More stories

  • in

    Myzomyia and Pyretophorus series of Anopheles mosquitoes acting as probable vectors of the goat malaria parasite Plasmodium caprae in Thailand

    Asada, M. et al. Close relationship of Plasmodium sequences detected from South American pampas deer (Ozotoceros bezoarticus) to Plasmodium spp. in North American white-tailed deer. Int. J. Parasitol. 7, 44–47. https://doi.org/10.1016/j.ijppaw.2018.01.001 (2018).Article 

    Google Scholar 
    Boundenga, L. et al. Haemosporidian parasites of antelopes and other vertebrates from Gabon, Central Africa. PLoS ONE 11, e0148958. https://doi.org/10.1371/journal.pone.0148958 (2016).Article 
    CAS 

    Google Scholar 
    Martinsen, E. S., Perkins, S. L. & Schall, J. J. A three-genome phylogeny of malaria parasites (Plasmodium and closely related genera): Evolution of life-history traits and host switches. Mol. Phylogen. Evol. 47, 261–273. https://doi.org/10.1016/j.ympev.2007.11.012 (2008).Article 
    CAS 

    Google Scholar 
    Templeton, T. J. et al. Ungulate malaria parasites. Sci. Rep. 6, 23230. https://doi.org/10.1038/srep23230 (2016).Article 
    ADS 
    CAS 

    Google Scholar 
    Templeton, T. J., Martinsen, E., Kaewthamasorn, M. & Kaneko, O. The rediscovery of malaria parasites of ungulates. Parasitology 143, 1501–1508. https://doi.org/10.1017/s0031182016001141 (2016).Article 

    Google Scholar 
    Bruce, D., Harvey, D., Hamerton, A. E. & Bruce, L. Plasmodium cephalophi, sp. nov. Proc. R. Soc. B. 87, 45–47 (1913).ADS 

    Google Scholar 
    Sheather, A. L. A malarial parasite in the blood of a buffalo. J. Comp. Pathol. 32, 223–229 (1919).Article 

    Google Scholar 
    Kandel, R. C. et al. First report of malaria parasites in water buffalo in Nepal. Vet. Parasitol. Reg. Stud. Rep. 18, 100348. https://doi.org/10.1016/j.vprsr.2019.100348 (2019).Article 

    Google Scholar 
    de Mello, F. & Paes, S. Sur une plasmodiae du sang des chèvres. C. R. Séanc. Soc. Biol 88, 829–830 (1923).
    Google Scholar 
    Kaewthamasorn, M. et al. Genetic homogeneity of goat malaria parasites in Asia and Africa suggests their expansion with domestic goat host. Sci. Rep. 8, 5827. https://doi.org/10.1038/s41598-018-24048-0 (2018).Article 
    ADS 
    CAS 

    Google Scholar 
    Garnham, P. C. & Edeson, J. F. Two new malaria parasites of the Malayan mousedeer. Riv. Malariol. 41, 1–8 (1962).CAS 

    Google Scholar 
    Garnham, P. C. & Kuttler, K. L. A malaria parasite of the white-tailed deer (Odocoileus virginianus) and its relation with known species of Plasmodium in other ungulates. Proc. R. Soc. Lond. B 206, 395–402. https://doi.org/10.1098/rspb.1980.0003 (1980).Article 
    ADS 
    CAS 

    Google Scholar 
    Martinsen, E. et al. Hidden in plain sight: Cryptic and endemic malaria parasites in North American white-tailed deer (Odocoileus virginianus). Sci. Adv. 2, e1501486. https://doi.org/10.1126/sciadv.1501486 (2016).Article 
    ADS 
    CAS 

    Google Scholar 
    Rattanarithikul, R. et al. Illustrated keys to the mosquitoes of Thailand. IV. Anopheles. Southeast Asian. Trop. Med. Public Health 37, 1–128 (2006).
    Google Scholar 
    Walter Reed Biosystematics Unit. Systematic catalogue of Culicidae. http://mosquitocatalog.org (2021).Manguin, S., Garros, C., Dusfour, I., Harbach, R. E. & Coosemans, M. Bionomics, taxonomy, and distribution of the major malaria vector taxa of Anopheles subgenus Cellia in Southeast Asia: An updated review. Infect. Genet. Evol. 8, 489–503. https://doi.org/10.1016/j.meegid.2007.11.004 (2008).Article 
    CAS 

    Google Scholar 
    Brosseau, L. et al. A multiplex PCR assay for the identification of five species of the Anopheles barbirostris complex in Thailand. Parasit. Vectors 12, 223. https://doi.org/10.1186/s13071-019-3494-8 (2019).Article 

    Google Scholar 
    Paredes-Esquivel, C., Donnelly, M. J., Harbach, R. E. & Townson, H. A molecular phylogeny of mosquitoes in the Anopheles barbirostris Subgroup reveals cryptic species: implications for identification of disease vectors. Mol. Phylogen. Evol. 50, 141–151. https://doi.org/10.1016/j.ympev.2008.10.011 (2009).Article 
    CAS 

    Google Scholar 
    Taai, K. & Harbach, R. E. Systematics of the Anopheles barbirostris species complex (Diptera: Culicidae: Anophelinae) in Thailand. Zool. J. Linn. Soc. 174, 244–264. https://doi.org/10.1111/zoj.12236 (2015).Article 

    Google Scholar 
    Garros, C., Van Bortel, W., Trung, H. D., Coosemans, M. & Manguin, S. Review of the Minimus Complex of Anopheles, main malaria vector in Southeast Asia: From taxonomic issues to vector control strategies. Trop. Med. Int. Health 11, 102–114. https://doi.org/10.1111/j.1365-3156.2005.01536.x (2006).Article 
    CAS 

    Google Scholar 
    Dahan-Moss, Y. et al. Member species of the Anopheles gambiae complex can be misidentified as Anopheles leesoni. Malar. J. 19, 89. https://doi.org/10.1186/s12936-020-03168-x (2020).Article 
    CAS 

    Google Scholar 
    Van Bortel, W. et al. Confirmation of Anopheles varuna in Vietnam, previously misidentified and mistargeted as the malaria vector Anopheles minimus. Am. J. Trop. Med. Hyg. 65, 729–732. https://doi.org/10.4269/ajtmh.2001.65.729 (2001).Article 

    Google Scholar 
    Wharton, R. H., Eyles, D. E., Warren, M., Moorhouse, D. E. & Sandosham, A. A. Investigations leading to the identification of members of the Anopheles umbrosus group as the probable vectors of mouse deer malaria. Bull. 29, 357–374 (1963).CAS 

    Google Scholar 
    Nugraheni, Y. R. et al. Myzorhynchus series of Anopheles mosquitoes as potential vectors of Plasmodium bubalis in Thailand. Sci. Rep. 12, 5747. https://doi.org/10.1038/s41598-022-09686-9 (2022).Article 
    ADS 
    CAS 

    Google Scholar 
    Tu, H. L. C. et al. Development of a novel multiplex PCR assay for the detection and differentiation of Plasmodium caprae from Theileria luwenshuni and Babesia spp. in goats. Acta Trop. 220, 105957. https://doi.org/10.1016/j.actatropica.2021.105957 (2021).Article 
    CAS 

    Google Scholar 
    Cywinska, A., Hunter, F. F. & Hebert, P. D. Identifying Canadian mosquito species through DNA barcodes. Med. Vet. Entomol. 20, 413–424. https://doi.org/10.1111/j.1365-2915.2006.00653.x (2006).Article 
    CAS 

    Google Scholar 
    Hebert, P. D., Cywinska, A., Ball, S. L. & de Waard, J. R. Biological identifications through DNA barcodes. Proc. R. Soc. Lond. B 270, 313–321. https://doi.org/10.1098/rspb.2002.2218 (2003).Article 
    CAS 

    Google Scholar 
    Ogola, E. O., Chepkorir, E., Sang, R. & Tchouassi, D. P. A previously unreported potential malaria vector in a dry ecology of Kenya. Parasit. Vectors 12, 80. https://doi.org/10.1186/s13071-019-3332-z (2019).Article 

    Google Scholar 
    Maquart, P. O., Fontenille, D., Rahola, N., Yean, S. & Boyer, S. Checklist of the mosquito fauna (Diptera, Culicidae) of Cambodia. Parasite 28, 60. https://doi.org/10.1051/parasite/2021056 (2021).Article 

    Google Scholar 
    Tainchum, K. et al. Diversity of Anopheles species and trophic behavior of putative malaria vectors in two malaria endemic areas of northwestern Thailand. J. Vector. Ecol. 39, 424–436. https://doi.org/10.1111/jvec.12118 (2014).Article 

    Google Scholar 
    Vantaux, A. et al. Anopheles ecology, genetics and malaria transmission in northern Cambodia. Sci. Rep. 11, 6458. https://doi.org/10.1038/s41598-021-85628-1 (2021).Article 
    ADS 
    CAS 

    Google Scholar 
    Chookaew, S. et al. Anopheles species composition in malaria high-risk areas in Ranong Province. Dis. Control J. 46, 483–493. https://doi.org/10.14456/dcj.2020.45 (2020).Article 

    Google Scholar 
    Makanga, B. et al. Ape malaria transmission and potential for ape-to-human transfers in Africa. Proc. Natl. Acad. Sci. USA. 113, 5329–5334. https://doi.org/10.1073/pnas.1603008113 (2016).Article 
    ADS 
    CAS 

    Google Scholar 
    Ariey, F., Gay, F. & Ménard, R. Malaria Control and Elimination Vol. 254 (Springer, 2020).
    Google Scholar 
    Williams, J. & Pinto, J. Training Manual on Malaria Entomology (Springer, 2012).
    Google Scholar 
    Rigg, C. A., Hurtado, L. A., Calzada, J. E. & Chaves, L. F. Malaria infection rates in Anopheles albimanus (Diptera: Culicidae) at Ipetí-Guna, a village within a region targeted for malaria elimination in Panamá. Infect. Genet. Evol. 69, 216–223. https://doi.org/10.1016/j.meegid.2019.02.003 (2019).Article 

    Google Scholar 
    Torres-Cosme, R. et al. Natural malaria infection in anophelines vectors and their incrimination in local malaria transmission in Darién Panama. PLoS ONE 16, e0250059. https://doi.org/10.1371/journal.pone.0250059 (2021).Article 
    CAS 

    Google Scholar 
    Beebe, N. W. & Saul, A. Discrimination of all members of the Anopheles punctulatus complex by polymerase chain reaction-restriction fragment length polymorphism analysis. Am. J. Trop. Med. Hyg. 53, 478–481. https://doi.org/10.4269/ajtmh.1995.53.478 (1995).Article 
    CAS 

    Google Scholar 
    Perkins, S. L. & Schall, J. J. A molecular phylogeny of malarial parasites recovered from cytochrome b gene sequences. J. Parasitol. 88, 972–978. https://doi.org/10.1645/0022-3395(2002)088[0972:AMPOMP]2.0.CO;2 (2002).Article 
    CAS 

