 (2000).Article 

Google Scholar 
21.Götz, A. & Phillips, M. SAEON Elwandle Applies Expertise to Marine Protected Area Management in Amathole. http://www.saeon.ac.za/enewsletter/archives/2016/august2016/doc03 (2019).22.DEA (Department of Environmental Affairs). Notice Declaring the Amathole Offshore Marine Protected Area Under Section 22A of the National Environmental Management: Protected Areas Act, 2003 (Act No.57 of 2003). Government Gazette, Republic of South Africa (2016).23.Green, A. N. et al. Relict and contemporary influences on the postglacial geomorphology and evolution of a current swept shelf: The Eastern Cape Coast, South Africa. Mar. Geol. 427, 106230 (2020).ADS 
Article 

Google Scholar 
24.Parker, D., Winker, H., Attwood, C. & Kerwath, S. Dark times for dageraad Chrysoblephus cristiceps: Evidence for stock collapse. Afr. J. Mar. Sci. 38, 341–349. https://doi.org/10.2989/1814232X.2016.1200142 (2016).Article 

Google Scholar 
25.Kerwath, S. et al. Tracking the decline of the world’s largest seabream against policy adjustments. Mar. Ecol. Prog. Ser. 610, 163–173. https://doi.org/10.3354/meps12853 (2019).ADS 
Article 

Google Scholar 
26.African Coelacanth Ecosystem Programme Project. African Coelacanth Ecosystem Programme Project Overviews 2017/2018. (2018).27.Donovan, B. A Retrospective Assessment of the Port Alfred Linefishery with Respect to the Changes in the South African Fisheries Management Environment (Rhodes University, 2010).
Google Scholar 
28.International Union for Conservation of Nature and Natural Resources. The IUCN Red List of Threatened Species (IUCN Global Species Programme Red List Unit, 2017).
Google Scholar 
29.Götz, A., Kerwath, S. E., Attwood, C. G. & Sauer, W. H. H. Effects of fishing on population structure and life history of roman Chrysoblephus laticeps (Sparidae). Mar. Ecol. Prog. Ser. 362, 245–259 (2008).ADS 
Article 

Google Scholar 
30.McCord, M. & Zweig, T. Fisheries: Facts and Trends. http://awsassets.wwf.org.za/downloads/wwf_a4_fish_facts_report_lr.pdf (2011).31.Southern African Marine Linefish Species Profiles (South African Association for Marine Biological Research, 2013).32.Smith, J. L. B. Smiths’ Sea Fishes. https://doi.org/10.1007/978-3-642-82858-4 (Springer, 1986).33.Compagno, L. J. V., Ebert, D. A. & Smale, M. J. Guide to the Sharks and Rays of Southern Africa (Struik, 1989).
Google Scholar 
34.Peres, M. B. & Klippel, S. Reproductive biology of Southwestern Atlantic wreckfish, Polyprion americanus (Teleostei: Polyprionidae). Environ. Biol. Fish. 68, 163–173 (2003).Article 

Google Scholar 
35.Baillon, S., Hamel, J.-F., Wareham, V. E. & Mercier, A. Deep cold-water corals as nurseries for fish larvae. Front. Ecol. Environ. 10, 351–356 (2012).Article 

Google Scholar 
36.Sink, K. J., Boshoff, W., Samaai, T., Timm, P. G. & Kerwath, S. E. Observations of the habitats and biodiversity of the submarine canyons at Sodwana Bay: Coelacanth research. S. Afr. J. Sci. 102, 466–474 (2006).
Google Scholar 
37.Heemstra, P. C. & Heemstra, E. Coastal Fishes of Southern Africa (National Inquiry Services Centre, 2004).
Google Scholar 
38.Epstein, H. E. & Kingsford, M. J. Are soft coral habitats unfavourable? A closer look at the association between reef fishes and their habitat. Environ. Biol. Fish. 102, 479–497. https://doi.org/10.1007/s10641-019-0845-4 (2019).Article 

Google Scholar 
39.Booth, A. J. & Buxton, C. D. The biology of the panga, Pterogymnus laniarius (Teleostei: Sparidae), on the Agulhas Bank, South Africa. Environ. Biol. Fish. 49, 207–226 (1997).Article 

Google Scholar 
40.Turner, J. A., Babcock, R. C., Hovey, R. & Kendrick, G. A. Deep thinking: A systematic review of mesophotic coral ecosystems. ICES J. Mar. Sci. 74, 2309–2320. https://doi.org/10.1093/icesjms/fsx085 (2017).Article 

Google Scholar 
41.Heyns, E., Bernard, A. T., Richoux, N. & Götz, A. Depth-related distribution patterns of subtidal macrobenthos in a well-established marine protected area. Mar. Biol. 163, 39. https://doi.org/10.1007/s00227-016-2816-z (2016).CAS 
Article 

Google Scholar 
42.Bridge, T. C. L. et al. Topography, substratum and benthic macrofaunal relationships on a tropical mesophotic shelf margin, central Great Barrier Reef, Australia. Coral Reefs 30, 143–153 (2011).ADS 
Article 

Google Scholar 
43.Doty, M. S. & Oguri, M. The Island mass effect. ICES J. Mar. Sci. 22, 33–37 (1956).Article 

Google Scholar 
44.Fabricius, K. E., Logan, M., Weeks, S. & Brodie, J. The effects of river run-off on water clarity across the central Great Barrier Reef. Mar. Pollut. Bull. 84, 191–200 (2014).CAS 
PubMed 
Article 

Google Scholar 
45.Foster, M. S. Rhodoliths: Between rocks and soft places. J. Phycol. 37, 659–667 (2001).Article 

Google Scholar 
46.Littler, M. M., Littler, D. S. & Dennis Hanisak, M. Deep-water rhodolith distribution, productivity, and growth history at sites of formation and subsequent degradation. J. Exp. Mar. Bio. Ecol. 150, 163–182 (1991).Article 

Google Scholar 
47.Tait, R. V. & Dipper, F. Elements of marine ecology (Butterworth-Heinemann, 1998).
Google Scholar 
48.Williams, A. & Bax, N. J. Delineating fish-habitat associations for spatially based management: An example from the south-eastern Australian continental shelf. Mar. Freshw. Res. 52, 513 (2001).Article 

Google Scholar 
49.Pearson, R. & Stevens, T. Distinct cross-shelf gradient in mesophotic reef fish assemblages in subtropical eastern Australia. Mar. Ecol. Prog. Ser. 532, 185–196 (2015).ADS 
Article 

Google Scholar 
50.MacDonald, C., Bridge, T. & Jones, G. Depth, bay position and habitat structure as determinants of coral reef fish distributions: Are deep reefs a potential refuge?. Mar. Ecol. Prog. Ser. 561, 217–231 (2016).ADS 
Article 

Google Scholar 
51.Fukunaga, A., Kosaki, R. K. & Wagner, D. Changes in mesophotic reef fish assemblages along depth and geographical gradients in the Northwestern Hawaiian Islands. Coral Reefs 36, 785–790 (2017).ADS 
Article 

Google Scholar 
52.Sih, T. L., Cappo, M. & Kingsford, M. Deep-reef fish assemblages of the Great Barrier Reef shelf-break (Australia). Sci. Rep. 7, 10886 (2017).ADS 
PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
53.Colwell, R. K. & Lees, D. C. The mid-domain effect: Geometric constraints on the geography of species richness. Trends Ecol. Evol. 15, 70–76 (2000).CAS 
PubMed 
Article 

Google Scholar 
54.Colwell, R. K., Rahbek, C. & Gotelli, N. J. The mid-domain effect and species richness patterns: What have we learned so far?. Am. Nat. 163, E1-23 (2004).PubMed 
Article 

Google Scholar 
55.Makwela, M. S. et al. Notes on a remotely operated vehicle survey to describe reef ichthyofauna and habitats—Agulhas Bank, South Africa. Bothalia 46, 1–7 (2016).Article 

Google Scholar 
56.Quantum GIS Development Team. Quantum GIS Geographic Information System. (2002).57.Kleczkowski, M., Babcock, R. C. & Clapin, G. Density and size of reef fishes in and around a temperate marine reserve. Mar. Freshw. Res. 59, 165 (2008).Article 

Google Scholar 
58.Althaus, F. et al. A standardised vocabulary for identifying benthic biota and substrata from underwater imagery: The CATAMI classification scheme. PLoS ONE 10, e0141039 (2015).PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
59.R Core Team. R: A Language and Environment for Statistical Computing. https://www.R-project.org/. (R Foundation for Statistical Computing, 2020).60.Charrad, M., Ghazzali, N., Boiteau, V. & Maintainer, A. N. NbClust: An R package for determining the relevant number of clusters in a data set. J. Stat. Softw. 61, 1 (2014).Article 

Google Scholar 
61.Liaw, A. & Wiener, M. Classification and regression by randomForest. R. News 2, 18–22 (2002).
Google Scholar 
62.De’ath, G. Multivariate regression trees: A new technique for modeling species-environment relationships. Ecology 83, 1105 (2002).
Google Scholar 
63.Zuur, A. F. Mixed Effects Models and Extensions in Ecology with R (Springer, 2009).MATH 
Book 

Google Scholar 
64.South African National Biodiversity Institute. https://www.sanbi.org/. (2021). More

1.Mauchamp, L., Mouly, A., Badot, P.-M. & Gillet, F. Impact of nitrogen inputs on multiple facets of plant biodiversity in mountain grasslands: Does nutrient source matter?. Appl. Veg. Sci. 19, 206–217 (2016).Article 

