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    Species characteristics and cultural value of stone wall trees in the urban area of Macao

    Species composition of stone wall treesFamilies and genera of stone wall treesThere were 96 stone wall trees belonging to 6 genera and 5 families in Macao. Among them, Moraceae and Ficus appeared the most frequently, both reaching 85 times, accounting for 88.5% (Table 1). It showed that Moraceae, a kind of tropical distribution family, was dominant in the stone wall trees communities, which meant that stone wall trees species in Macao appeared distinctly tropical nature18.Table 1 Frequency of occurrence of stone wall Trees in different families and genera.Full size tableSpecies of stone wall treesThere were 16 species of the stone wall trees in Macao including Bridelia tomentosa, Celtis sinensis, Eriobotrya japonica, Ficus altissima, F. benjamina, F. elastica, F. hispida, F. microcarpa, F. pandurata, F. subpisocarpa, F. tinctoria subsp. gibbosa, F. rumphii, F. variegata, F. virens, Leucaena leucocephala, and Trema cannabina (Fig. 2).Figure 216 species of stone wall trees in Macao (photo was taken by Professor Qin Xingsheng).Full size imageBased on the frequency of occurrence of various tree species, the frequency was concentrated in the range of 1–5%. Among them, Ficus microcarpa had the highest frequency, reaching 58 times, with a frequency of 60.4% (Fig. 3). This tree species is robust, adaptable and fast growing, which is the main population of Ficus19.Figure 3Frequency distribution of stone wall tree species in Macao.Full size imageStone wall trees in the historic center of MacaoThe historic center of Macao, covering an area of about 2.8 km2, is the heartland of Macao’s historical and cultural heritage, which plays a significant role in the cultural heritage around the world18. The historic center of Macao provides valuable historical and cultural resources that enable Macao to transform into a world tourism center20.A total of 14 plots were located in the historic Center of Macao (Fig. 4), with 45 stone wall trees, accounting for 47.9% of the total number of trees in the survey. Among them, Jardim Luís de Camões has the largest number of 9 stone wall trees. The park, built in the mid-eighteenth century, is one of the oldest gardens in Macao and has the largest number of old trees in Macao. The park had provided good time and environmental conditions for the growth of stone wall trees.Figure 4(a) Schematic diagram of distribution and number of stone wall trees in the historic Center of Macao. (b) Schematic diagram of historic center of Macao. (URL of the Macao map: https://www.d-maps.com/m/asia/china/macau/macau02.gif).Full size imageAccording to Decree No. 56/84/M of the Macao Special Administrative Region Government Printing Department, immovable property that represents the creation of man, or the development of nature or technology and has cultural significance is considered tangible cultural property. The occurrence of the stone wall tree was inextricably linked to ancient wall-building techniques of that time, which was of great significance for the study of the technological development and ecological landscape of the historic center of Macao. The concept of “historic urban landscape” was proposed by Zhang Song20, who argued that cities were organisms in continuous evolution, emphasizing respect for the interrelationship between natural and man-made environments. The stone wall trees in the historic center of Macao have been associated with the local culture and ecology tightly and should be preserved as important urban landscape.Symbiotic relationship between tree and stone wallsAs shown in the table below (Table 2), it was found that most of the stone wall trees had root systems that were not only superficially attached to the wall but also extended to the top or bottom of the wall. In particular, Ficus spp. whose strong root system could closely mosaic with the wall, thus forming a strong symbiosis.Table 2 The relationship between the root system of the stone wall tree and the wall.Full size tableStone walls can imitate the traditional nature-accommodating features to permit spontaneous establishment of a diverse plant assemblage. Besides vegetative diversities in terms of species composition, growth form and biomass structure, stone walls can support a mass collection of urban wildlife and provide various ecosystem service. It is highly recommended that modern urban design be created to embrace stone wall landscape as an integral part of naturalistic or ecological design.Vision for the establishment of the stone wall tree trail system in the historic of MacaoThe traditional street environment in the Macao Peninsula is a kind of distinctive urban landscape, which can highlight the specificity and value of the urban context. The combination of the stone wall trees and walls, together with the traditional streets, form a spatial urban landscape. Starting from the location of the stone wall tree landscape, the dots and lines are prospective to promote the establishment of a comprehensive stone wall tree landscape trail system (Fig. 5), so that the public can make use of the existing biological resources to have a better understanding of the land on which they live.Figure 5Schematic diagram of the stone wall trees trail system on the Macao Peninsula (URL of the Macao map: https://www.d-maps.com/m/asia/china/macau/macau02.gif and the finished map is created by Meisi Chen through the Photoshop CS6 and Arc GIS 10.2).Full size imageSince 2012, the Macao Government has been implementing the “Strolling along Macao Street” project, which aims at studying and exploring the history and culture of the streets of Macao through an in-depth cultural tourism route and promoting it to different levels of society. The establishment of the stone wall tree trail system can rely on this project to raise the public’s awareness of the protection and cultural identity of the stone wall tree landscape through a variety of ways. For example, route design competition, photography competition and exhibition, recruitment of “Stonewall Tree Protection Ambassadors” and other forms of participation, so that the public could complete the “role change” in the high degree of such participation—from “onlookers” to “bystanders”.Survey results of associated plant speciesSpecies composition and occurrence of frequencyThe survey showed that there were 101 species of stone wall tree associated plants in Macao, under 88 genera and 51 families. Most associated plants belonged to Euphorbiaceae, Compositae, and Araceae.There were 85 species with a frequency of 1–5 times, accounting for 84.2% of total species. A total of 11 species appeared 11–15 times, accounting for 4.0% (Fig. 6). There were a total of 4 species that appeared more than 15 times. They were Cocculus orbiculatus, Pteris cretica, Paederia scandens, and Pyrrosia adnascens. Most of the associated species appeared only 1–5 times, indicating that most plants were selective and accidental for the growth conditions of stone wall sites.Figure 6Occurrence frequency in various species of associated plants.Full size imageLife form compositionHerbaceous plants with 37 species, accounting the percentage of 52.3% (Fig. 7), were dominant in the associated plant species because the seeds of herbaceous plants are lighter and can be propagated to the wall surface by wind force.Figure 7Life form of associated plants with stone wall trees in Macao.Full size imageSimilarity analysis of the associated plants in MacaoIn order to compare the similarity of associated plant species in different environment, the surveyed sample sites for this study were divided into three categories: motorized lanes, non-motorized lanes, and park habitats (Table 3). According to Jaccard’s similarity principle, Sj is extremely dissimilar when it is 0.00–0.25, and the analysis showed that the similarity of companion plant species in all three habitats was extremely dissimilar. Therefore, it indicated that the companion plants in different habitats had obvious diversity and uniqueness.Table 3 Jaccard similarity index for companion plant species composition among three habitats.Full size table More