    Google Scholar 
    Snounou, G. et al. High sensitivity of detection of human malaria parasites by the use of nested polymerase chain reaction. Mol. Biochem. Parasitol. 61, 315–320. https://doi.org/10.1016/0166-6851(93)90077-B (1993).Article 
    CAS 

    Google Scholar 
    Hall, T. A. BioEdit: A user-friendly biological sequence alignment editor and analysis program for windows 95/98/NT. Nucleic. Acids. Symp. Ser. 41, 95–98 (1999).CAS 

    Google Scholar 
    Huelsenbeck, J. P. & Ronquist, F. MRBAYES: Bayesian inference of phylogenetic trees. Bioinformatics 17, 754–755 (2001).Article 
    CAS 

    Google Scholar 
    Rambaut, A., Drummond, A. J., Xie, D., Baele, G. & Suchard, M. A. Posterior summarization in Bayesian phylogenetics using tracer 1.7. Syst. Biol. 67, 901–904. https://doi.org/10.1093/sysbio/syy032 (2018).Article 
    CAS 

    Google Scholar 
    Nguyen, L. T., Schmidt, H. A., von Haeseler, A. & Minh, B. Q. IQ-TREE: A fast and effective stochastic algorithm for estimating maximum-likelihood phylogenies. Mol. Biol. Evol. 32, 268–274. https://doi.org/10.1093/molbev/msu300 (2015).Article 
    CAS 

    Google Scholar 
    Ventim, R. et al. Avian malaria infections in western European mosquitoes. Parasitol. Res. 111, 637–645. https://doi.org/10.1007/s00436-012-2880-3 (2012).Article 

    Google Scholar  More

  • in

    Escaping Darwin’s shadow: how Alfred Russel Wallace inspires Indigenous researchers

    A map of the Amazon River and its tributaries, as published in Alfred Russel Wallace’s 1853 book.Credit: Mary Evans/Natural History Museum

    Dzoodzo Baniwa, a member of an Indigenous community in Brazil’s Amazonas state, has been collecting data on the region’s biodiversity for around 15 years. He lives in a remote village called Canadá on the Ayari River, a tributary of the Içana, which in turn feeds the Rio Negro, one of the main branches of the Amazon. The nearest city, São Gabriel da Cachoeira, is a three-day trip by motor boat.Dzoodzo (who goes by his Indigenous name but is also known as Juvêncio Cardoso) takes inspiration for his work from many cross-cultural sources. A perhaps unexpected one is a 170-year-old book by the British naturalist Alfred Russel Wallace, who visited the Amazon and Negro rivers on his expeditions in 1848–52. A Narrative of Travels on the Amazon and Rio Negro gives detailed accounts of the wildlife and people Wallace encountered near Dzoodzo’s home, including the Guianan cock-of-the-rock (Rupicola rupicola), a bright orange bird that Wallace describes as “magnificent … sitting amidst the gloom, shining out like a mass of brilliant flame”1.Dzoodzo’s passion for local biodiversity is reflected in his work at Baniwa Eeno Hiepole School, an internationally praised education centre for Indigenous people. He dreams of one day turning it into a research institute and university that might increase scientific understanding of the region’s species, including R. rupicola.
    Alfred Russel Wallace’s first expedition ended in flames
    Wallace, who was born 200 years ago, on 8 January 1823, is best known for spurring Charles Darwin into finally publishing On the Origin of Species, after Wallace sent Darwin his own independent discovery of evolution by natural selection in 1858. Most of Wallace’s subsequent work drew on observations from his 1854–62 expeditions in southeast Asia; his earlier work in Amazonia is much less well known.Yet there are lessons from Wallace’s time in Brazil that are especially relevant for conservationists and other scientists today — notably, what can come from paying attention to what local people say about their own territory.Barriers and boundariesWallace made two key contributions that still shape thinking about Amazonia, the world’s most biodiverse region, which covers parts of Bolivia, Brazil, Colombia, Ecuador, Peru, Venezuela, Guyana, Suriname and French Guiana.On 14 December 1852, Wallace read out his manuscript ‘On the monkeys of the Amazon’ at a meeting of the Zoological Society of London. In this study, which was later published2, Wallace relays observations that form the basis of the most debated hypothesis for how Amazonian organisms diversified: the riverine barrier hypothesis.His paper refers to the large Amazonian rivers as spatial boundaries to the ranges of several primate species. “I soon found that the Amazon, the Rio Negro and the Madeira formed the limits beyond which certain species never passed,” he writes. Since 1852, Wallace’s observations that large rivers could act as geographical barriers that shape the distribution of species have been corroborated, criticized and debated by many. The phenomenon he described clearly holds for some groups, such as monkeys and birds3,4, but not for other groups, such as plants and insects5.Subsequent researchers have explored whether the distribution patterns of species, such as those observed by Wallace, indicate that the evolution of the Amazonian drainage system has itself driven the diversification of species6. Work in the past few years by geologists and biologists show that this drainage system, which includes some of the largest rivers in the world, is dynamic7, and that its rearrangements lead to changes in the distribution ranges of species8. Current species ranges thus hold information about how the Amazonian landscape has changed over time.

    The Guianan cock-of-the-rock (Rupicola rupicola), which Wallace likened to a “brilliant flame”.Credit: Hein Nouwens/Getty

    The second crucial observation made by Wallace, also in his 1852 paper, was that the composition of species varies in different regions. He describes how “several Guiana species come up to the Rio Negro and Amazon, but do not pass them; Brazilian species on the contrary reach but do not pass the Amazon to the north. Several Ecuador species from the east of the Andes reach down into the tongue of land between the Rio Negro and Upper Amazon, but pass neither of those rivers, and others from Peru are bounded on the north by the Upper Amazon, and on the east by the Madeira.” From these observations, he concluded that “there are four districts, the Guiana, the Ecuador, the Peru and the Brazil districts, whose boundaries on one side are determined by the rivers I have mentioned.”
    Evolution’s red-hot radical
    Even though Amazonia is presented as a single, large, green ellipse in most world maps, it is actually a heterogeneous place, with each region and habitat type holding a distinct set of species9,10. The four districts proposed by Wallace are bounded by the region’s largest rivers: the Amazon, Negro and Madeira. But further studies of species ranges since then have revealed more districts, now called areas of endemism, some of which are also bounded by these and other large Amazonian rivers, such as the Tapajós, Xingu and Tocantins9,11.This recognition of spatial heterogeneity in Amazonian species distributions — first accomplished by Wallace — is essential for today’s research, conservation and planning10. Each area of endemism includes species that occur only in that area. And different areas of endemism are affected differently by anthropogenic impacts, such as deforestation, fires and development10. More than half of Amazonia is now within federal or state reserves or Indigenous lands — territories that are recognized by current governments as belonging to Indigenous people. But nearly half of the region’s areas of endemism are located in the south of the region, close to the agricultural frontier, and the species they contain are severely threatened by habitat loss10 (see also www.raisg.org/en).Local knowledgeAlthough Wallace’s writings indicate that in many ways he admired most of the Indigenous people he met, especially those from the upper Rio Negro basin, he still viewed Indigenous people through the European colonial lens of his time. In A Narrative of Travels on the Amazon and Rio Negro1, Wallace describes the Indigenous communities he encountered as “in an equally low state of civilization” — albeit seemingly “capable of being formed, by education and good government, into a peaceable and civilized community”.Yet he did better than many of his contemporaries when it came to respecting local knowledge. In his 1852 paper, for example, Wallace notes that his fellow European naturalists often give vague information about the locality of their collected specimens, and fail to specify such localities in relation to river margins. By contrast, he writes, the “native hunters are perfectly acquainted” with the impact of rivers on the distribution of species, “and always cross over the river when they want to procure particular animals, which are found even on the river’s bank on one side, but never by any chance on the other.” Likewise, in his 1853 book1, Wallace frequently corroborates his findings with information he has obtained from Indigenous people — for example, about the habitat preferences of umbrellabirds (Cephalopterus ornatus) or of “cow-fish” (manatees; Trichechus inunguis).Considering the vastness and complexity of Amazonia, it is hard to see how Wallace could have gained the insights he did after working in the region for only four years, had he not paid close attention to local knowledge.
    The other beetle-hunter
    Amazonian Indigenous peoples have had to endure invasion of their lands, enslavement, violence from invaders and the imposition of other languages and cultures. Despite this, numerous Indigenous researchers wish to expand their knowledge about Amazonia by combining Indigenous and European world views. Meanwhile, a better understanding of how the Amazonian socio-ecological system is organized, and how it is being affected by climate change and local and regional impacts12, hinges on the ability of researchers worldwide to learn from and to be led by Indigenous scientists.The 98 Indigenous lands in the Rio Negro basin cover more than 33 million hectares (see go.nature.com/3wkkftu). If the hopes of Dzoodzo and others to build a research institute and university for the region are met, school students will no longer have to leave their homeland to pursue higher education. The community would have a way to document its own knowledge and that of its ancestors in a more systematic way. And the legitimization of Indigenous people’s research efforts in the legal and academic frameworks recognized by non-Indigenous scientists — such as through the awarding of degrees — would make it easier for Indigenous researchers to partner with other organizations, both nationally and internationally.Indigenous people in the Rio Negro basin today are no longer objects of observation — they have taken charge of their own research using tools from different cultures. Indeed, Dzoodzo is turning to Wallace’s writings, in part, to learn more about how his own ancestors lived.Perhaps the thread between Wallace and Dzoodzo, spanning so many years and such disparate cultures, could seed new kinds of partnership in which learning is reciprocal and for the benefit of all. More

  • in

    Citizen science helps in the study of fungal diversity in New Jersey

    Martinez-Garcia, L. B., De Deyn, G. B., Pugnaire, F. I., Kothamasi, D. & van der Heijden, M. G. A. Symbiotic soil fungi enhance ecosystem resilience to climate change. Glob. Chang. Biol. 23, 5228–5236 (2017).Article 
    ADS 

    Google Scholar 
    Averill, C. & Hawkes, C. V. Ectomycorrhizal fungi slow soil carbon cycling. Ecol. Lett. 19, 937–947 (2016).Article 

    Google Scholar 
    Cairney, J. W. G. Extramatrical mycelia of ectomycorrhizal fungi as moderators of carbon dynamics in forest soil. Soil Biol. Biochem. 47, 198–208 (2012).Article 
    CAS 

    Google Scholar 
    Milovic, M., Kebert, M. & Orlovic, S. How mycorrhizas can help forests to cope with ongoing climate change? Sumar. List 145, 279–286 (2021).Article 

    Google Scholar 
    Hawksworth, D. L. & Luecking, R. Fungal diversity revisited: 2.2 to 3.8 million species. Microbiol. Spectr. 5, 5.4.10 (2017).Article 