Google Scholar 
2.Mesbahi, G., Michelot-Antalik, A., Goulnik, J. & Plantureux, S. Permanent grassland classifications predict agronomic and environmental characteristics well, but not ecological characteristics. Ecol. Indic. 110, 105956 (2020).Article 

Google Scholar 
3.Karimi, B. et al. Biogeography of soil microbial habitats across France. Glob. Ecol. Biogeogr. 29, 1399–1411 (2020).Article 

Google Scholar 
4.Mahaut, L., Fort, F., Violle, C. & Freschet, G. T. Multiple facets of diversity effects on plant productivity: Species richness, functional diversity, species identity and intraspecific competition. Funct. Ecol. 34, 287–298 (2020).Article 

Google Scholar 
5.Tilman, D. The ecological consequences of changes in biodiversity: A search for general principles. Ecology 80, 1455–1474 (1999).
Google Scholar 
6.van der Heijden, M. G. A., Bardgett, R. D. & van Straalen, N. M. The unseen majority: Soil microbes as drivers of plant diversity and productivity in terrestrial ecosystems. Ecol. Lett. 11, 296–310 (2008).PubMed 
Article 
PubMed Central 

Google Scholar 
7.Tardy, V. et al. Stability of soil microbial structure and activity depends on microbial diversity: Linking microbial diversity and stability. Environ. Microbiol. Rep. 6, 173–183 (2014).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
8.Philippot, L. et al. Loss in microbial diversity affects nitrogen cycling in soil. ISME J. 7, 1609–1619 (2013).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
9.Fierer, N., Barberan, A. & Laughlin, D. C. Seeing the forest for the genes: Using metagenomics to infer the aggregated traits of microbial communities. Front. Microbiol. 5, 614 (2014).PubMed 
PubMed Central 
Article 

Google Scholar 
10.Loreau, M. Linking biodiversity and ecosystems: Towards a unifying ecological theory. Philos. Trans. R. Soc. B 365, 49–60 (2010).Article 

Google Scholar 
11.Buchin, S., Martin, B., Dupont, D., Bornard, A. & Achilleos, C. Influence of the composition of Alpine highland pasture on the chemical, rheological and sensory properties of cheese. J. Dairy Res. 66, 579–588 (1999).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
12.Bugaud, C., Buchin, S., Hauwy, A. & Coulon, J.-B. Flavour and texture of cheeses according to grazing type: The Abundance cheese. INRA Prod. Anim. 15, 31–36 (2002).Article 

Google Scholar 
13.Monnet, J. C., Berodier, F. & Badot, P. M. Characterization and localization of a cheese georegion using edaphic criteria (Jura Mountains, France). J. Dairy Sci. 83, 1692–1704 (2000).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
14.Mariotte, P., Vandenberghe, C., Kardol, P., Hagedorn, F. & Buttler, A. Subordinate plant species enhance community resistance against drought in semi-natural grasslands. J. Ecol. 101, 763–773 (2013).Article 

Google Scholar 
15.Montel, M.-C. et al. Traditional cheeses: Rich and diverse microbiota with associated benefits. Int. J. Food Microbiol. 177, 136–154 (2014).PubMed 
Article 
PubMed Central 

Google Scholar 
16.Bouton, Y., Guyot, P., Berthier, F., & Beuvier, E. Investigation of bacterial community development from raw milk and starter to curd and mature Comte cheese. in Cheese ripening and technology: abstracts of IDF symposium held in Banff, Canada, March 2000 (ed. International Dairy Federation) 85 (Brussel, Belgium, 2000).17.Demarigny, Y., Beuvier, E., Buchin, S., Pochet, S. & Grappin, R. Influence of raw milk microflora on the characteristics of Swiss-type cheese. Lait 77, 151–167 (1997).CAS 
Article 

Google Scholar 
18.Bouton, Y., Buchin, S., Duboz, G., Pochet, S. & Beuvier, E. Effect of mesophilic lactobacilli and enterococci adjunct cultures on the final characteristics of a microfiltered milk Swiss-type cheese. Food Microbiol. 26, 183–191 (2009).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
19.Vacheyrou, M. et al. Cultivable microbial communities in raw cow milk and potential transfers from stables of sixteen French farms. Int. J. Food Microbiol. 146, 253–262 (2011).PubMed 
Article 
PubMed Central 

Google Scholar 
20.Verdier-Metz, I. et al. Cow teat skin, a potential source of diverse microbial populations for cheese production. Appl. Environ. Microbiol. 78, 326–333 (2012).ADS 
CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
21.Doyle, C. J., Gleeson, D., O’Toole, P. W. & Cotter, P. D. Impacts of seasonal housing and teat preparation on raw milk microbiota: A high-throughput sequencing study. Appl. Environ. Microbiol. 83(e02694–16), e02694-e2716 (2017).CAS 
PubMed 
PubMed Central 

Google Scholar 
22.Frétin, M. et al. Bacterial community assembly from cow teat skin to ripened cheeses is influenced by grazing systems. Sci. Rep. 8, 200 (2018).ADS 
PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
23.Falentin, H. et al. Bovine teat microbiome analysis revealed reduced alpha diversity and significant changes in taxonomic profiles in quarters with a history of mastitis. Front. Microbiol. 7, 480 (2016).PubMed 
PubMed Central 
Article 

Google Scholar 
24.Fierer, N. Embracing the unknown: disentangling the complexities of the soil microbiome. Nat. Rev. Microbiol. 15, 579–590 (2017).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
25.Vorholt, J. A. Microbial life in the phyllosphere. Nat. Rev. Microbiol. 10, 828–840 (2012).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
26.Dequiedt, S. et al. Biogeographical patterns of soil bacterial communities. Environ. Microbiol. Rep. 1, 251–255 (2009).PubMed 
Article 
PubMed Central 

Google Scholar 
27.Sadet-Bourgeteau, S. et al. Lasting effect of repeated application of organic waste products on microbial communities in arable soils. Appl. Soil Ecol. 125, 278–287 (2018).Article 

Google Scholar 
28.Nacke, H. et al. Pyrosequencing-based assessment of bacterial community structure along different management types in german forest and grassland soils. PLoS ONE 6, e17000 (2011).ADS 
CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
29.Coolon, J. D., Jones, K. L., Todd, T. C., Blair, J. M. & Herman, M. A. Long-term nitrogen amendment alters the diversity and assemblage of soil bacterial communities in Tallgrass Prairie. PLoS ONE 8, e67884 (2013).ADS 
CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
30.Toyota, K. & Kuninaga, S. Comparison of soil microbial community between soils amended with or without farmyard manure. Appl. Soil Ecol. 33, 39–48 (2006).Article 

Google Scholar 
31.Garnier, E. & Navas, M.-L. A trait-based approach to comparative functional plant ecology: Concepts, methods and applications for agroecology: A review. Agron. Sustain. Dev. 32, 365–399 (2012).Article 

Google Scholar 
32.Mauchamp, L., Mouly, A., Badot, P.-M. & Gillet, F. Impact of management type and intensity on multiple facets of grassland biodiversity in the French Jura Mountains. Appl. Veg. Sci. 17, 645–657 (2014).Article 

Google Scholar 
33.Chytrý, M. et al. European map of alien plant invasions based on the quantitative assessment across habitats. Divers. Distrib. 15, 98–107 (2009).Article 

Google Scholar 
34.Klaudisová, M., Hejcman, M. & Pavlů, V. Long-term residual effect of short-term fertilizer application on Ca, N and P concentrations in grasses Nardus stricta L. and Avenella flexuosa L. Nutr. Cycl. Agroecosyst. 85, 187–193 (2009).Article 
CAS 

Google Scholar 
35.Terrat, S. et al. Mapping and predictive variations of soil bacterial richness across France. PLoS ONE 12, e0186766 (2017).PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
36.Terrat, S. et al. Improving soil bacterial taxa–area relationships assessment using DNA meta-barcoding. Heredity 114, 468–475 (2015).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
37.Navrátilová, D. et al. Diversity of fungi and bacteria in species-rich grasslands increases with plant diversity in shoots but not in roots and soil. FEMS Microbiol. Ecol. https://doi.org/10.1093/femsec/fiy208 (2018).Article 

Google Scholar 
38.Zhang, Q. et al. Niche differentiation in the composition, predicted function, and co-occurrence networks in bacterial communities associated with antarctic vascular plants. Front. Microbiol. 11, 1036 (2020).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
39.Falardeau, J., Keeney, K., Trmčić, A., Kitts, D. & Wang, S. Farm-to-fork profiling of bacterial communities associated with an artisan cheese production facility. Food Microbiol. 83, 48–58 (2019).CAS 
PubMed 
Article 

Google Scholar 
40.Plassart, P. et al. Soil parameters, land use, and geographical distance drive soil bacterial communities along a European transect. Sci. Rep. 9, 605 (2019).ADS 
PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
41.Rastogi, G., Coaker, G. L. & Leveau, J. H. J. New insights into the structure and function of phyllosphere microbiota through high-throughput molecular approaches. FEMS Microbiol. Lett. 348, 1–10 (2013).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
42.Andrews, T., Neher, D. A., Weicht, T. R. & Barlow, J. W. Mammary microbiome of lactating organic dairy cows varies by time, tissue site, and infection status. PLoS ONE 14, e0225001 (2019).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
43.Karimi, B. et al. Biogeography of soil bacteria and archaea across France. Sci. Adv. 4, 1808 (2018).ADS 
Article 

Google Scholar 
44.Lewin, G. R. et al. Evolution and ecology of Actinobacteria and their bioenergy applications. Annu. Rev. Microbiol. 70, 235–254 (2016).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
45.Li, N. et al. Variation in raw milk microbiota throughout 12 months and the impact of weather conditions. Sci. Rep. 8, 2371 (2018).ADS 
PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
46.Lavoie, K., Touchette, M., St-Gelais, D. & Labrie, S. Characterization of the fungal microflora in raw milk and specialty cheeses of the province of Quebec. Dairy Sci. Technol. 92, 455–468 (2012).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
47.Verdier-Metz, I. & Monsallier, F. Place des pâturages des bovins dans les flux microbiens laitiers. Fourrages 6, 1–10 (2012).
Google Scholar 
48.Laforest-Lapointe, I., Paquette, A., Messier, C. & Kembel, S. W. Leaf bacterial diversity mediates plant diversity and ecosystem function relationships. Nature 546, 145–147 (2017).ADS 
CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
49.Fierer, N. & Jackson, R. B. The diversity and biogeography of soil bacterial communities. Proc. Natl. Acad. Sci. USA 103, 626–631 (2006).ADS 
CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
50.Mallet, A. et al. Quantitative and qualitative microbial analysis of raw milk reveals substantial diversity influenced by herd management practices. Int. Dairy J. 27, 13–21 (2012).Article 

Google Scholar 
51.Kottek, M., Grieser, J., Beck, C., Rudolf, B. & Rubel, F. World Map of the Köppen-Geiger climate classification updated. Metz 15, 259–263 (2006).ADS 
Article 

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

Google Scholar 
53.QGIS.org. QGIS Geographic Information System. QGIS Association. http://www.qgis.org (2021).54.Homburger, H. & Hofer, G. Diversity change of mountain hay meadows in the Swiss Alps. Basic Appl. Ecol. 13, 132–138 (2012).Article 

Google Scholar 
55.Gillet, F., Mauchamp, L., Badot, P.-M. & Mouly, A. Recent changes in mountain grasslands: a vegetation resampling study. Ecol. Evol. 6, 2333–2345 (2016).PubMed 
PubMed Central 
Article 

Google Scholar 
56.Maabel, E. Transformation of cover-abundance values in phytosociology and its effects on community similarity. Vegetatio 39, 97–114 (1979).Article 

Google Scholar 
57.Jost, L. The relation between evenness and diversity. Diversity 26, 207–230 (2010).Article 

Google Scholar 
58.ChemidlinPrévost-Bouré, N. et al. Validation and application of a PCR primer set to quantify fungal communities in the soil environment by real-time quantitative PCR. PLoS ONE 6, e24166 (2011).ADS 
Article 
CAS 

Google Scholar 
59.Djemiel, C. et al. BIOCOM-PIPE: A new user-friendly metabarcoding pipeline for the characterization of microbial diversity from 16S, 18S and 23S rRNA gene amplicons. BMC Bioinform. 21, 492. https://doi.org/10.1186/s12859-020-03829-3 (2020).CAS 
Article 

Google Scholar 
60.Cole, J. R. et al. The ribosomal database project: Improved alignments and new tools for rRNA analysis. Nucleic Acids Res. 37, D141–D145 (2009).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
61.Bardou, P., Mariette, J., Escudié, F., Djemiel, C. & Klopp, C. jvenn: An interactive Venn diagram viewer. BMC Bioinform. 15, 293 (2014).Article 

Google Scholar 
62.Oksanen, J. et al. vegan: Community Ecology Package. R package version 2.5–7. https://CRAN.R-project.org/package=vegan (2020).63.Segata, N. et al. Metagenomic biomarker discovery and explanation. Genome Biol. 12, R60 (2011).PubMed 
PubMed Central 
Article 

Google Scholar 
64.Asnicar, F., Weingart, G., Tickle, T. L., Huttenhower, C. & Segata, N. Compact graphical representation of phylogenetic data and metadata with GraPhlAn. PeerJ 3, e1029. https://doi.org/10.7717/peerj.1029 (2015).Article 
PubMed 
PubMed Central 

Google Scholar 
65.Yekutieli, D. & Benjamini, Y. The control of the false discovery rate in multiple testing under dependency. Ann. Statist. 29, 1165–1188 (2001).MathSciNet 
MATH 

Google Scholar 
66.Gysi, D. M., Voigt, A., Fragoso, T. M., Almaas, E. & Nowick, K. wTO: an R package for computing weighted topological overlap and a consensus network with integrated visualization tool. BMC Bioinform. 19, 392 (2018).Article 

Google Scholar 
67.Kursa, M. B. & Rudnicki, W. R. Feature Selection with the Boruta Package. J. Stat. Soft. 36, 11 (2010).Article 