    Google Scholar 
    Stork, N. E. How many species of insects and other terrestrial arthropods are there on Earth? Annu. Rev. Entomol. 63, 31–45 (2018).Article 
    CAS 

    Google Scholar 
    Christenhusz, M. J. M. & Byng, J. W. The number of known plants species in the world and its annual increase. Phytotaxa 261, 201–217 (2016).Article 

    Google Scholar 
    Terrer, C., Vicca, S., Hungate, B. A., Phillips, R. P. & Prentice, I. C. Mycorrhizal association as a primary control of the CO2 fertilization effect. Science 353, 72–74 (2016).Article 
    ADS 
    CAS 

    Google Scholar 
    van der Heijden, M. G. A., Martin, F. M., Selosse, M. A. & Sanders, I. R. Mycorrhizal ecology and evolution: the past, the present, and the future. New Phytol. 205, 1406–1423 (2015).Article 

    Google Scholar 
    Braghiere, R. K. et al. Modeling global carbon costs of plant nitrogen and phosphorus acquisition. J. Adv. Model. Earth Syst. 14, e2022MS003204 (2022).Article 
    ADS 
    CAS 

    Google Scholar 
    Jaouen, G. et al. Fungi of French Guiana gathered in a taxonomic, environmental and molecular dataset. Sci. Data 6, 206 (2019).Article 

    Google Scholar 
    Beninde, J. et al. CaliPopGen: A genetic and life history database for the fauna and flora of California. Sci. Data 9, 380 (2022).Article 

    Google Scholar 
    Gyeltshen, C. & Prasad, K. Biodiversity checklists for Bhutan. Biodivers. Data J. 10, e83798 (2022).Article 

    Google Scholar 
    Steidinger, B. S. et al. Climatic controls of decomposition drive the global biogeography of forest-tree symbioses. Nature 569, 404–408 (2019).Article 
    ADS 
    CAS 

    Google Scholar 
    Soudzilovskaia, N. A. et al. Global mycorrhizal plant distribution linked to terrestrial carbon stocks. Nat. Commun. 10, 5077 (2019).Article 
    ADS 

    Google Scholar 
    Melo, C. D., Walker, C., Freitas, H., Machado, A. C. & Borges, P. A. V. Distribution of arbuscular mycorrhizal fungi (AMF) in Terceira and Sao Miguel Islands (Azores). Biodivers. Data J. 8, e49759 (2020).Article 

    Google Scholar 
    Ordynets, A. et al. Aphyllophoroid fungi in insular woodlands of eastern Ukraine. Biodivers. Data J. 5, e22426 (2017).Article 

    Google Scholar 
    Monteiro, M. et al. A database of the global distribution of alien macrofungi. Biodivers. Data J. 8, e51459 (2020).Article 

    Google Scholar 
    Filippova, N. et al. Yugra State University Biological Collection (Khanty-Mansiysk, Russia): general and digitisation overview. Biodivers. Data J. 10, e77669 (2022).Article 

    Google Scholar 
    Wu, B. et al. Current insights into fungal species diversity and perspective on naming the environmental DNA sequences of fungi. Mycology 10, 127–140 (2019).Article 

    Google Scholar 
    Nilsson, R. H. et al. The UNITE database for molecular identification of fungi: handling dark taxa and parallel taxonomic classifications. Nucleic Acids Res. 47, D259–D264 (2019).Article 
    CAS 

    Google Scholar 
    Gorczak, M. et al. 18th Congress of European Mycologists Bioblitz 2019 – naturalists contribute to the knowledge of mycobiota and lichenobiota of Białowieża Primeval Forest. Acta Mycol. 55, 1–26 (2020).
    Google Scholar 
    Goncalves, S. C., Haelewaters, D., Furci, G. & Mueller, G. M. Include all fungi in biodiversity goals. Science 373, 403–403 (2021).Article 
    ADS 

    Google Scholar 
    Hochkirch, A. et al. A strategy for the next decade to address data deficiency in neglected biodiversity. Conserv. Biol. 35, 502–509 (2021).Article 

    Google Scholar 
    Allen, E. B. et al. Patterns and regulation of mycorrhizal plant and fungal diversity. Plant Soil 170, 47–62 (1995).Article 
    CAS 

    Google Scholar 
    Mueller, G. M. & Schmit, J. P. Fungal biodiversity: what do we know? What can we predict? Biodivers. Conserv. 16, 1–5 (2007).Article 

    Google Scholar 
    Waters, D. P. & Lendemer, J. C. The lichens and allied fungi of Mercer County, New Jersey. Opusc. Philolichenum 18, 17–51 (2019).
    Google Scholar 
    Waters, D. P. & Lendemer, J. C. A revised checklist of the lichenized, lichenicolous and allied fungi of New Jersey. Bartonia, 1–62 (2019).Schwarze, C. A. The parasitic fungi of New Jersey. (New Jersey Agricultural Experiment Stations, 1917).Moose, R. A., Schigel, D., Kirby, L. J. & Shumskaya, M. Dead wood fungi in North America: an insight into research and conservation potential. Nat. Conserv. 32, 1–17 (2019).Article 

    Google Scholar 
    Hibbett, D. S. et al. A higher-level phylogenetic classification of the Fungi. Mycol. Res. 111, 509–547 (2007).Article 

    Google Scholar 
    Hibbett, D. The invisible dimension of fungal diversity. Science 351, 1150–1151 (2016).Article 
    ADS 
    CAS 

    Google Scholar 
    James, T. Y., Stajich, J. E., Hittinger, C. T. & Rokas, A. Toward a Fully Resolved Fungal Tree of Life. Annu. Rev. Microbiol. 74, 291–313 (2020).Article 
    CAS 

    Google Scholar 
    Braghiere, R. K. et al. Mycorrhizal distributions impact global patterns of carbon and nutrient cycling. Geophys. Res. Lett. 48, e2021GL094514 (2021).Article 
    ADS 
    CAS 

    Google Scholar 
    Bonney, R. et al. Citizen science: A developing tool for expanding science knowledge and scientific literacy. Bioscience 59, 977–984 (2009).Article 

    Google Scholar 
    Van Vliet, K. & Moore, C. Citizen science initiatives: engaging the public and demystifying science. J. Microbiol. Biol. Educ. 17, 13–16 (2016).Article 

    Google Scholar 
    Feldman, M. J. et al. Trends and gaps in the use of citizen science derived data as input for species distribution models: A quantitative review. PLoS One 16, e0234587 (2021).Article 
    CAS 

    Google Scholar 
    Shumskaya, M. et al. Fungi of parks, forests and reserves of New Jersey (2007–2019). Version 1.4. Sampling event dataset. Kean University https://doi.org/10.15468/7scek4 (2022).Heilmann-Clausen, J. et al. How citizen science boosted primary knowledge on fungal biodiversity in Denmark. Biol. Conserv. 237, 366–372 (2019).Article 

    Google Scholar 
    GBIF.Org User. NJMA dataset. GBIF Occurrence Download. GBIF https://doi.org/10.15468/dl.93232n (2022).GBIF.Org User. New Jersey Agaricomycetes. GBIF Occurrence Download. Dataset. GBIF https://doi.org/10.15468/dl.6j6382 (2022).GBIF.Org User. USA Agaricomycetes. GBIF Occurrence Download. GBIF https://doi.org/10.15468/dl.ncukzy (2022).GBIF.Org User. Global records Agaricomycetes. GBIF Occurrence Download. GBIF https://doi.org/10.15468/dl.nk54e7 (2022).Meyke, E. When data management meets project management. Biodivers. Inf. Sci. Stand. 3, e37224 (2019).
    Google Scholar 
    Wieczorek, J. et al. Darwin Core: an evolving community-developed biodiversity data standard. PLoS One 7, e29715 (2012).Article 
    ADS 
    CAS 

    Google Scholar 
    Pagad, S., Genovesi, P., Carnevali, L., Schigel, D. & McGeoch, M. A. Data Descriptor: introducing the global register of introduced and invasive species. Sci. Data 5, 170102 (2018).Article 

    Google Scholar 
    Registry-Migration.Gbif.Org.GBIF Backbone Taxonomy. GBIF Secretariat. https://doi.org/10.15468/39omei (2021).Mesibov, R. Archived websites: A Data Cleaner’s Cookbook (version 3) and all BASHing data blog posts 1–200. Zenodo https://doi.org/10.5281/zenodo.6423347 (2022).Chamberlain, S. A. & Boettiger, C. R Python, and Ruby clients for GBIF species occurrence data. PeerJ Preprints 5, e3304v3301 (2017).
    Google Scholar 
    Chamberlain, S. et al. rgbif: Interface to the Global Biodiversity Information Facility API. R package version 3.7.1. Available from https://cran.rproject.org/package=rgbif (2022).Nguyen, N. H. et al. FUNGuild: An open annotation tool for parsing fungal community datasets by ecological guild. Fungal Ecol. 20, 241–248 (2016).Article 

    Google Scholar 
    Sousa, D. et al. Tree canopies reflect mycorrhizal composition. Geophys. Res. Lett. 48, e2021GL092764 (2021).Article 
    ADS 

    Google Scholar 
    R Core Team. R: A language and environment for statistical computing. https://www.R-project.org/ (2020).Wickham, H. ggplot2: Elegant Graphics for Data Analysis. https://ggplot2.tidyverse.org (2016).Bederson, B. B., Shneiderman, B. & Wattenberg, M. Ordered and quantum treemaps: Making effective use of 2D space to display hierarchies. ACM Trans. Graph. 21, 833–854 (2002).Article 

    Google Scholar 
    Simpson, H. J. & Schilling, J. S. Using aggregated field collection data and the novel r package fungarium to investigate fungal fire association. Mycologia 113, 842–855 (2021).Article 

    Google Scholar 
    Robertson, T. et al. The GBIF Integrated Publishing Toolkit: Facilitating the efficient publishing of biodiversity data on the Internet. PLoS One 9, e102623 (2014).Article 
    ADS 

    Google Scholar  More

  • in

    Alfred Russel Wallace’s first expedition ended in flames

    Naturalist Alfred Russel Wallace went on an expedition to Amazonas state in Brazil in 1848–52.Credit: Mondadori Portfolio via Getty