Google Scholar  More

AS systems display many novel and a high fraction of shared virusesWe used metagenomic sequencing of 30 Gb of sequences per sample to characterize the composition of viral concentrates across six WWTPs (see Methods). After assembly and mapping of reads, 24–34% of the total sequence information could be classified as viral using two different identification pipelines (see Methods). By combining the results of these two pipelines, the final data set consisted of 50,037 viral contigs with an N50 >20 kb. To evaluate whether our sequencing effort sufficiently sampled the viromes, all six WWTP samples were subsampled iteratively to evaluate the saturation dynamics. Rarefaction curves of the number of viral contigs, reached a plateau ~15 Gb of sequencing data for all six samples (Fig. 1a), indicating adequate recovery of prokaryotic viruses in these AS systems at the sequencing depth (30 Gb per sample) in this study.Fig. 1: Compositional variation in viromes among wastewater treatment plants.a Rarefaction curve of each sample. b Rank-abundance curve of each sample. c PCoA analysis of viromes based on the relative abundance of viral genera. d Relative abundance and appearance of dominant viral genera in each WWTP. White color denotes no appearance in this WWTP. Source data are provided as a Source Data file.Full size imageViral contigs were further classified into 8756 viral clusters (VC, equivalent to viral genera) using vConTACT2 by calculating the gene-content-based distance between viral contigs (~40% proteome similarity)11, and each VC was assigned an ID for identification (mean length = 15.2 kb, mean genera size = 3). Compared with the current number of viral sequences from AS systems, our sequencing data increase the AS virome database (N = 2103 in the IMG/VR database v.2.0)10 by 12-fold at the genus level and by 23-fold at the species-level (95% identity, 80% coverage). Comparison with NCBI RefSeq viral genome database showed that across the six AS systems, only 0.4–1.6% of total viral contigs (coverage percentage) could be assigned to a known viral family. Similar to previously described viral metagenomes from the soil, freshwater, and marine system7,12,13, this limited annotation highlights substantial uncharacterized viral diversity in AS communities. Among these recognizable viruses, members of the family Podoviridae (short-tailed phages from the Caudovirales order) were the most prevalent, comprising on average 41.3% of these viral contigs (coverage percentage) across the six WWTPs.All samples displayed high but variable diversity of viral genera with Shannon’s diversity index H’ ranging from 5.22 to 7.14 and Pielou’s evenness index J’ ranging from 0.71 to 0.86 (Supplementary Data 1). These differences are evident in rank-abundance curves, which show that each sample has different viral frequency patterns (Fig. 1b). Most viruses occurred at low frequency with the relative abundance of individual genera diminishing below 0.1% after counting the top 138 viral genera.Principal coordinate analysis (PCoA) of the Bray–Curtis dissimilarity based on the relative abundance of viral genera suggested that most samples are divergent from each other (Fig. 1c), with only two pairs of AS viromes, ST and STL as well as SK and SWH, displaying higher similarity to each other.The overall variability in the viromes is also reflected in different dominance patterns. Each AS sample yielded a different dominant viral genus, and while these were also abundant in some WWTPs, they were below the detection limit in others (Fig. 1d). Although high relative abundance across all WWTPs indicates linkage to consistently abundant hosts, highly variable occurrence suggests that host populations are also more dynamic.Although the viromes appear overall variable in rank abundance, many viral genera were shared across the WWTPs. Fifty-three viral genera were detected in all samples and were thus considered to be common members of the AS viromes, accounting for 1.7–5.4% of viral contigs (coverage percentage) in each WWTP (Fig. 2). Thirteen of these common viral genera were also present in AS viromes in the IMG/VR database v.2.010. Of the total of 8756 unique viral genera collected across samples, STL and ST contained the largest fraction (5245 and 5149 genera, respectively) and shared most viral genera (N = 2885) with each other, far exceeding the number of all the other shared or unique viral genera (Fig. 2). These two WWTPs also had only 45 and 64 site-specific viral genera, consistent with the pattern in the PCoA virome profile. On the other hand, SWH possessed the most unique viral genera (N = 323), making up 11.0% of the total (Fig. 2). In fact, only relatively few viral genera were found exclusively in one of the WWTPs.Fig. 2: Shared viral genera in each WWTP.The UpSet55 chart shows the total number of viral genera and their sharedness in each WWTP. The bar chart on the top right shows the distribution of predicted hosts for common viral genera in all WWTPs. Shared viral genera between ST and STL were labeled orange and shared viral genera in all WWTPs were labeled red. Source data are provided as a Source Data file.Full size imageOverall, these results suggest that the virome across WWTPs consists of many shared genera. The lack of detection of some viral genera in the AS virome of one WWTP may be primarily due to the biological variation in the grab samples and/or the technical variation. Hence, if such technical and biological variations are taken into account, the virome shared among all AS maybe even more diverse.Viruses infect a broad spectrum of bacteria and archaeaTo examine putative host associations of all 50,037 viral contigs in the six WWTPs, we amassed a database of approximately three million CRISPR-Cas spacers from the NCBI prokaryotic complete genomes and metagenomes database (https://www.ncbi.nlm.nih.gov/assembly/). Host prediction was performed by matching CRISPR-Cas spacers at a sequence identity above 97%, sequence coverage over 90% in length, and mismatches 1000 ORFs in each sample were found in the following categories, L: replication, recombination, and repair, M: cell wall/membrane/envelope biogenesis, and K: transcription, confirming that the main functions sustain viral reproduction and transcription. It is noteworthy that viruses encode on average 541 ORFs (1.4% of annotated viral ORFs) in each sample in category G: carbohydrate transport and metabolism. After removing redundant viral ORFs, most unique ORFs (N = 1610) were classified into the glycoside hydrolases (GH) module24 (Fig. 5b). These GHs may be involved in the digestion of capsules to allow the viral tail to reach its membrane receptor on the host. The high representation of this function may be explained by the prevalence of biofilm formation among AS microbes and has previously also been noted among mangrove sediment viruses25.Fig. 5: Distribution of auxiliary metabolic genes (AMGs) relevant to the carbon cycle and nutrient removal.a Boxplot of the overall gene profile for six WWTPs was summarized both as viral ORF hits and viral ORF relative abundance (ORF hits to each COG function class/total ORFs that have hits to eggNOG database). Data in a are presented as mean values (center) and 25%, 75% percentiles (bounds of box). The minima and maxima represent the range of the data. b Number of unique viral ORFs related to each CAZy function class was shown in a descendant order. Source data are provided as a Source Data file.Full size imagePrevious work has shown that many prokaryotic viruses carry auxiliary metabolic genes (AMGs), which can modulate host energy metabolism to provide an energetic advantage during viral genome and protein synthesis26. Broadly speaking, AMGs refer to all metabolic genes in lytic phages27, i.e., all genes in categories C, E, F, G, H, I, and P. Though only about a quarter of the viral ORFs have annotations in the eggNOG database, our data reveal a large repertoire of potential AMGs in the AS viromes (Fig. 5a). For example, 72 ORFs in total are potentially involved with carbon fixation pathways and 35 of them annotate as photosynthetic carbon fixation pathways in KEGG28. Moreover, 10 viral ORFs belong to CobS genes, which are essential for the biosynthesis of cobalamin. Cobalamin biosynthesis pathway is usually not complete in bacterial genomes29, and these viral encoded CobS genes could possibly assist the host metabolic capability. Seven viral genes encode adenylyl-sulfate kinase (CysC), which could facilitate host’s assimilatory sulfate reduction. By modulating host metabolism during infection, AMGs could alter the specific functions of their hosts in WWTPs and therefore influence carbon cycling and the removal of nutrients.Viromes are shared between WWTPs and the water environmentTo investigate whether the WWTP viral genera also occur in other habitats, we compared all viral sequences recovered here with the IMG/VR database v.2.0, which consists of 735,112 viral contigs predicted from metagenomic data10. Viral sequences from five ecosystems (AS, AD, solid waste, freshwater, marine) were included for comparison. For AS (N = 2103), AD (N = 8580), and solid waste (N = 5760), all viral sequences were subjected to our viral clustering pipeline, whereas for freshwater and marine environments, we each randomly selected 50,037 viral sequences to match the number in our samples (N = 50,037).Results showed that viral genera in our samples were shared among multiple environments. There was considerable overlap between WWTPs and freshwater (N = 402) and marine (N = 172) environments (Fig. 6). Moreover, 200, 273, and 244 viral genera were shared with AS, AD, and solid waste, respectively (Fig. 6). This represents a higher number than with marine viromes, and if normalized against the dataset size, there is also a larger fraction of connections than with freshwater viromes. When hosts were predicted for these shared viral genera, Proteobacteria was the most shared host phylum, followed by Firmicutes, Actinobacteria, Bacteroidetes, and Cyanobacteria (Supplementary Data 2). At the genus level, Bacillus was the most abundant between our samples and marine, AD, and AS environments, while Streptomyces displayed a higher prevalence between our samples and freshwater and solid waste environments (Supplementary Data 3). Although it is difficult to identify the source and sink dynamics, AS and AD are typical processes in WWTPs, and solid waste viral sequences mainly stem from compost and leachate microbial communities. The considerable sharing of viromes between WWTPs and marine water may be caused by Hong Kong’s extensive application of marine water for toilet flushing, causing the influent sewage of WWTPs to contain a sizable amount of marine water. These data thus suggest that viruses are extensively shared and that the same viral genera may manipulate microbial communities in these different environments.Fig. 6: Connections between viral genera in AS samples and in five ecosystems from IMG/VR database.Pairwise connections were shown in a Circos56 plot between samples and different ecosystems, including AS, AD, solid waste, freshwater, and marine water. Source data are provided as a Source Data file.Full size imageHi-C validation of virus–host interactions in AS systemHigh throughput chromosome conformation capture (Hi-C) method was used to validate the virus–host connections predicted by our CRISPR-based methods using an additional sample in December 2020 at ST WWTP, by referring to viral contigs and host genome bins obtained from direct sequencing using Illumina and Nanopore metagenomic sequencing (Fig. 7).Fig. 7: General workflow to validate the precision of CRISPR-based methods in the present study.For Illumina data, 91% precision was observed. For Nanopore data, 94% precision was observed. Source data are provided as a Source Data file.Full size imageAs for Illumina metagenomic sequencing, 4578 viral contigs were identified and 1695 of them were deconvoluted in Hi-C data to have virus–host interactions with 197 host bins (Supplementary Data 9). To compare the Hi-C results with the CRISPR-based methods, 21 viruses were predicted by BLASTn-short to link with spacers in eight bins (Supplementary Data 10).As for hybrid assembly using both Nanopore and Illumina reads, 2593 viral contigs were identified and 989 of them were deconvoluted in Hi-C data to have virus–host interactions with 144 host bins (Supplementary Data 11). To compare the Hi-C results with the CRISPR-based methods, 28 viruses were predicted by BLASTn-short to link with spacers in 10 bins (Supplementary Data 12).Results show that CRISPR-based results have very high accuracy. For Illumina data, of the 21 virus–host connections predicted using CRISPR spacers, 11 are simultaneously found in Hi-C data and 10 are not detected in Hi-C data. Of 11 detected connections, only 1 is different in Hi-C data and 10 are the same (91% precision) (Supplementary Data 13). Also for the Nanopore/Illumina hybrid data, of the 28 virus–host connections predicted using CRISPR spacers, 16 are simultaneously found in Hi-C data and 12 are not detected in Hi-C data. Of 16 detected connections, only 1 is different in Hi-C data, 15 are the same (94% precision) (Supplementary Data 14).It should be noticed that some of the predicted CRISPR-based virus–host interactions are undetected in Hi-C data. CRISPR spacers represent a collection of memories regarding past virus invasions, whereas Hi-C data provide a snapshot of ongoing virus–host interactions. Also, Hi-C crosslinking may not be 100% efficient and might miss some of the virus–host interactions. More

1.Backhaus E, Berg S, Andersson R, Ockborn G, Malmström P, Dahl M, et al. Epidemiology of invasive pneumococcal infections: manifestations, incidence and case fatality rate correlated to age, gender and risk factors. BMC Infect Dis. 2016;16:367.PubMed 
PubMed Central 
Article 

Google Scholar 
2.Bogaert D, de Groot R, Hermans P. Streptococcus pneumoniae colonisation: the key to pneumococcal disease. Lancet Infect Dis. 2004;4:144–54.PubMed 
Article 
CAS 

Google Scholar 
3.Wahl B, O’Brien KL, Greenbaum A, Majumder A, Liu L, Chu Y, et al. Burden of Streptococcus pneumoniae and Haemophilus influenzae type b disease in children in the era of conjugate vaccines: global, regional, and national estimates for 2000-15. Lancet Glob Health. 2018;6:e744–57.PubMed 
PubMed Central 
Article 

Google Scholar 
4.McAllister DA, Liu L, Shi T, Chu Y, Reed C, Burrows J, et al. Global, regional, and national estimates of pneumonia morbidity and mortality in children younger than 5 years between 2000 and 2015: a systematic analysis. Lancet Glob Health. 2019;7:e47–57.PubMed 
Article 

Google Scholar 
5.Abdullahi O, Karani A, Tigoi CC, Mugo D, Kungu S, Wanjiru E, et al. The prevalence and risk factors for pneumococcal colonization of the nasopharynx among children in Kilifi District, Kenya. PloS ONE. 2012;7:e30787.PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
6.Kelly MS, Surette MG, Smieja M, Rossi L, Luinstra K, Steenhoff AP, et al. Pneumococcal colonization and the nasopharyngeal microbiota of children in Botswana. Pediatr Infect Dis J. 2018;37:1176–83.PubMed 
PubMed Central 
Article 

Google Scholar 
7.Huang SS, Hinrichsen VL, Stevenson AE, Rifas-Shiman SL, Kleinman K, Pelton SI, et al. Continued impact of pneumococcal conjugate vaccine on carriage in young children. Pediatrics 2009;124:e1–e11.PubMed 
Article 

Google Scholar 
8.van Hoek AJ, Sheppard CL, Andrews NJ, Waight PA, Slack MP, Harrison TG, et al. Pneumococcal carriage in children and adults two years after introduction of the thirteen valent pneumococcal conjugate vaccine in England. Vaccine.2014;32:4349–55.PubMed 
Article 

Google Scholar 
9.Almeida ST, Nunes S, Paulo ACS, Valadares I, Martins S, Breia F, et al. Low prevalence of pneumococcal carriage and high serotype and genotype diversity among adults over 60 years of age living in Portugal. PloS ONE 2014;9:e90974.PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
10.Kaplan SL, Mason EO, Wald ER, Schutze GE, Bradley JS, Tan TQ, et al. Decrease of invasive pneumococcal infections in children among 8 children’s hospitals in the United States after the introduction of the 7-valent pneumococcal conjugate vaccine. Pediatrics.2004;113:443–9.PubMed 
Article 

Google Scholar 
11.Hammitt LL, Etyang AO, Morpeth SC, Ojal J, Mutuku A, Mturi N, et al. Effect of ten-valent pneumococcal conjugate vaccine on invasive pneumococcal disease and nasopharyngeal carriage in Kenya: a longitudinal surveillance study. Lancet.2019;393:2146–54.PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
12.Cutts F, Zaman S, Enwere GY, Jaffar S, Levine O, Okoko J, et al. Efficacy of nine-valent pneumococcal conjugate vaccine against pneumonia and invasive pneumococcal disease in The Gambia: randomised, double-blind, placebo-controlled trial. Lancet.2005;365:1139–46.PubMed 
Article 
CAS 

Google Scholar 
13.Congdon M, Hong H, Young RR, Cunningham CK, Enane LA, Arscott-Mills T, et al. Effect of Haemophilus influenzae type b and 13-valent pneumococcal conjugate vaccines on childhood pneumonia hospitalizations and deaths in Botswana. Clin Infect Dis. 2020; e-pub ahead of print 8 July 2020; https://doi.org/10.1093/cid/ciaa919.14.Eskola J, Kilpi T, Palmu A, Jokinen J, Eerola M, Haapakoski J, et al. Efficacy of a pneumococcal conjugate vaccine against acute otitis media. N. Engl J Med. 2001;344:403–9.PubMed 
Article 
CAS 

Google Scholar 
15.Pelton SI, Huot H, Finkelstein JA, Bishop CJ, Hsu KK, Kellenberg J, et al. Emergence of 19A as virulent and multidrug resistant Pneumococcus in Massachusetts following universal immunization of infants with pneumococcal conjugate vaccine. Pediatr Infect Dis J. 2007;26:468–72.PubMed 
Article 

Google Scholar 
16.Pichichero ME, Casey JR. Emergence of a multiresistant serotype 19A pneumococcal strain not included in the 7-valent conjugate vaccine as an otopathogen in children. JAMA.2007;298:1772–8.PubMed 
Article 
CAS 

Google Scholar 
17.Neves FP, Cardoso NT, Snyder RE, Marlow MA, Cardoso CA, Teixeira LM, et al. Pneumococcal carriage among children after four years of routine 10-valent pneumococcal conjugate vaccine use in Brazil: the emergence of multidrug resistant serotype 6C. Vaccine.2017;35:2794–800.PubMed 
Article 

Google Scholar 
18.Bradshaw JL, McDaniel LS. Selective pressure: rise of the nonencapsulated pneumococcus. PLoS Pathog. 2019;15:e1007911.PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
19.Ladhani SN, Collins S, Djennad A, Sheppard CL, Borrow R, Fry NK, et al. Rapid increase in non-vaccine serotypes causing invasive pneumococcal disease in England and Wales, 2000–17: a prospective national observational cohort study. Lancet Infect Dis. 2018;18:441–51.PubMed 
Article 

Google Scholar 
20.Ouldali N, Levy C, Varon E, Bonacorsi S, Béchet S, Cohen R, et al. Incidence of paediatric pneumococcal meningitis and emergence of new serotypes: a time-series analysis of a 16-year French national survey. Lancet Infect Dis. 2018;18:983–91.PubMed 
Article 

Google Scholar 
21.Zaneveld J, Turnbaugh PJ, Lozupone C, Ley RE, Hamady M, Gordon JI, et al. Host-bacterial coevolution and the search for new drug targets. Curr Opin Chem Biol. 2008;12:109–14.PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
22.de Steenhuijsen Piters WA, Binkowska J, Bogaert D. Early life microbiota and respiratory tract infections. Cell Host Microbe. 2020;28:223–32.PubMed 
Article 
CAS 

Google Scholar 
23.Bogaert D, van Belkum A, Sluijter M, Luijendijk A, de Groot R, Rümke H, et al. Colonisation by Streptococcus pneumoniae and Staphylococcus aureus in healthy children. Lancet.2004;363:1871–2.PubMed 
Article 
CAS 

Google Scholar 
24.Pettigrew MM, Gent JF, Revai K, Patel JA, Chonmaitree T. Microbial interactions during upper respiratory tract infections. Emerg Infect Dis. 2008;14:1584.PubMed 
PubMed Central 
Article 

Google Scholar 
25.Shiri T, Nunes MC, Adrian PV, Van Niekerk N, Klugman KP, Madhi SA. Interrelationship of Streptococcus pneumoniae, Haemophilus influenzae and Staphylococcus aureus colonization within and between pneumococcal-vaccine naïve mother–child dyads. BMC Infect Dis. 2013;13:483.PubMed 
PubMed Central 
Article 

Google Scholar 
26.Jacoby P, Watson K, Bowman J, Taylor A, Riley TV, Smith DW, et al. Modelling the co-occurrence of Streptococcus pneumoniae with other bacterial and viral pathogens in the upper respiratory tract. Vaccine.2007;25:2458–64.PubMed 
Article 

Google Scholar 
27.Nzenze S, Shiri T, Nunes M, Klugman K, Kahn K, Twine R, et al. Temporal association of infant immunisation with pneumococcal conjugate vaccine on the ecology of Streptococcus pneumoniae, Haemophilus influenzae and Staphylococcus aureus nasopharyngeal colonisation in a rural South African community. Vaccine.2014;32:5520–30.PubMed 
Article 
CAS 

Google Scholar 
28.Faden H, Stanievich J, Brodsky L, Bernstein J, Ogra PL. Changes in nasopharyngeal flora during otitis media of childhood. Pediatr Infect Dis J. 1990;9:623–6.PubMed 
CAS 

Google Scholar 
29.Shekhar S, Khan R, Schenck K, Petersen FC. Intranasal Immunization with the commensal Streptococcus mitis confers protective immunity against pneumococcal lung infection. Appl Environ Microbiol. 2019;85:e02235–18.PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
30.Cangemi de Gutierrez R, Santos V, Nader-Macias ME. Protective effect of intranasally inoculated Lactobacillus fermentum against Streptococcus pneumoniae challenge on the mouse respiratory tract. FEMS Immunol Med Microbiol. 2001;31:187–95.PubMed 
Article 
CAS 

Google Scholar 
31.Wong SS, Quan Toh Z, Dunne EM, Mulholland EK, Tang ML, Robins-Browne RM, et al. Inhibition of Streptococcus pneumoniae adherence to human epithelial cells in vitro by the probiotic Lactobacillus rhamnosus GG. BMC Res Notes. 2013;6:135.PubMed 
PubMed Central 
Article 

Google Scholar 
32.Laufer AS, Metlay JP, Gent JF, Fennie KP, Kong Y, Pettigrew MM. Microbial communities of the upper respiratory tract and otitis media in children. mBio.2011;2:e00245–10.PubMed 
PubMed Central 
Article 

Google Scholar 
33.Bomar L, Brugger SD, Yost BH, Davies SS, Lemon KP. Corynebacterium accolens releases antipneumococcal free fatty acids from human nostril and skin surface triacylglycerols. mBio.2016;7:e01725–15.PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
34.Cope EK, Goldstein-Daruech N, Kofonow JM, Christensen L, McDermott B, Monroy F, et al. Regulation of virulence gene expression resulting from Streptococcus pneumoniae and nontypeable Haemophilus influenzae interactions in chronic disease. PloS ONE. 2011;6:e28523.PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
35.Lysenko ES, Ratner AJ, Nelson AL, Weiser JN. The role of innate immune responses in the outcome of interspecies competition for colonization of mucosal surfaces. PloS Pathog. 2005;1:e1.PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
36.Weimer KE, Juneau RA, Murrah KA, Pang B, Armbruster CE, Richardson SH, et al. Divergent mechanisms for passive pneumococcal resistance to β-lactam antibiotics in the presence of Haemophilus influenzae. J Infect Dis. 2011;203:549–55.PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
37.Tikhomirova A, Kidd SP. Haemophilus influenzae and Streptococcus pneumoniae: living together in a biofilm. Pathog Dis. 2013;69:114–26.PubMed 
Article 
CAS 

Google Scholar 
38.Brugger SD, Eslami SM, Pettigrew MM, Escapa IF, Henke MT, Kong Y, et al. Dolosigranulum pigrum cooperation and competition in human nasal microbiota. mSphere. 2020;5.39.Teo SM, Mok D, Pham K, Kusel M, Serralha M, Troy N, et al. The infant nasopharyngeal microbiome impacts severity of lower respiratory infection and risk of asthma development. Cell Host Microbe. 2015;17:704–15.PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
40.Mika M, Mack I, Korten I, Qi W, Aebi S, Frey U, et al. Dynamics of the nasal microbiota in infancy: a prospective cohort study. J Allergy Clin Immunol. 2015;135:905–12.PubMed 
Article 