    Best known for formulating the theory of evolution by natural selection, independently of Charles Darwin, Alfred Russel Wallace is an appealing if enigmatic figure. The appeal stems in part from his underdog status: poor and self-educated, Wallace had none of Darwin’s social and financial advantages. The enigma comes from his keen embrace of a range of eccentric non-scientific causes, including spiritualism, phrenology and anti-vaccination (for smallpox).Scientists do not like their scientific heroes to bear the taint of irrational thinking. Wallace’s enthusiasms have therefore contributed to him becoming marginalized in the history of evolutionary thought. Most people know about Darwin and the HMS Beagle. But what about Wallace and the Helen?The Helen story is worth revisiting because it shows Wallace at his resolute best. Despite numerous disastrous career setbacks — of which the Helen episode was the most severe — he persevered and eventually succeeded as a scientist.More than 150 years after Wallace’s experience on the Helen, doing science continues to be hard and can be disappointing. Wallace’s misadventure provides both perspective and an object lesson in how to navigate setbacks. His response to problems showcases his most inspiring traits: his commitment to science, his almost superhuman resilience and his refusal to mire himself in self-pity.Tropical explorationsIn his first job as a land surveyor, Wallace developed an interest in the plants he encountered as he tramped across the countryside. Then, in 1844 at the age of 21, he met Henry Walter Bates, who would later discover ‘Batesian mimicry’ (whereby members of a palatable prey species gain protection by mimicking an unpalatable one).Bates, two years Wallace’s junior, had a fixation with beetles, and he catalysed Wallace’s transformation from hobbyist naturalist to serious collector. Wallace’s new-found focus on beetles transcended mere entomological stamp-collecting; he developed an interest in some of the great scientific questions of the time. He was particularly inspired by the anonymously published Vestiges of the Natural History of Creation (1844) by Robert Chambers, which put forward a vision of a transmutational process, with life progressing from simple to complex.Without money or connections, Wallace and Bates aspired to careers in science at a time when the field was the preserve of the moneyed elite. They would have to fund their scientific explorations by collecting and selling specimens. After a hasty choice of destination — tropical South America — and a crash course in collecting methods, Wallace, aged 25, and Bates, aged 23, arrived in Belém, Brazil, in May 1848 (see ‘Doggedly determined’).
    Doggedly determined

    Alfred Russel Wallace tends to be unjustly relegated to a footnote in the Charles Darwin story. He was, in fact, a pioneering biologist who refused to let disadvantage or disaster prevent him from pursuing his scientific dreams.
    January 1823: Alfred Russel Wallace is born in Usk in Wales.
    May 1848: Wallace and Henry Walter Bates arrive in Belém, Brazil.
    July 1852: Wallace boards the Helen, which catches fire three weeks later while at sea.
    October 1852: Wallace reaches Deal, England, aboard the Jordeson.
    March 1854: Wallace leaves Southampton for southeast Asia.
    September 1855: Wallace’s first evolutionary paper describing his ‘Sarawak Law’ is published.
    May 1856: Citing the Sarawak Law paper, geologist Charles Lyell alerts Darwin to the possibility that Wallace is developing ideas similar to Darwin’s.
    February 1858: Wallace sends his paper on natural selection to Darwin from Ternate in the Maluku islands (Moluccas), Indonesia.
    July 1858: The joint Darwin–Wallace paper is presented at the Linnean Society in London.
    November 1859: Darwin’s On the Origin of Species is published.
    March 1862: Wallace returns from southeast Asia.
    November 1913: Wallace dies in Broadstone, England.

    The two split up early on, with Wallace concentrating on the Amazon River’s northern tributary, the Rio Negro, and Bates on the southern fork, the Solimões.Collecting was challenging. The Amazon’s ubiquitous ants often deprived science of hard-won specimens. Crucial collecting materials also disappeared: Wallace once recovered from a bout of fever to discover that local people had drunk the cachaça (a Brazilian rum) he’d been using to pickle specimens. Transport was a constant headache, with travel upstream past rapids requiring unwieldy portages of canoes and cargo. And thanks to his collecting, the cargo became ever more voluminous and unwieldy.Wallace and Bates sporadically sent back shipments of material to their agent in London, Samuel Stevens, who publicized their adventures in scientific journals and sold their specimens, taking a 20% commission.
    Escaping Darwin’s shadow: how Alfred Russel Wallace inspires Indigenous researchers
    Wallace’s journeys on the Rio Negro and its tributaries took him into areas that had not yet been visited by Europeans. He saw (and collected) an extraordinary array of species, many of them new to science. He had a chance to observe and collect artefacts from several Indigenous groups with little or no previous contact with Europeans. As he travelled, Wallace capitalized on his surveying skills to map the terrain. But the remoteness took its toll. He made an “inward vow never to travel again in such wild, unpeopled districts without some civilised companion or attendant”1.Wallace was frequently ill, on one occasion nearly lethally so. His younger brother came out to join him as an assistant in 1849 but died of yellow fever two years later in Belém, on his way back to England. Wallace learnt that his brother was sick but had to wait many anxious months before news of his death made it upriver.In 1852, after four years of exploring and collecting, it was time for Wallace himself to head home. He envisaged a triumphant return. He would complement his collections of preserved organisms with a menagerie of living ones. Mr Wallace’s biological wonders would surely be the toast of scientific London.On 12 July in Belém, Wallace boarded the Helen, a freighter ship bound for London. The trip across the continent to Belém had not gone smoothly. The authorities in Manaus, Brazil, had had to be persuaded to release some of his earlier shipments meant for London, which they had impounded, making the final haul aboard the Helen even larger. But now all that remained was the long voyage back across the Atlantic. Wallace, who shared Captain Turner’s cabin, was the only passenger.Disaster strikesThree weeks into the voyage, Captain Turner interrupted Wallace’s morning routine to tell him that the ship was on fire.Friction caused by the rocking of the ship had ignited poorly stowed cargo. Attempts to intervene were counterproductive — removing the hold covers merely oxygenated the fire — and soon the ship became what Wallace later called “a most magnificent conflagration”1.Captain Turner gave the order to abandon ship, and the scramble to prepare two small wooden boats began. Having been stored on deck in the tropical sunshine, both boats leaked badly. The cook had to find corks to plug their hulls.Before he left the ship, Wallace “went down into the cabin, now suffocatingly hot and full of smoke, to see what was worth saving”1. He retrieved his “watch and a small tin box containing some shirts and a couple of old note-books, with some drawings of plants and animals, and scrambled up with them on deck”1. He tried to lower himself on a rope into one of the small boats, but fever-weakened, he ended up sliding down the rope, stripping the skin off his hands.

    Some of Alfred Russel Wallace’s sketches were salvaged from the fire aboard the Helen on his return journey from South America in 1852.Credit: The Natural History Museum/Alamy

    With fine weather, the best hope of rescue lay in other ships seeing the fire. The two boats duly circled the burning wreck for the next 24 hours, meaning that Wallace got to witness every moment of the tragedy. The animals he had brought with him on the long river journey across the continent, now free from their cages, sought refuge on the one part of the ship still untouched by the flames, the bowsprit. Wallace watched as the monkeys, parrots and more — his pets as well as his best hope of impressing London’s scientific elite — were incinerated.The hoped-for rescue did not immediately materialize, and Captain Turner turned the two open boats towards Bermuda, 1,100 kilometres away to the northwest.As the days ticked by, the situation became increasingly desperate. Water ran low and the tropical sun left Wallace’s “hands and face very much blistered”1. Wallace nevertheless remained upbeat, later recalling that during one night, he “saw several meteors, and in fact could not be in a better position for observing them, than lying on [his] back in a small boat in the middle of the Atlantic”1.Finally, ten days into the ordeal, salvation appeared on the horizon in the form of the Jordeson, a creaking and already overladen cargo ship bound for London.With the immediate crisis past, the magnitude of what had happened started to sink in. In a letter2 written aboard the Jordeson to botanist Richard Spruce (see go.nature.com/3prhbdk), Wallace tallied his catastrophic losses — “almost all the reward of my four years of privation & danger was lost” — and concluded with characteristic understatement, “I have some need of philosophic resignation to bear my fate with patience and equanimity.”
    Evolution’s red-hot radical
    The Jordeson finally limped into Deal, England, on 1 October 1852. Wallace had been at sea for 80 days. His outward voyage with Bates had taken only 29 days.Wallace added a PS to his letter to Spruce. First there was immediate exhilaration about the return — “Such a dinner! Oh! beef steaks & damson tart”. But then came thoughts about the future: “Fifty times since I left Pará [Belém] have I vowed if I once reached England never to trust myself more on the ocean.” Even then, he noted that “good resolutions soon fade”.Stevens had thoughtfully taken out insurance. So Wallace had £200 (US$980 at the time) — a fraction of his collections’ actual value — to cover his costs for a year in London while he tried to salvage what he could from the disaster and make future plans.He rushed out two books, one a travelogue, the other a more technical account of the palm trees of the Amazon. Neither did well — 250 copies remained unsold a decade later from the travel book’s print run of 750. But he was getting his name out there. Stevens, too, had a done a good job of publicizing Wallace’s discoveries while Wallace had been away.Perhaps most crucially, the positive response of the UK Royal Geographical Society to his mapping work of the Rio Negro yielded a free steamship ticket to Singapore.In March 1854, less than 18 months since the Jordeson’s bedraggled arrival at Deal, Wallace departed from Southampton in England for what he would call the “central and controlling incident”2 of his life.Eight more years of perilous travel awaited. So, too, did the discoveries of what came to be known as Wallace’s Line (a boundary between the Asian and Australasian biogeographic regions) and of the theory of evolution by natural selection3,4.The scientific acclaim that greeted Wallace’s return from southeast Asia in 1862 was a just reward both for his contributions and for that phenomenal doggedness — his determination, despite everything, to be a scientist. More

  • in

    Spatio-temporal patterns of Synechococcus oligotypes in Moroccan lagoonal environments