Google Scholar 
41.Biesbroek G, Tsivtsivadze E, Sanders EA, Montijn R, Veenhoven RH, Keijser BJ, et al. Early respiratory microbiota composition determines bacterial succession patterns and respiratory health in children. Am J Respir Crit Care Med. 2014;190:1283–92.PubMed 
Article 
PubMed Central 

Google Scholar 
42.Bosch AA, Levin E, van Houten MA, Hasrat R, Kalkman G, Biesbroek G, et al. Development of upper respiratory tract microbiota in infancy is affected by mode of delivery. EBioMedicine.2016;9:336–45.PubMed 
PubMed Central 
Article 

Google Scholar 
43.Dominguez-Bello MG, Costello EK, Contreras M, Magris M, Hidalgo G, Fierer N, et al. Delivery mode shapes the acquisition and structure of the initial microbiota across multiple body habitats in newborns. Proc Natl Acad Sci USA. 2010;107:11971–5.PubMed 
PubMed Central 
Article 

Google Scholar 
44.Biesbroek G, Bosch AA, Wang X, Keijser BJ, Veenhoven RH, Sanders EA, et al. The impact of breastfeeding on nasopharyngeal microbial communities in infants. Am J Respir Crit Care Med. 2014;190:298–308.PubMed 
Article 

Google Scholar 
45.Bogaert D, Keijser B, Huse S, Rossen J, Veenhoven R, Van Gils E, et al. Variability and diversity of nasopharyngeal microbiota in children: a metagenomic analysis. PloS ONE. 2011;6:e17035.PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
46.Bruce N, Perez-Padilla R, Albalak R. Indoor air pollution in developing countries: a major environmental and public health challenge. Bull World Health Organ. 2000;78:1078–92.PubMed 
PubMed Central 
CAS 

Google Scholar 
47.Pelissari DM, Diaz-Quijano FA. Household crowding as a potential mediator of socioeconomic determinants of tuberculosis incidence in Brazil. PloS ONE. 2017;12:e0176116.PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
48.Mannucci PM, Franchini M. Health effects of ambient air pollution in developing countries. Int J Environ Res Public Health. 2017;14:1048.PubMed Central 
Article 
CAS 
PubMed 

Google Scholar 
49.Yatsunenko T, Rey FE, Manary MJ, Trehan I, Dominguez-Bello MG, Contreras M, et al. Human gut microbiome viewed across age and geography. Nature.2012;486:222–7.PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
50.Ferretti P, Pasolli E, Tett A, Asnicar F, Gorfer V, Fedi S, et al. Mother-to-infant microbial transmission from different body sites shapes the developing infant gut microbiome. Cell Host Microbe. 2018;24:133–45.PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
51.Ravel J, Gajer P, Abdo Z, Schneider GM, Koenig SS, McCulle SL, et al. Vaginal microbiome of reproductive-age women. Proc Natl Acad Sci USA. 2011;108:4680–7.PubMed 
Article 

Google Scholar 
52.Mallick H, Rahnavard A, McIver LJ, Ma S, Zhang Y, Nguyen LH, et al. Multivariable association discovery in population-scale meta-omics studies. bioRxiv. 2021. https://doi.org/10.1101/2021.01.20.427420.53.Hojsak I, Snovak N, Abdović S, Szajewska H, Mišak Z, Kolaček S. Lactobacillus GG in the prevention of gastrointestinal and respiratory tract infections in children who attend day care centers: a randomized, double-blind, placebo-controlled trial. Clin Nutr. 2010;29:312–6.PubMed 
Article 

Google Scholar 
54.Gluck U, Gebbers JO. Ingested probiotics reduce nasal colonization with pathogenic bacteria (Staphylococcus aureus, Streptococcus pneumoniae, and beta-hemolytic streptococci). Am J Clin Nutr. 2003;77:517–20.PubMed 
Article 
CAS 

Google Scholar 
55.Feleszko W, Jaworska J, Rha RD, Steinhausen S, Avagyan A, Jaudszus A, et al. Probiotic-induced suppression of allergic sensitization and airway inflammation is associated with an increase of T regulatory-dependent mechanisms in a murine model of asthma. Clin Exp Allergy. 2007;37:498–505.PubMed 
Article 
CAS 

Google Scholar 
56.Nhan T-X, Parienti J-J, Badiou G, Leclercq R, Cattoir V. Microbiological investigation and clinical significance of Corynebacterium spp. in respiratory specimens. Diagn Microbiol Infect Dis. 2012;74:236–41.PubMed 
Article 

Google Scholar 
57.Díez-Aguilar M, Ruiz-Garbajosa P, Fernández-Olmos A, Guisado P, Del Campo R, Quereda C, et al. Non-diphtheriae Corynebacterium species: an emerging respiratory pathogen. Eur J Clin Microbiol Infect Dis. 2013;32:769–72.PubMed 
Article 
CAS 

Google Scholar 
58.Teutsch B, Berger A, Marosevic D, Schönberger K, Lâm T-T, Hubert K, et al. Corynebacterium species nasopharyngeal carriage in asymptomatic individuals aged ≥ 65 years in Germany. Infection.2017;45:607–11.PubMed 
Article 
CAS 

Google Scholar 
59.Camacho C, Coulouris G, Avagyan V, Ma N, Papadopoulos J, Bealer K, et al. BLAST+: architecture and applications. BMC Bioinforma. 2009;10:421.Article 
CAS 

Google Scholar 
60.Turner P, Turner C, Green N, Ashton L, Lwe E, Jankhot A, et al. Serum antibody responses to pneumococcal colonization in the first 2 years of life: results from an SE Asian longitudinal cohort study. Clin Microbiol Infect. 2013;19:e551–8.PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
61.Numminen E, Chewapreecha C, Turner C, Goldblatt D, Nosten F, Bentley SD, et al. Climate induces seasonality in pneumococcal transmission. Sci Rep. 2015;5:11344.PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
62.Kelly MS, Smieja M, Luinstra K, Wirth KE, Goldfarb DM, Steenhoff AP, et al. Association of respiratory viruses with outcomes of severe childhood pneumonia in Botswana. PloS ONE. 2015;10:e0126593.PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
63.le Roux DM, Myer L, Nicol MP, Zar HJ. Incidence and severity of childhood pneumonia in the first year of life in a South African birth cohort: the Drakenstein Child Health Study. Lancet Glob Health. 2015;3:e95–103.PubMed 
Article 

Google Scholar 
64.von Mollendorf C, von Gottberg A, Tempia S, Meiring S, de Gouveia L, Quan V, et al. Increased risk and mortality of invasive pneumococcal disease in HIV-exposed-uninfected infants More

1.Pakhomov, E. A., Froneman, P. W. & Perissinotto, R. Salp/krill interactions in the Southern Ocean: Spatial segregation and implications for the carbon flux. Deep Sea Res. II 49, 1881–1907 (2002).CAS 
Article 

Google Scholar 
2.Steinberg, D. K. et al. Long-term (1993–2013) changes in macrozooplankton off the Western Antarctic Peninsula. Deep Sea Res. I 101, 54–70 (2015).Article 

Google Scholar 
3.Whitehouse, M. J. et al. Role of krill versus bottom-up factors in controlling phytoplankton biomass in the northern Antarctic waters of South Georgia. Mar. Ecol. Prog. Ser. 393, 69–82 (2009).CAS 
Article 

Google Scholar 
4.Tarling, G. A. & Fielding, S. in Biology and Ecology of Antarctic krill (ed Siegel, V.) 279–319 (Springer International Publishing, 2016).5.Henschke, N., Everett, J. D., Richardson, A. J. & Suthers, I. M. Rethinking the role of salps in the ocean. Trends Ecol. Evol. 31, 720–733 (2016).PubMed 
Article 

Google Scholar 
6.Cavan, E. L. et al. The importance of Antarctic krill in biogeochemical cycles. Nat. Commun. 10, 4742 (2019).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
7.Belcher, A. et al. Krill faecal pellets drive hidden pulses of particulate organic carbon in the marginal ice zone. Nat. Commun. 10, 889 (2019).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
8.Phillips, B., Kremer, P. & Madin, L. P. Defecation by Salpa thompsoni and its contribution to vertical flux in the Southern Ocean. Mar. Biol. 156, 455–467 (2009).Article 

Google Scholar 
9.Siegel, V. & Watkins, J. L. in Biology and Ecology of Antarctic krill (ed Siegel, V.) 21–100 (Springer International Publishing, 2016).10.Atkinson, A. et al. Krill (Euphausia superba) distribution contracts southward during rapid regional warming. Nat. Clim. Change 9, 142–147 (2019).Article 

Google Scholar 
11.Vaughan, D. G. et al. Recent rapid regional climate warming on the Antarctic Peninsula. Clim. Change 60, 243–274 (2003).Article 

Google Scholar 
12.Ducklow, H. W. et al. Marine pelagic ecosystems: the West Antarctic Peninsula. Philos. Trans. R. Soc. B. 362, 67–94 (2007).Article 

Google Scholar 
13.Montes-Hugo, M. et al. Recent changes in phytoplankton communities associated with rapid regional climate change along the Western Antarctic Peninsula. Science 323, 1470–1473 (2009).CAS 
PubMed 
Article 

Google Scholar 
14.Clarke, A. et al. Climate change and the marine ecosystem of the western Antarctic Peninsula. Philos. T. R. Soc., B 362, 149–166 (2007).Article 

Google Scholar 
15.Atkinson, A., Siegel, V., Pakhomov, E. & Rothery, P. Long-term decline in krill stock and increase in salps within the Southern Ocean. Nature 432, 100–103 (2004).CAS 
PubMed 
Article 

Google Scholar 
16.Loeb, V. et al. Effects of sea-ice extent and krill or salp dominance on the Antarctic food web. Nature 387, 897–900 (1997).CAS 
Article 

Google Scholar 
17.Flores, H. et al. Impact of climate change on Antarctic krill. Mar. Ecol. Prog. Ser. 458, 1–19 (2012).Article 

Google Scholar 
18.Cox, M. J. et al. No evidence for a decline in the density of Antarctic krill Euphausia superba Dana, 1850, in the Southwest Atlantic sector between 1976 and 2016. J. Crust. Biol. 38, 656–661 (2018).19.Foxton, P. The Distribution and Life-history of Salpa thompsoni Foxton with observations on a Related Species, Salpa gerlachei Foxton (The University Press, 1966).20.Bernard, K. S., Steinberg, D. K. & Schofield, O. M. E. Summertime grazing impact of the dominant macrozooplankton off the Western Antarctic Peninsula. Deep Sea Res. I 62, 111–122 (2012).Article 

Google Scholar 
21.Condon, R. H. et al. Jellyfish blooms result in a major microbial respiratory sink of carbon in marine systems. Proc. Natl Acad. Sci. 108, 10225–10230 (2011).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
22.Meyer, M. A. & Elsayed, S. Z. Grazing of Euphausia superba Dana on natural phytoplankton populations. Polar Biol. 1, 193–197 (1983).Article 

Google Scholar 
23.Haberman, K. L., Ross, R. M. & Quetin, L. B. Diet of the Antarctic krill (Euphausia superba Dana): II. Selective grazing in mixed phytoplankton assemblages. J. Exp. Mar. Biol. Ecol. 283, 97–113 (2003).Article 

Google Scholar 
24.Schmidt, K. & Atkinson, A. in Biology and Ecology of Antarctic krill (ed Siegel, V.) 175–224 (Springer International Publishing, 2016).25.Andersen, V. in The Biology of Pelagic Tunicates (ed Bone, Q.) 125–137 (Oxford University Press, 1998).26.Mitra, A. et al. Bridging the gap between marine biogeochemical and fisheries sciences; configuring the zooplankton link. Prog. Oceanogr. 129, 176–199 (2014).Article 

Google Scholar 
27.Sailley, S. F., Polimene, L., Mitra, A., Atkinson, A. & Allen, J. I. Impact of zooplankton food selectivity on plankton dynamics and nutrient cycling. J. Plankton Res. 37, 519–529 (2015).CAS 
Article 

Google Scholar 
28.Hamner, W. M., Hamner, P. P., Strand, S. W. & Gilmer, R. W. Behavior of Antarctic krill, Euphausia superba: Chemoreception, feeding, schooling, and molting. Science 220, 433–435 (1983).CAS 
PubMed 
Article 

Google Scholar 
29.DeMott, W. R. in Behavioural Mechanisms of Food Selection (ed Hughes, R. N.) 569–594 (Springer, 1990).30.Le Fèvre, J., Legendre, L. & Rivkin, R. B. Fluxes of biogenic carbon in the Southern Ocean: Roles of large microphagous zooplankton. J. Mar. Syst. 17, 325–345 (1998).Article 

Google Scholar 
31.Moline, M. A., Claustre, H., Frazer, T. K., Schofield, O. & Vernet, M. Alteration of the food web along the Antarctic Peninsula in response to a regional warming trend. Glob. Change Biol. 10, 1973–1980 (2004).Article 

Google Scholar 
32.Frischer, M. E. et al. Selective feeding and linkages to the microbial food web by the doliolid Dolioletta gegenbauri. Limnol. Oceanogr. 66, 1993–2010 (2021).Article 

Google Scholar 
33.Dadon-Pilosof, A., Lombard, F., Genin, A., Sutherland, K. R. & Yahel, G. Prey taxonomy rather than size determines salp diets. Limnol. Oceanogr. 64, 1996–2010 (2019).Article 

Google Scholar 
34.Metfies, K., Nicolaus, A., von Harbou, L., Bathmann, U. & Peeken, I. Molecular analyses of gut contents: elucidating the feeding of co-occurring salps in the Lazarev Sea from a different perspective. Antarct. Sci. 26, 5545–5553 (2014).Article 

Google Scholar 
35.Cleary, A. C., Durbin, E. G. & Casas, M. C. Feeding by Antarctic krill Euphausia superba in the West Antarctic Peninsula: differences between fjords and open waters. Mar. Ecol. Prog. Ser. 595, 39–54 (2018).CAS 
Article 

Google Scholar 
36.Pompanon, F. et al. Who is eating what: Diet assessment using next generation sequencing. Mol. Ecol. 21, 1931–1950 (2012).CAS 
PubMed 
Article 

Google Scholar 
37.Passmore, A. J. et al. DNA as a dietary biomarker in Antarctic krill, Euphausia superba. Mar. Biotechnol. 8, 686–696 (2006).CAS 
Article 

Google Scholar 
38.von Harbou, L. et al. Salps in the Lazarev Sea, Southern Ocean: I. Feeding dynamics. Mar. Biol. 158, 2009–2026 (2011).Article 

Google Scholar 
39.Vernet, M. et al. Primary production throughout austral fall, during a time of decreasing daylength in the western Antarctic Peninsula. Mar. Ecol. Prog. Ser. 452, 45–61 (2012).CAS 
Article 

Google Scholar 
40.Moreau, S. et al. Variability of the microbial community in the western Antarctic Peninsula from late fall to spring during a low ice cover year. Polar Biol. 33, 1599–1614 (2010).Article 

Google Scholar 
41.Selz, V. et al. Distribution of Phaeocystis antarctica-dominated sea ice algal communities and their potential to seed phytoplankton across the western Antarctic Peninsula in spring. Mar. Ecol. Prog. Ser. 586, 91–112 (2018).CAS 
Article 

Google Scholar 
42.Nichols, D. S., Nichols, P. D. & Sullivan, C. W. Fatty acid, sterol and hydrocarbon composition of Antarctic sea ice diatom communities during the spring bloom in McMurdo Sound. Antarct. Sci. 5, 271–278 (1993).Article 

Google Scholar 
43.Fahl, K. & Kattner, G. Lipid Content and fatty acid composition of algal communities in sea-ice and water from the Weddell Sea (Antarctica). Polar Biol. 13, 405–409 (1993).Article 

Google Scholar 
44.Boyd, C. M., Heyraud, M. & Boyd, C. N. Feeding of the Antarctic krill Euphausia superba. J. Crust. Biol. 4, 123–141 (1984).Article 

Google Scholar 
45.Bone, Q., Carré, C. & Chang, P. Tunicate feeding filters. J. Mar. Biol. Assoc. U. K. 83, 907–919 (2003).Article 

Google Scholar 
46.Nelson, M. M., Phleger, C. F., Mooney, B. D. & Nichols, P. D. Lipids of gelatinous Antarctic zooplankton: Cnidaria and Ctenophora. Lipids 35, 551–559 (2000).CAS 
PubMed 
Article 

Google Scholar 
47.Huntley, M. E., Sykes, P. F. & Marin, V. Biometry and trophodynamics of Salpa thompsoni Foxton (Tunicata: Thaliacea) near the Antarctic Peninsula in austral summer, 1983–1984. Polar Biol. 10, 59–70 (1989).Article 

Google Scholar 
48.Hopkins, T. L. Food web of an Antarctic midwater ecosystem. Mar. Biol. 89, 197–212 (1985).Article 

Google Scholar 
49.Paffenhöfer, G. A. & Köster, M. Digestion of diatoms by planktonic copepods and doliolids. Mar. Ecol. Prog. Ser. 297, 303–310 (2005).Article 

Google Scholar 
50.von Harbou, L. Trophodynamics of Salps in the Atlantic Southern Ocean. PhD thesis, University of Bremen (2009).51.Hargraves, P. E. The ebridian flagellates Ebria and Hermesinum. Plankton Biol. Ecol. 49, 9–16 (2002).
Google Scholar 
52.Cavan, E. L. et al. Attenuation of particulate organic carbon flux in the Scotia Sea, Southern Ocean, is controlled by zooplankton fecal pellets. Geophys. Res. Lett. 42, 821–830 (2015).CAS 
Article 

Google Scholar 
53.Smith, K. L. Jr. et al. Large salp bloom export from the upper ocean and benthic community response in the abyssal northeast Pacific: day to week resolution. Limnol. Oceanogr. 59, 745–757 (2014).CAS 
Article 

Google Scholar 
54.Cadée, G. C., González, H. & Schnack-Schiel, S. B. Krill diet affects faecal string settling. Polar Biol. 12, 75–80 (1992).
Google Scholar 
55.Ploug, H., Iversen, M. H., Koski, M. & Buitenhuis, E. T. Production, oxygen respiration rates, and sinking velocity of copepod fecal pellets: Direct measurements of ballasting by opal and calcite. Limnol. Oceanogr. 53, 469–476 (2008).CAS 
Article 

Google Scholar 
56.Atkinson, A., Schmidt, K., Fielding, S., Kawaguchi, S. & Geissler, P. A. Variable food absorption by Antarctic krill: Relationships between diet, egestion rate and the composition and sinking rates of their fecal pellets. Deep Sea Res. II 59-60, 147–158 (2012).CAS 
Article 

Google Scholar 
57.Schmidt, K., Atkinson, A., Pond, D. W. & Ireland, L. C. Feeding and overwintering of Antarctic krill across its major habitats: The role of sea ice cover, water depth, and phytoplankton abundance. Limnol. Oceanogr. 59, 17–36 (2014).Article 