    In a previous study18, we used bioinformatics tools to analyze the metagenome and the amplicon 16S sequences to gain an insight into microbial diversity in Moroccan lagoons, namely Marchica and Oualidia. 16S rRNA gene classification revealed a high percentage of bacteria in both lagoons. On average, bacteria accounted for 90% of the total prokaryotes in Marchica and ~ 70% in Oualidia. The five phyla that were the most abundant in both lagoons, Marchica and Oualidia, respectively, were Proteobacteria (53.62%, 29.18%), Bacteroidetes (16.46%, 43.49%), Cyanobacteria (0.53%, 34.35%), Verrucomicrobia (1.75%, 15.82%), and Actinobacteria (7.42%, 13.98%). At the genus level, we found that the highest assigned hits were attributed to Synechococcus, which was highly abundant in Marchica (32%) compared to Oualidia (0.07%) in 2014. This amount dropped to 22% in Marchica and 0.04% in Oualidia in 2015. Hence, in this study we performed the analysis of the Synechococcus genus community using oligotyping to investigate their dynamics and understand their co-occurrence and covariation in space and time within fragile ecosystems such as lagoons.We may divide our results into two emerging Synechococcus communities: one dominated in 2014 and the other was less present in 2015, each composed of different cooccurring Synechococcus oligotypes. The abundant Synechococcus community in Marchica in 2014 consisted of clades I, 5.3, III, IV, and VII. These clades are typically found in either warmer or more oligotrophic environments19,20. This result is in accordance with Marchica’s environmental characteristics; it is an oligotrophic ecosystem with high primary production and warmer water in summer21. The community included clades CB5 and WPC1 in Marchica 2014 and 2015 when the number of Synechococcus reads was lower. Strains belonging to the CB5 clade lack phycourobilin (PUB), contain one motile strain22,23, are present in temperate coastal waters and are prevalent in polar/subpolar waters24,25,26. WPC1 strains are observed in open-ocean and near-shore waters1,24,27. Clades IV and I usually co-occur and are more prevalent in cold coastal waters19,28,29,30. Interestingly, Clade III was prominent in Marchica. This clade is known to be motile and restricted to warm, oligotrophic water19,20,30. Although at a smaller read number, clade III was also observed in Oualidia, where the temperature is cooler compared to Marchica. Furthermore, we found that clade III growth has been shown to be severely affected at low temperatures30. Moreover, representatives of both clades I and IV were present in Oualidia in both the summers of 2014 and 2015. Some Synechococcus strains, which are known to prefer cooler water temperatures and salinities, were in higher relative abundance in the waters of Marchica. This result agrees with a previous study showing that Synechococcus isolates of clades I and IV exhibited temperature preferences31. Their growth rates were marginally lower at low temperatures in strains from clades I and IV, which were dominant in temperate regions.Nitrate levels are typically low or undetectable in these lagoons, which allows the persistence of clades that would not typically thrive in coastal waters at other times of the year. In 2014, the nitrate concentration was higher than the average of 10 mg/l, which could be due to increased agricultural activities and wastewater treatment plant effluent21. The decreasing nitrate concentration in Marchica in 2015 could be explained by the newly installed inlet in 2010, which was designed to improve water exchange with the open sea and reduce the amount of suspended matter21. Temperature and salinity have a large effect on nitrate in marine ecosystems32; the highest nitrate degradation rates were observed at 35 °C and at increasing salinity rates. Therefore, we expected to see correlations between salinity, temperature and nitrate concentrations. Interestingly, clades CB5 in Marchica and IV in Oualidia increased in relative abundance in summer 2015 compared to 2014, when the nitrate concentration decreased. Moreover, the Synechococcus microbial community diversity and density are variables depending on the variations in the physical and chemical parameters. These parameters are strongly influenced by the marine waters passing through the artificial inlets, which have an impact on the internal hydrodynamics of both lagoons and hence the distribution and co-occurrence of Synechococcus strains. In addition, anthropogenic activities also have a great influence on Synechococcales population growth and interactions with their viruses33,34.This study revealed some differences between Marchica and Oualidia in identified Synechococcus clades. The Marchica lagoon showed more heterogeneity (clades I, II, III, IV, VII, VIII, 5.3, WPC1, CB5, and IX) than the Oualidia lagoon, where fewer clades were identified (I, III, IV, and VII). There was a clear variation in the pattern of correlation between oligotypes of the same or different clades for both the 2014 and 2015 samplings. Furthermore, we observed complex patterns of co-occurrence among oligotypes; in 2014 (clades I, III, IV, 5.3, VII), and in 2015, we found clades CB5 and WPC1. In Oualidia, values decreased in comparison to Marchica in both 2014 and 2015 summer samplings, following a pattern of co-occurrence, especially for both clades I and IV in both sampling years. Many studies have shown that the relative proportions of cooccurring Synechococcus populations to each other at the clade and subclade levels vary in space and time based on environmental factors such as seasonal temperature fluctuations, nutrient availability and upwelling, circulation patterns, and abundance of other phytoplankton8.We presume that the greater variability in oligotype co-occurrence behavior observed in Marchica Lagoon, especially in the summer of 2014, could be due to the higher abundance and diversity of Synechococcus oligotypes, physico-chemical parameter fluctuations or rehabilitation of the lagoon.Less abundant oligotypes could also be considered potential bioindicators of Synechococcus genetic diversity. Their seasonal occurrence might contribute to changing ecological and biogeochemical characteristics of the marine environment35. The Synechococcus relative abundance count revealed that the Marchica Synechococcus community included the least abundant oligotypes in 2015. For instance, O7 and O8 were detected in 2014 and were absent in 2015 (Table 1). It is unclear which factors served to constrain the relative abundances of these least present oligotypes, but temperature and salinity could have an impact on their distribution in Marchica (Fig. 4) and the opposite for Oualidia, which are cooler-temperature adapted ones. We noticed that the relative abundance of cooccurring Synechococcus was not constant. For instance, oligotype 4 belonging to Clade IV showed higher values in summer 2014 (974 reads) in Marchica compared to summer 2015 (319 reads), and the opposite was observed in Oualidia, with a lower abundance compared to Marchica. Increased values of cooccurring clade I oligotypes (14, 26, and 6) were detected in the summer of 2014 in both lagoons.Figure 4Principle component analysis of Synechococcus oligotype relative abundance. The plot is generated using the relative abundance of each oligotype, T temperature, S Salinity, and NO3− Nitrate. Each point represents an oligotype. Colors represent the year of sampling; red for 2014 and blue for 2015. The shape of point indicates the sampling site; rounded points refer to Marchica lagoon, and triangles refer to Oualidia. Circles represent the normal distribution of oligotypes; the red circle refers to 2014, and the blue one refers to 2015.Full size imageIn comparing our results with a study from Little Sippewissett Marsh (LSM)8 that used oligotyping to investigate the distribution of the genus Synechococcus in space and time sequencing the V4-V6 hypervariable region of the 16S rRNA gene, we found 31 oligotypes, while they identified 12. In both studies, the proportion of Synechococcus oligotypes increased in summer and in coastal waters compared to estuaries. In addition, Clades I and IV were more abundant in saline conditions, such as Marchica Lagoon. However, these clades were found in greater relative abundances at cold temperatures, in contrast to our study, where they were identified in Marchica’s warm waters. Moreover, clade CB5 tended to be prominent at relatively warm temperatures (17–20 °C)6. In our work, it was not prevalent either in cooler or warmer water. Notably, the relative abundance of rare oligotypes was higher in warm hypersaline estuary waters8,18, while in our case study, they occurred in cooler moderately saline Oualidia waters.The dominance of a certain clade could have many different ecological ramifications, especially as the clades can be incredibly diverse in their growth, loss, nutrient utilization and other attributes. The dominant clade’s growth and loss patterns will set the stage for the population dynamics. For instance, if the dominant clade only blooms in a given environmental factor such as temperature, light, or salinity, it will then affect the timing of blooms, and follow-on the effects of subsequent grazing, lysis or even biogeochemical cycling. Even if the population is diverse, the dynamics as a whole will be a composite response of each individual clade’s ecophysiology, making it important to understand their composition and how it changes over space and time.While the rpoC1 gene is a higher resolution diversity marker36, 16S amplicon data can be used for exploring the entire bacterial assemblage including Synechococcus clade designations via oligotyping35. The latter has a great advantage in answering unexplained diversity contained in taxa using 16S rRNA gene sequences. Nevertheless, it has some limitations, as it acts optimally only when performed on taxa that are closely related. Regarding distantly related taxa, the high number of increased-entropy locations makes the supervision steps difficult. In addition, although oligotyping does not rely on clustering conditions or availability of existing reads within reference databases, it demands preliminary operational taxonomic unit clustering to find closely related species appropriate for the analysis. This method is under continuous improvement to better exploit the information within subtle variations in 16S rRNA gene sequences5.In conclusion, we explored the patterns of Synechococcus diversity in space and time using an oligotyping approach to examine these populations in lagoon waters of Mediterranean Marchica and Atlantic Oualidia, in Morocco. Patterns that have been observed at the clade and subclade levels, such as Synechococcus, relative abundance and the co-occurrence of groups from different clades, were shown to occur among oligotypes. The Marchica Lagoon showed a heterogeneous Synechococcus diversity compared to Oualidia in summer 2014. Thirty-one Synechococcus oligotypes were identified. Two distinct communities emerged in the 2014 and 2015 summer samplings, abundant and rare Synechococcus species, each comprising cooccurring Synechococcus oligotypes from different clades. Network analysis showed that six oligotypes were exclusive to Marchica Lagoon. The identified clades I, III, IV, VII, and 5.3 in Marchica were in accordance with its environmental characteristics. In addition, the relative abundance of some cooccurring Synechococcus strains was not constant over time and space (e.g., clades I and IV). Using gene oligotyping, we illustrated some of the challenges associated with the identification of novel Synechococcus strains or studied their co-occurrence in space and time. Oligotyping has been instrumental in discriminating closely related Synechococcus strains. However, this study leaves open questions about how samples differ by location and whether locations differ from year to year. Do cooccurring oligotypes interact with each other and to what extent do they correlate with physicochemical parameters? What triggers the coexistence of clades I and IV with clade III in warm water or 5.3 with VII, which do not know much about. Finally, how do relative abundances change over seasons. Hence, future work needs to consider additional stations and seasons to provide better statistical support for our findings and to better understand their correlation with physical and chemical environmental parameters. Other factors were not considered in this study, such as nutrient availability, chlorophyll, irradiance, viral lysis, and greater sequencing depth, which could also influence the observed seasonal dynamics. More

  • in

    Image dataset for benchmarking automated fish detection and classification algorithms

    Cheung, W. W. L. et al. Shrinking of fishes exacerbates impacts of global ocean changes on marine ecosystems. Nat. Clim. Chang. 3, 254–258, https://doi.org/10.1038/nclimate1691 (2013).Article 
    ADS 

    Google Scholar 
    Cheung, W. W. L., Watson, R. & Pauly, D. Signature of ocean warming in global fisheries catch. Nature 497, 365–368, https://doi.org/10.1038/nature12156 (2013).Article 
    ADS 
    CAS 

    Google Scholar 
    Hilborn, R. et al. Global status of groundfish stocks. Fish Fish. 00, 1–18, https://doi.org/10.1111/faf.12560 (2021).Article 

    Google Scholar 
    Aguzzi, J. et al. Challenges to the assessment of benthic populations and biodiversity as a result of rhythmic behaviour: video solutions from cabled observatories. Oceanography and Marine Biology: An Annual Review 50, 233–284 (2012).
    Google Scholar 
    Aguzzi, J. et al. Coastal observatories for monitoring of fish behaviour and their responses to environmental changes. Reviews in fish biology and fisheries 25, 463–483, https://doi.org/10.1007/s11160-015-9387-9 (2015).Article 

    Google Scholar 
    Doya, C. et al. Diel behavioral rhythms in sablefish (Anoplopoma fimbria) and other benthic species, as recorded by the Deep-sea cabled observatories in Barkley canyon (NEPTUNE-Canada). Journal of Marine Systems 130, 69–78, https://doi.org/10.1016/j.jmarsys.2013.04.003 (2014).Article 
    ADS 

    Google Scholar 
    Aguzzi, J. et al. Ecological video monitoring of Marine Protected Areas by underwater cabled surveillance cameras. Marine Policy 119, 104052, https://doi.org/10.1016/j.marpol.2020.104052 (2020).Article 

    Google Scholar 
    Milligan, R. J. et al. Evidence for seasonal cycles in deep‐sea fish abundances: A great migration in the deep SE Atlantic? Journal of Animal Ecology 89, 1593–1603, https://doi.org/10.1111/1365-2656.13215 (2020).Article 

    Google Scholar 
    Hutchingson, G. E. Concluding remarks. Cold Spring Harbor Symp. 22, 415–427, https://doi.org/10.1101/SQB.1957.022.01.039 (1957).Article 

    Google Scholar 
    Hut, R. A., Kronfeld-Schor, N., Van Der Vinne, V. & De la Iglesia, H. In search of a temporal niche: environmental factors. Progress in brain research 199, 281–304, https://doi.org/10.1016/B978-0-444-59427-3.00017-4 (2012).Article 

    Google Scholar 
    Aguzzi, J. et al. The hierarchic treatment of marine ecological information from spatial networks of benthic platforms. Sensors 20, 1751, https://doi.org/10.3390/s20061751 (2020).Article 
    ADS 

    Google Scholar 
    Danovaro, R. et al. A new international ecosystem-based strategy for the global deep ocean. Science 355, 452–454, https://doi.org/10.1126/science.aah7178 (2017).Article 
    ADS 
    CAS 

    Google Scholar 
    Aguzzi, J. et al. The potential of video imagery from worldwide cabled observatory networks to provide information supporting fish-stock and biodiversity assessment. ICES Journal of Marine Science 77, 2396–2410, https://doi.org/10.1093/icesjms/fsaa169 (2020).Article 

    Google Scholar 
    Aguzzi, J. et al. New high-tech flexible networks for the monitoring of deep-sea ecosystems. Environmental science and technology 53, 6616–6631, https://doi.org/10.1021/acs.est.9b00409 (2019).Article 
    ADS 
    CAS 

    Google Scholar 
    Rountree, R. A. et al. Towards an optimal design for ecosystem-level ocean observatories. In Oceanography and Marine Biology. Taylor and Francis, pp. 79–106 (2020).Aguzzi, J. et al. Developing technological synergies between deep-sea and space research. Elementa: Science of the Anthropocene 10, 00064, https://doi.org/10.1525/elementa.2021.00064 (2022).Article 

    Google Scholar 
    Aguzzi, J. et al. Multiparametric monitoring of fish activity rhythms in an Atlantic coastal cabled observatory. Journal of Marine Systems 212, 103424, https://doi.org/10.1016/j.jmarsys.2020.103424 (2020).Article 

    Google Scholar 
    Matabos et al. Expert, Crowd, Students or Algorithm: who holds the key to deep-sea imagery ‘big data’ processing? Methods in Ecology and Evolution 8, 996–1004, https://doi.org/10.1111/2041-210X.12746 (2017).Article 

    Google Scholar 
    Zuazo, A. et al. An automated pipeline for image processing and data treatment to track activity rhythms of Paragorgia arborea in relation to hydrographic conditions. Sensors 20, 6281, https://doi.org/10.3390/s20216281 (2020).Article 
    ADS 

    Google Scholar 
    Dibattista, J. D. et al. Community-based citizen science projects can support the distributional monitoring of fishes. Aquatic Conservation: Marine and Freshwater Ecosystems 31, 3580–3593, https://doi.org/10.1002/aqc.3726 (2021).Article 

    Google Scholar 
    Malde, K., Handegard, N. O., Eikvil, L. & Salberg, A. B. Machine intelligence and the data-driven future of marine science. ICES Journal of Marine Science 77, 1274–1285, https://doi.org/10.1093/icesjms/fsz057 (2020).Article 

    Google Scholar 
    European Marine Board. Big Data in Marine Science. European Marine Broad Advencing Seas & Ocean Science. https://www.marineboard.eu/publications/big-data-marine-science (2020).Aguzzi, J. et al. The new SEAfloor OBservatory (OBSEA) for remote and long-term coastal ecosystem monitoring. Sensors-Basel 11, 5850–5872, https://doi.org/10.3390/s110605850 (2011).Article 
    ADS 

    Google Scholar 
    Del Rio, J. et al. Obsea: a decadal balance for a cabled observatory deployment. IEEE Access 8, 33163–33177, https://doi.org/10.1109/ACCESS.2020.2973771 (2020).Article 

    Google Scholar 
    Condal, F. et al. Seasonal rhythm in a Mediterranean coastal fish community as monitored by a cabled observatory. Marine Biology 159, 2809–2817, https://doi.org/10.1007/s00227-012-2041-3 (2012).Article 

    Google Scholar 
    Naylor, E. Chronobiology of marine organisms (Cambridge University Press, 2010).Weis, J. S., Smith, G., Zhou, T., Santiago-Bass, C. & Weis, P. Effects of contaminants on behavior: biochemical mechanisms and ecological consequences: killifish from a contaminated site are slow to capture prey and escape predators; altered neurotransmitters and thyroid may be responsible for this behavior, which may produce population changes in the fish and their major prey, the grass shrimp. Bioscience 51, 209–217 https://doi.org/10.1641/0006-3568(2001)051[0209:EOCOBB]2.0.CO;2 (2001).Bellido, J. M. et al. Identifying essential fish habitat for small pelagic species in Spanish Mediterranean waters. In Essential Fish Habitat Mapping in the Mediterranean. Springer Netherlands, 171–184 https://doi.org/10.1007/978-1-4020-9141-4_13 (2008).Brander, K. Impacts of climate change on fisheries. Journal of Marine Systems 79, 389–402, https://doi.org/10.1016/j.jmarsys.2008.12.015 (2010).Article 
    ADS 

    Google Scholar 
    Viehman, H. A. & Zydlewski, G. B. Multi-scale temporal patterns in fish presence in a high-velocity tidal channel. PLoS One 12, e0176405, https://doi.org/10.1371/journal.pone.0176405 (2017).Article 
    CAS 

    Google Scholar 
    Van Der Walt, K. A., Porri, F., Potts, W. M., Duncan, M. I. & James, N. C. Thermal tolerance, safety margins and vulnerability of coastal species: Projected impact of climate change induced cold water variability in a temperate African region. Marine Environmental Research 169, 105346, https://doi.org/10.1016/j.marenvres.2021.105346 (2021).Article 
    CAS 

    Google Scholar 
    Marini, S. et al. Tracking fish abundance by underwater image recognition. Scientific reports 8, 1–12, https://doi.org/10.1038/s41598-018-32089-8 (2018).Article 
    ADS 
    CAS 

    Google Scholar 
    Sbragaglia, V. et al. Annual rhythms of temporal niche partitioning in the Sparidae family are correlated to different environmental variables. Scientific reports 9, 1–11, https://doi.org/10.1038/s41598-018-37954-0 (2019).Article 
    ADS 
    CAS 

    Google Scholar 
    Francescangeli, M. et al. Long-Term Monitoring of Diel and Seasonal Rhythm of Dentex dentex at an Artificial Reef. Frontier in Marine Science 9, 1–17, https://doi.org/10.3389/fmars.2022.801033 (2022).Article 

    Google Scholar 
    Knausgård, K. M. et al. Temperate fish detection and classification: a deep learning based approach. Applied Intelligence 52, 6988–7001, https://doi.org/10.1007/s10489-020-02154-9 (2022).Article 

    Google Scholar 
    Wu, J. et al. Multi-Label Active Learning Algorithms for Image Classification: Overview and Future Promise. ACM Computing Surveys (CSUR) 53, 1–35, https://doi.org/10.1145/3379504 (2020).Article 

    Google Scholar 
    He J., Mao R., Shao Z. & Zhu F. Incremental Learning in Online Scenario. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. 13923–13932 https://doi.org/10.1109/CVPR42600.2020.01394 (2020).Zhou, D. W., Yang, Y., & Zhan, D. C. Learning to Classify with Incremental New Class. In IEEE Transactions on Neural Networks and Learning Systems https://doi.org/10.1109/TNNLS.2021.3104882 (2021).Hashmani, M. A., Jameel, S. M., Alhussain, H., Rehman, M. & Budiman, A. Accuracy performance degradation in image classification models due to concept drift. International Journal of Advanced Computer Science and Applications 10, 422–425, https://doi.org/10.14569/ijacsa.2019.0100552 (2019).Article 

    Google Scholar 
    Langenkämper, D., van Kevelaer, R., Purser, A. & Nattkemper, T. W. Gear-Induced Concept Drift in Marine Images and Its Effect on Deep Learning Classification. Front. Mar. Sci. 7, 506, https://doi.org/10.3389/fmars.2020.00506 (2020).Article 

    Google Scholar 
    Kloster, M., Langenkämper, D., Zurowietz, M., Beszteri, B. & Nattkemper, T. W. Deep learning-based diatom taxonomy on virtual slides. Scientific Reports 10, 1–13, https://doi.org/10.1038/s41598-020-71165-w (2020).Article 
    CAS 

    Google Scholar 
    Ottaviani, E. et al. Assessing the image concept drift at the OBSEA coastal underwater cabled observatory. Frontiers in Marine Science 9, 1–13, https://doi.org/10.3389/fmars.2022.840088 (2022).Article 

    Google Scholar 
    Katija, K. et al. FathomNet: A global image database for enabling artificial intelligence in the ocean. Scientific reports 12, 1–14, https://doi.org/10.1038/s41598-022-19939-2 (2022).Article 
    ADS 
    CAS 

    Google Scholar 
    Kohavi, R. A study of cross-validation and bootstrap for accuracy estimation and model selection. International Joint Conference on Artificial Intelligence 14, 1137–1145 (1995).
    Google Scholar 
    Tharwat, A. Classification assessment methods. Applied Computing and Informatics 17, 168–192, https://doi.org/10.1016/j.aci.2018.08.003 (2018).Article 

    Google Scholar 
    Qi, C., Diao, J. & Qiu, L. On estimating model in feature selection with cross-validation. IEEE Access 7, 33454–33463, https://doi.org/10.1109/ACCESS.2019.2892062 (2019).Article 

    Google Scholar 
    Lopez-Vazquez, V. et al. Video image enhancement and machine learning pipeline for underwater animal detection and classification at cabled observatories. Sensors 20, 726, https://doi.org/10.3390/s20030726 (2020).Article 
    ADS 