Google Scholar 
58.Cripps, G. C., Watkins, J. L., Hill, H. J. & Atkinson, A. Fatty acid content of Antarctic krill Euphausia superba at South Georgia related to regional populations and variations in diet. Mar. Ecol. Prog. Ser. 181, 177–188 (1999).CAS 
Article 

Google Scholar 
59.Schmidt, K., Atkinson, A., Petzke, K.-J., Voss, M. & Pond, D. W. Protozoans as a food source for Antarctic krill, Euphausia superba: Complementary insights from stomach content, fatty acids, and stable isotopes. Limnol. Oceanogr. 51, 2409–2427 (2006).CAS 
Article 

Google Scholar 
60.Hagen, W., Van Vleet, E. S. & Kattner, G. Seasonal lipid storage as overwintering strategy of Antarctic krill. Mar. Ecol. Prog. Ser. 134, 85–89 (1996).CAS 
Article 

Google Scholar 
61.Kawaguchi, S. & Takahashi, Y. Antarctic krill (Euphausia superba Dana) eat salps. Polar Biol. 16, 479–481 (1996).
Google Scholar 
62.Clarke, L. J., Bestley, S., Bissett, A. & Deagle, B. E. A globally distributed Syndiniales parasite dominates the Southern Ocean micro-eukaryote community near the sea-ice edge. ISME J. 13, 734–737 (2019).CAS 
PubMed 
Article 

Google Scholar 
63.Coats, D. W. & Park, M. G. Parasitism of photosynthetic dinoflagellates by three strains of Amoebophrya (Dinophyta): Parasite survival, infectivity, generation time, and host specificity. J. Phycol. 38, 520–528 (2002).Article 

Google Scholar 
64.Sutherland, K. R., Madin, L. P. & Stocker, R. Filtration of submicrometer particles by pelagic tunicates. Proc. Natl Acad. Sci. 107, 15129–15134 (2010).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
65.Gómez-Gutiérrez, J. & Morales-Avila, J. R. in Biology and Ecology of Antarctic krill (ed Siegel, V.) 351–387 (Springer International Publishing, 2006).66.Cleary, A. C., Casas, M. C., Durbin, E. G. & Gómez-Gutiérrez, J. Parasites in Antarctic krill guts inferred from DNA sequences. Antarct. Sci. 31, 16–22 (2019).Article 

Google Scholar 
67.Zamora-Terol, S., Novotny, A. & Winder, M. Molecular evidence of host-parasite interactions between zooplankton and Syndiniales. Aquat. Ecol. 55, 125–134 (2021).CAS 
Article 

Google Scholar 
68.Kawaguchi, S., Ichii, T. & Naganobu, M. Do krill and salps compete? Contrary evidence from the krill fisheries. CCAMLR Sci. 5, 205–216 (1998).
Google Scholar 
69.Fadeev, E. et al. Microbial communities in the east and west Fram Strait during sea ice melting season. Front. Mar. Sci. 5, 429 (2018).Article 

Google Scholar 
70.Vestheim, H. & Jarman, S. N. Blocking primers to enhance PCR amplification of rare sequences in mixed samples – a case study on prey DNA in Antarctic krill stomachs. Front. Zool. 5, 12 (2008).PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
71.Ludwig, W. et al. ARB: a software environment for sequence data. Nucleic Acids Res. 32, 1363–1371 (2004).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
72.R Foundation for Statistical Computing. R: A Language and Environment for Statistical Computing (R Foundation for Statistical Computing, 2019).73.Callahan, B. J. et al. DADA2: High-resolution sample inference from Illumina amplicon data. Nat. Methods 13, 581–583 (2016).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
74.Callahan, B. DADA2 Pipeline Tutorial (1.16), available online: https://benjjneb.github.io/dada2/tutorial.html. Accessed: 3 Feb 2020.75.Martin, M. Cutadapt removes adapter sequences from high-throughput sequencing reads. EMBnet. J. 17, 3 (2011).Article 

Google Scholar 
76.Guillou, L. et al. The protist ribosomal reference database (PR2): A catalog of unicellular eukaryote small sub-unit rRNA sequences with curated taxonomy. Nucleic Acids Res. 41, D597–D604 (2013).CAS 
PubMed 
Article 

Google Scholar 
77.Gong, W. & Marchetti, A. Estimation of 18S gene copy number in marine eukaryotic plankton using a next-generation sequencing approach. Front. Mar. Sci. 6, 219 (2019).Article 

Google Scholar 
78.Metfies, K. et al. Uncovering the intricacies of microbial community dynamics at Helgoland Roads at the end of a spring bloom using automated sampling and 18S meta-barcoding. PLoS ONE 15, e0233921 (2020).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
79.Catlett, D. et al. Evaluation of accuracy and precision in an amplicon sequencing workflow for marine protist communities. Limnol. Oceanogr. Methods 18, 20–40 (2019).Article 

Google Scholar 
80.Kattner, G. & Fricke, H. S. G. Simple gas-liquid-chromatographic method for the simultaneous determination of fatty-acids and alcohols in wax esters of marine organisms. J. Chromatogr. 361, 263–268 (1986).CAS 
Article 

Google Scholar 
81.Gloor, G. B., Macklaim, J. M., Pawlowsky-Glahn, V. & Egozcue, J. J. Microbiome datasets are compositional: And this is not optional. Front. Microbiol. 8, 2224 (2017).PubMed 
PubMed Central 
Article 

Google Scholar 
82.Palarea-Albaladejo, J. & Martín-Fernández, J. A. zCompositions—R package for multivariate imputation of left-censored data under a compositional approach. Chemometrics Intell. Lab. Syst. 143, 85–96 (2015).CAS 
Article 

Google Scholar 
83.Fernandes, A. D., Macklaim, J. M., Linn, T. G., Reid, G. & Gloor, G. B. ANOVA-Like differential expression (ALDEx) analysis for mixed population RNA-Seq. PLoS ONE 8, e67019 (2013).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
84.Quinn, T. P., Richardson, M. F., Lovell, D. & Crowley, T. M. propr: an R-package for identifying proportionally abundant features using compositional data analysis. Sci. Rep. 7, 16252 (2017).PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
85.Bian, G. et al. The gut microbiota of healthy aged chinese is similar to that of the healthy young. mSphere 2, e00327–00317 (2017).PubMed 
PubMed Central 
Article 

Google Scholar 
86.Gloor, G. B. & Reid, G. Compositional analysis: A valid approach to analyze microbiome high-throughput sequencing data. Can. J. Microbiol. 62, 692–703 (2016).CAS 
PubMed 
Article 

Google Scholar 
87.Borstein, S. R. dietr: An R package for calculating fractional trophic levels from quantitative and qualitative diet data. Hydrobiologia 847, 4285–4294 (2020).Article 

Google Scholar 
88.Lechowicz, M. J. The sampling characteristics of electivity indices. Oecologia 52, 22–30 (1982).PubMed 
Article 

Google Scholar 
89.Dalsgaard, J., St John, M., Kattner, G., Muller-Navarra, D. & Hagen, W. Fatty acid trophic markers in the pelagic marine environment. Adv. Mar. Biol. 46, 225–340 (2003).PubMed 
Article 

Google Scholar 
90.Graeve, M., Kattner, G. & Hagen, W. Diet-induced changes in the fatty acid composition of Arctic herbivorous copepods: Experimental evidence of trophic markers. J. Exp. Mar. Biol. Ecol. 182, 97–110 (1994).CAS 
Article 

Google Scholar 
91.Kharlamenko, V. I., Zhukova, N. V., Khotimchenko, S. V., Svetashev, V. I. & Kamenev, G. M. Fatty acids as markers of food sources in a shallow-water hydrothermal ecosystem (Kraternaya Bight, Yankich Island, Kurile Islands). Mar. Ecol. Prog. Ser. 120, 231–241 (1995).CAS 
Article 

Google Scholar 
92.Greenacre, M. Compositional Data Analysis in Practice (CRC Press, Taylor & Francis Group, 2018).93.Suh, H.-L. & Nemoto, T. Comparative morphology of filtering structure of five species of Euphausia (Euphausiacea, Crustacea) from the Antarctic Ocean. Proc. NIPR Symp. Polar Biol. 1, 72–83 (1987).
Google Scholar 
94.Alldredge, A. L. & Madin, L. P. Pelagic tunicates: unique herbivores in the marine plankton. Bioscience 32, 655–663 (1982).Article 

Google Scholar 
95.Kelly, P. S. The Ecological Role of Salpa Thompsoni in the Kerguelen Plateau Region of the Southern Ocean: A First Comprehensive Evaluation. PhD thesis, University of Tasmania (2019).96.Ericson, J. A. et al. Seasonal and interannual variations in the fatty acid composition of adult Euphausia superba Dana, 1850 (Euphausiacea) samples derived from the Scotia Sea krill fishery. J. Crust. Biol. 38, 662–672 (2018).
Google Scholar 
97.Martin, D. L., Ross, R. M., Quetin, L. B. & Murray, A. E. Molecular approach (PCR-DGGE) to diet analysis in young Antarctic krill Euphausia superba. Mar. Ecol. Prog. Ser. 319, 155–165 (2006).CAS 
Article 

Google Scholar 
98.Matsuoka, K. et al. Quantarctica, an integrated mapping environment for Antarctica, the Southern Ocean, and sub-Antarctic islands. Environ. Model. Softw. 140, 105015 (2021).Article 

Google Scholar  More