    Google Scholar 
    Francescangeli, M. et al. Underwater camera photos with manual tagging of fish species at OBSEA seafloor observatory from 2013 to 2014. PANGAEA https://doi.pangaea.de/10.1594/PANGAEA.946149 (2022).Marini, S. Source code for: simoneMarinIsmar/Image-Tagging-tool: Image Tagging (v1.0). Zenodo https://doi.org/10.5281/zenodo.6566282 (2022).Froese, R. & Pauly, D. FishBase. www.fishbase.org (2019).Martinez Padro, E. et al. CTD data acquired at the OBSEA seafloor observatory from 2013 to 2014. PANGAEA https://doi.org/10.1594/PANGAEA.946015 (2022).Martinez Padro, E. et al. Meteorological data from a weather station at Vilanova i la Geltrú (Catalonia, Spain) from 2013 to 2014. PANGAEA https://doi.org/10.1594/PANGAEA.945911 (2022).Martinez Padro, E. et al. Meteorological data from a weather station at Sant Pere de Ribes (Catalonia, Spain) from 2013 to 2014. PANGAEA https://doi.org/10.1594/PANGAEA.945906 (2022).Redmon, J., Divvala, S., Girshick, R. & Farhadi, A. You Only Look Once: Unified, Real-Time Object Detection. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 779–788 https://doi.org/10.1109/CVPR.2016.91 (2016).Marrable, D. et al. Accelerating species recognition and labelling of fish from underwater video with machine-assisted deep learning. Frontiers in Marine Science 9, 944582, https://doi.org/10.3389/fmars.2022.944582 (2022).Article 

    Google Scholar 
    Zabala, M., García-Rubies, A., & Corbera, J. Els peixos de les illes Medes i del litoral català: guia per observar-los al seu ambient (Centre d’Estudis Marins de Badalona, 1992).Corbera, J., Sabatés, A., & García-Rubies, A. Peces de mar de la península ibérica (Ed. Planeta, 1996).Mercader, L., Lloris, D., & Rucabado, J. Tots els peixos del mar Català: Diagnosis i claus d’identificació (Institut d’Estudis Catalans, 2001).Aguzzi, J. et al. Daily activity rhythms in temperate coastal fishes: insights from cabled observatory video monitoring. Marine Ecology Progress Series 486, 223–236, https://doi.org/10.3354/meps10399 (2013).Article 
    ADS 

    Google Scholar 
    Campos‐Candela, A. et al. A camera‐based method for estimating absolute density in animals displaying home range behaviour. Journal of Animal Ecology 87, 825–837, https://doi.org/10.1111/1365-2656.12787 (2018).Article 

    Google Scholar 
    Jang, J. & Yoon, S. Feature concentration for supervised and semisupervised learning with unbalanced datasets in visual inspection. IEEE Transactions on Industrial Electronics 68, 7620–7630, https://doi.org/10.1109/TIE.2020.3003622 (2020).Article 

    Google Scholar 
    Zhang, J. et al. Adaptive Vertical Federated Learning on Unbalanced Features. IEEE Transactions on Parallel and Distributed Systems 33, 4006–4018, https://doi.org/10.1109/TPDS.2022.3178443 (2022).Article 

    Google Scholar 
    Lin, C. H., Lin, C. S., Chou, P. Y. & Hsu, C. C. An Efficient Data Augmentation Network for Out-of-Distribution Image Detection. IEEE Access 9, 35313–35323, https://doi.org/10.1109/ACCESS.2021.3062187 (2021).Article 

    Google Scholar 
    Lu, Y., Chen, D., Olaniyi, E. & Huang, Y. Generative adversarial networks (GANs) for image augmentation in agriculture: A systematic review. Computers and Electronics in Agriculture 200, 107208, https://doi.org/10.1016/j.compag.2022.107208 (2022).Article 

    Google Scholar 
    Waqas, N., Safie, S. I., Kadir, K. A., Khan, S. & Khel, M. H. K. DEEPFAKE Image Synthesis for Data Augmentation. IEEE Access 10, 80847–80857, https://doi.org/10.1109/ACCESS.2022.3193668 (2022).Article 

    Google Scholar  More

  • in

    Methane emissions offset atmospheric carbon dioxide uptake in coastal macroalgae, mixed vegetation and sediment ecosystems

    Mcleod, E. et al. A blueprint for blue carbon: toward an improved understanding of the role of vegetated coastal habitats in sequestering CO 2. Front. Ecol. Environ. 9, 552–560 (2011).Article 

    Google Scholar 
    Macreadie, P. I. et al. The future of Blue Carbon science. Nat. Commun. 10, 1–13 (2019).
    Google Scholar 
    Lovelock, C. E. & Duarte, C. M. Dimensions of Blue Carbon and emerging perspectives. Biol. Lett. 15, 20180781 (2019).Article 

    Google Scholar 
    Macreadie, P. I. et al. Blue carbon as a natural climate solution. Nat Rev Earth Environ 2, 826–839 (2021).Al‐Haj, A. N. & Fulweiler, R. W. A synthesis of methane emissions from shallow vegetated coastal ecosystems. Glob. Chang. Biol. 26, 2988–3005 (2020).Article 
    ADS 

    Google Scholar 
    Rosentreter, J. A. et al. Half of global methane emissions come from highly variable aquatic ecosystem sources. Nat. Geosci. https://doi.org/10.1038/s41561-021-00715-2 (2021).Bastviken, D., Tranvik, L. J., Downing, J. A., Crill, P. M. & Enrich-Prast, A. Freshwater methane emissions offset the continental carbon sink. Science (80-) 331, 50–50 (2011).Article 
    ADS 
    CAS 

    Google Scholar 
    Rosentreter, J. A., Maher, D. T., Erler, D. V., Murray, R. H. & Eyre, B. D. Methane emissions partially offset “blue carbon” burial in mangroves. Sci. Adv. 4, eaao4985 (2018).Article 
    ADS 

    Google Scholar 
    Rosentreter, J. A., Al‐Haj, A. N., Fulweiler, R. W. & Williamson, P. Methane and nitrous oxide emissions complicate coastal blue carbon assessments. Glob. Biogeochem. Cycles 35, e2020GB006858 (2021).Duarte, C. M., Middelburg, J. J. & Caraco, N. Major role of marine vegetation on the oceanic carbon cycle. Biogeosciences 2, 1–8 (2005).Article 
    ADS 
    CAS 

    Google Scholar 
    Snelgrove, P. V. R. et al. Global carbon cycling on a heterogeneous seafloor. Trends Ecol. Evol. 33, 96–105 (2018).Article 

    Google Scholar 
    Ortega, A. et al. Important contribution of macroalgae to oceanic carbon sequestration. Nat. Geosci. 12, 748–754 (2019).Article 
    ADS 
    CAS 

    Google Scholar 
    Barnes, R. O. & Goldberg, E. D. Methane production and consumption in anoxic marine sediments. Geology 4, 297 (1976).Article 
    ADS 
    CAS 

    Google Scholar 
    Reeburgh, W. S. Rates of biogeochemical processes in anoxic sediments. Annu. Rev. Earth Planet. Sci. 11, 269–298 (1983).Article 
    ADS 
    CAS 

    Google Scholar 
    Wallenius, A. J., Dalcin Martins, P., Slomp, C. P. & Jetten, M. S. M. Anthropogenic and environmental constraints on the microbial methane cycle in coastal sediments. Front. Microbiol. 12, 631621 (2021).Tokoro, T. et al. Net uptake of atmospheric CO2 by coastal submerged aquatic vegetation. Glob. Chang. Biol. 20, 1873–1884 (2014).Article 
    ADS 

    Google Scholar 
    Gallagher, J. B., Shelamoff, V. & Layton, C. Seaweed ecosystems may not mitigate CO2 emissions. ICES J. Mar. Sci. https://doi.org/10.1093/icesjms/fsac011 (2022).Oremland, R. S. & Taylor, B. F. Sulfate reduction and methanogenesis in marine sediments. Geochim. Cosmochim. Acta 42, 209–214 (1978).Article 
    ADS 
    CAS 

    Google Scholar 
    Egger, M., Riedinger, N., Mogollón, J. M. & Jørgensen, B. B. Global diffusive fluxes of methane in marine sediments. Nat. Geosci. 11, 421–425 (2018).Article 
    ADS 
    CAS 

    Google Scholar 
    Weber, T., Wiseman, N. A. & Kock, A. Global ocean methane emissions dominated by shallow coastal waters. Nat. Commun. 10, 1–10 (2019).Article 

    Google Scholar 
    Neubauer, S. C. & Megonigal, J. P. Moving beyond global warming potentials to quantify the climatic role of ecosystems. Ecosystems 18, 1000–1013 (2015).Article 

    Google Scholar 
    Neubauer, S. C. Global warming potential is not an ecosystem property. Ecosystems https://doi.org/10.1007/s10021-021-00631-x (2021).Howard, J., Hoyt, S., Isensee, K., Telszewski, M. & Pidgeon, E. Coastal blue carbon: methods for assessing carbon stocks and emissions factors in mangroves, tidal salt marshes, and seagrasses. 1–181 (2014). https://unesdoc.unesco.org/ark:/48223/pf0000372868.Berg, P., Huettel, M., Glud, R. N., Reimers, C. E. & Attard, K. M. Aquatic eddy covariance: the method and its contributions to defining oxygen and carbon fluxes in marine environments. Ann. Rev. Mar. Sci. 14, 431–455 (2022).Article 

    Google Scholar 
    Tokoro, T., Watanabe, K., Tada, K. & Kuwae, T. Air–water CO2 flux in shallow coastal waters: theory, methods, and empirical studies. in Blue Carbon in Shallow Coastal Ecosystems 153–184 (Springer Singapore, 2019).Saintilan, N., Rogers, K., Mazumder, D. & Woodroffe, C. Allochthonous and autochthonous contributions to carbon accumulation and carbon store in southeastern Australian coastal wetlands. Estuar. Coast. Shelf Sci. 128, 84–92 (2013).Article 
    ADS 
    CAS 

    Google Scholar 
    Ollivier, Q. R., Maher, D. T., Pitfield, C. & Macreadie, P. I. Net drawdown of greenhouse gases (CO2, CH4 and N2O) by a temperate australian seagrass meadow. Estuaries Coasts https://doi.org/10.1007/s12237-022-01068-8 (2022).Maher, D. T. et al. Novel use of cavity ring-down spectroscopy to investigate aquatic carbon cycling from microbial to ecosystem scales. Environ. Sci. Technol. 47, 12938–12945 (2013).Article 
    ADS 
    CAS 

    Google Scholar 
    Call, M. et al. Spatial and temporal variability of carbon dioxide and methane fluxes over semi-diurnal and spring–neap–spring timescales in a mangrove creek. Geochim. Cosmochim. Acta 150, 211–225 (2015).Article 
    ADS 
    CAS 

    Google Scholar 
    Maher, D. T., Cowley, K., Santos, I. R., Macklin, P. & Eyre, B. D. Methane and carbon dioxide dynamics in a subtropical estuary over a diel cycle: Insights from automated in situ radioactive and stable isotope measurements. Mar. Chem. 168, 69–79 (2015).Article 
    CAS 

    Google Scholar 
    Attard, K. M. et al. Seasonal metabolism and carbon export potential of a key coastal habitat: The perennial canopy-forming macroalga Fucus vesiculosus. Limnol. Oceanogr. 64, 149–164 (2019).Article 
    ADS 

    Google Scholar 
    Attard, K. M. et al. Seasonal ecosystem metabolism across shallow benthic habitats measured by aquatic eddy covariance. Limnol. Oceanogr. Lett. 4, 79–86 (2019).Article 

    Google Scholar 
    Trevathan-Tackett, S. M. et al. Comparison of marine macrophytes for their contributions to blue carbon sequestration. Ecology 96, 3043–3057 (2015).Article 

    Google Scholar 
    Pessarrodona, A. et al. Global seaweed productivity. Sci. Adv. 8, eabn2465 (2022).Machado, L., Magnusson, M., Paul, N. A., de Nys, R. & Tomkins, N. Effects of marine and freshwater macroalgae on in vitro total gas and methane production. PLoS ONE 9, e85289 (2014).Article 
    ADS 

    Google Scholar 
    Hansson, G. Methane production from marine, green macro-algae. Resour. Conserv. 8, 185–194 (1983).Article 
    CAS 

    Google Scholar 
    Björk, M., Rosenqvist, G., Gröndahl, F. & Bonaglia, S. Methane emissions from macrophyte beach wrack on Baltic seashores. Ambio 52, 171–181 (2023).Article 

    Google Scholar 
    Lundevall-Zara, M., Lundevall-Zara, E. & Brüchert, V. Sea-air exchange of methane in shallow inshore areas of the Baltic sea. Front. Mar. Sci. 8, 1–20 (2021).Article 

    Google Scholar 
    Yvon-Durocher, G. et al. Methane fluxes show consistent temperature dependence across microbial to ecosystem scales. Nature 507, 488–491 (2014).Article 
    ADS 
    CAS 

    Google Scholar 
    Roth, F. et al. High spatiotemporal variability of methane concentrations challenges estimates of emissions across vegetated coastal ecosystems. Glob. Chang. Biol. https://doi.org/10.1111/gcb.16177 (2022).Koweek, D. A. et al. A year in the life of a central California kelp forest: physical and biological insights into biogeochemical variability. Biogeosciences 14, 31–44 (2017).Article 
    ADS 
    CAS 

    Google Scholar 
    Watanabe, K. et al. Macroalgal metabolism and lateral carbon flows can create significant carbon sinks. Biogeosciences 17, 2425–2440 (2020).Article 
    ADS 
    CAS 

    Google Scholar 
    Möller, P., Pihl, L. & Rosenberg, R. Benthic faunal energy flow and biological interaction in some shallow marine soft bottom habitats. Mar. Ecol. Prog. Ser. 27, 109–121 (1985).Article 
    ADS 

    Google Scholar 
    Frigstad, H. et al. Blue Carbon – Climate Adaptation, CO2 Uptake And Sequestration Of Carbon In Nordic Blue Forests – Results From The Nordic Blue Carbon Project. (Nordic Council of Ministers, 2021).Ikawa, H. & Oechel, W. C. Temporal variations in air-sea CO 2 exchange near large kelp beds near San Diego, California. J. Geophys. Res. Ocean. 120, 50–63 (2015).Article 
    ADS 
    CAS 

    Google Scholar 
    Reeburgh, W. S. Oceanic methane biogeochemistry. Chem. Rev. 107, 486–513 (2007).Article 
    CAS 

    Google Scholar 
    Davidson, E. A. & Janssens, I. A. Temperature sensitivity of soil carbon decomposition and feedbacks to climate change. Nature 440, 165–173 (2006).Article 
    ADS 
    CAS 

    Google Scholar 
    Oreska, M. P. J. et al. The greenhouse gas offset potential from seagrass restoration. Sci. Rep. 10, 1–15 (2020).Article 

    Google Scholar 
    Asplund, M. E. et al. Methane emissions from nordic seagrass meadow sediments. Front. Mar. Sci. 8, 811533 (2022).Article 

    Google Scholar 
    Schorn, S., Ahmerkamp, S., Bullock, E., Weber, M. & Lott, C. Diverse methylotrophic methanogenic archaea cause high methane emissions from seagrass meadows. Proc. Natl Acad. Sci. USA. https://doi.org/10.1073/pnas.2106628119/-/DCSupplemental.Published (2022).Koebsch, F., Glatzel, S. & Jurasinski, G. Vegetation controls methane emissions in a coastal brackish fen. Wetl. Ecol. Manag. 21, 323–337 (2013).Article 
    CAS 

    Google Scholar 
    Sansone, F. J. & Martens, C. S. Methane production from acetate and associated methane fluxes from anoxic coastal sediments. Science (80-). 211, 707–709 (1981).Article 
    ADS 
    CAS 

    Google Scholar 
    Egger, M. et al. Rapid sediment accumulation results in high methane effluxes from coastal sediments. PLoS ONE 11, e0161609 (2016).Article 

    Google Scholar 
    Hamdan, L. J. & Wickland, K. P. Methane emissions from oceans, coasts, and freshwater habitats: New perspectives and feedbacks on climate. Limnol. Oceanogr. 61, S3–S12 (2016).Article 
    ADS 

    Google Scholar 
    Cai, M. et al. Metatranscriptomics reveals different features of methanogenic archaea among global vegetated coastal ecosystems. Sci. Total Environ. 802, 149848 (2022).Article 
    ADS 
    CAS 

    Google Scholar 
    Evans, P. N. et al. Methane metabolism in the archaeal phylum Bathyarchaeota revealed by genome-centric metagenomics. Science (80-). 350, 434–438 (2015).Article 
    ADS 
    CAS 

    Google Scholar 
    Zhang, C.-J., Pan, J., Liu, Y., Duan, C.-H. & Li, M. Genomic and transcriptomic insights into methanogenesis potential of novel methanogens from mangrove sediments. Microbiome 8, 94 (2020).Article 
    CAS 

    Google Scholar 
    Hilt, S., Grossart, H., McGinnis, D. F. & Keppler, F. Potential role of submerged macrophytes for oxic methane production in aquatic ecosystems. Limnol. Oceanogr. https://doi.org/10.1002/lno.12095 (2022).Söllinger, A. & Urich, T. Methylotrophic methanogens everywhere — physiology and ecology of novel players in global methane cycling. Biochem. Soc. Trans. 47, 1895–1907 (2019).Article 

    Google Scholar 
    Karl, D. M. et al. Aerobic production of methane in the sea. Nat. Geosci. 1, 473–478 (2008).Article 
    ADS 
    CAS 

    Google Scholar 
    McGenity, T. J. & Sorokin, D. Y. Handbook of Hydrocarbon and Lipid Microbiology. p. 665–680 (Springer, 2010).Murray, B. C., Pendleton, L., Jenkins, W. A. & Sifleet, S. Green Payments for Blue Carbon Economic Incentives for Protecting Threatened Coastal Habitats (Nicholas Institute for Environmental Policy Solutions, 2011).Kuwae, T., Watanabe, A., Yoshihara, S., Suehiro, F. & Sugimura, Y. Implementation of blue carbon offset crediting for seagrass meadows, macroalgal beds, and macroalgae farming in Japan. Mar. Policy 138, 104996 (2022).Article 

    Google Scholar 
    Medvedev, I. P., Rabinovich, A. B. & Kulikov, E. A. Tides in three enclosed basins: the Baltic, Black, and Caspian Seas. Front. Mar. Sci. 3, 46 (2016).Article 

    Google Scholar 
    Haugen, D. A. Workshop on Micrometeorology (American Meteorological Society, 1973).Weiss, R. F. Carbon dioxide in water and seawater: the solubility of a non-ideal gas. Mar. Chem. 2, 203–215 (1974).Article 
    CAS 

    Google Scholar 
    Wiesenburg, D. A. & Guinasso, N. L. Equilibrium solubilities of methane, carbon monoxide, and hydrogen in water and sea water. J. Chem. Eng. Data 24, 356–360 (1979).Article 
    CAS 

    Google Scholar 
    Wanninkhof, R. Relationship between wind speed and gas exchange over the ocean revisited. Limnol. Oceanogr. Methods 12, 351–362 (2014).Article 

    Google Scholar 
    Gülzow, W. et al. One year of continuous measurements constraining methane emissions from the Baltic Sea to the atmosphere using a ship of opportunity. Biogeosciences 10, 81–99 (2013).Article 
    ADS 

    Google Scholar 
    Jähne, B. et al. On the parameters influencing air-water gas exchange. J. Geophys. Res. 92, 1937 (1987).Article 
    ADS 

    Google Scholar 
    Bonaglia, S. et al. Meiofauna improve oxygenation and accelerate sulfide removal in the seasonally hypoxic seabed. Mar. Environ. Res. 159, 104968 (2020).Article 
    CAS 

    Google Scholar 
    Parada, A. E., Needham, D. M. & Fuhrman, J. A. Every base matters: assessing small subunit rRNA primers for marine microbiomes with mock communities, time series and global field samples. Environ. Microbiol. 18, 1403–1414 (2016).Article 
    CAS 

    Google Scholar 
    Apprill, A., McNally, S., Parsons, R. & Weber, L. Minor revision to V4 region SSU rRNA 806R gene primer greatly increases detection of SAR11 bacterioplankton. Aquat. Microb. Ecol. 75, 129–137 (2015).Article 

    Google Scholar 
    St John, J. SeqPrep. https://github.com/jstjohn/SeqPrep (2011).Callahan, B. J. et al. DADA2: high-resolution sample inference from Illumina amplicon data. Nat. Methods 13, 581–583 (2016).Article 
    CAS 

    Google Scholar 
    R Core Team. R: A Language And Environment For Statistical Computing (R Foundation for Statistical Computing, 2021).Andrews, S. FastQC: A Quality Control Tool For High Throughput Sequence Data (2010).Ewels, P., Magnusson, M., Lundin, S. & Käller, M. MultiQC: summarize analysis results for multiple tools and samples in a single report. Bioinformatics 32, 3047–3048 (2016).Article 
    CAS 

    Google Scholar 
    Quast, C. et al. The SILVA ribosomal RNA gene database project: improved data processing and web-based tools. Nucleic Acids Res 41, D590–D596 (2012).Article 

    Google Scholar 
    Robertson, C. E. et al. Explicet: graphical user interface software for metadata-driven management, analysis and visualization of microbiome data. Bioinformatics 29, 3100–3101 (2013).Article 
    CAS 

    Google Scholar 
    Coolen, M. J. L. et al. Evolution of the methane cycle in Ace Lake (Antarctica) during the Holocene: response of methanogens and methanotrophs to environmental change. Org. Geochem. 35, 1151–1167 (2004).Article 
    CAS 

    Google Scholar 
    Wobbrock, J. O., Findlater, L., Gergle, D. & Higgins, J. J. The aligned rank transform for nonparametric factorial analyses using only anova procedures. in Proceedings of the SIGCHI Conference on Human Factors in Computing Systems 143–146 (ACM, 2011).Hammer, Ø., Harper, D. & Ryan, P. PAST: paleontological statistics software package for education and data analysis. Palaeontol. Electron. 4, 1–9 (2001). More