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    Integrated molecular and behavioural data reveal deep circadian disruption in response to artificial light at night in male Great tits (Parus major)

    ALAN advances timing of activity and BMAL1 expressionDaily cycles of activity were strongly affected by the ALAN treatment (GAMM, p = 0.001, Fig. 2A and Fig. S2; Table S4). In the 5 lux group birds were generally active 6–7 h before lights-on, whereas birds in the other two light treatments (0.5 and 1.5 lux) advanced morning activity to a much lesser extent. Because of the advanced onset of activity, 40% of the overall diel activity in the 5 lux group occurred during the night, compared to 11 and 14% in the 0.5 and 1.5 lux groups, and less than 1% in the control dark group. Thus, with increasing ALAN, nocturnal activity also increased (LMM, treatment p  0.1 for pairwise comparison), and thereafter their timing remained stable. The group exposed to 5 lux showed a much larger instantaneous phase advance of almost five hours (mean ± SEM = 289 ± 21 min), and thereafter continued to gradually phase-advance until reaching a stable phase after 10 days (interaction treatment × day, p  More

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    Richard Leakey (1944–2022)

    OBITUARY
    28 January 2022

    Richard Leakey (1944–2022)

    Palaeontologist of human origins, conservationist and politician.

    Marta Mirazón Lahr

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    Marta Mirazón Lahr

    Marta Mirazón Lahr is professor of human evolutionary biology and prehistory at the University of Cambridge, UK. Leakey was a friend, colleague and supporter of her work in Turkana, where she directs research in human origins.

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    Credit: William Campbell/Sygma/Getty

    Richard Leakey made palaeontological discoveries of lasting significance, and brought animal poaching to the world’s attention. His fossil finds at Koobi Fora on the shores of Lake Turkana, Kenya, transformed our understanding of the diversity of human ancestors. He directed Kenya’s national museum, reorganized the country’s wildlife services and headed Kenya’s civil service. He died aged 77, at home in the Ngong Hills, Kenya.In science, he liked exploration, big-picture problems and building institutions. He made huge strides in conservation, empowering organizations and deploying shock tactics. He entered politics, creating an opposition party, then worked in government, finally becoming its corruption watchdog. He mentored young Kenyan scholars, conservationists and artists who are now leaders in their field.Born in Nairobi, Richard was the middle child of pioneers in African palaeontology and archaeology Louis and Mary Leakey. He abandoned school at 16 to open an animal-trapping and safari business, earning enough to pay for flying lessons and his own small plane. In 1963, a mix of interest in his parents’ world and a wish to prove himself to them lured him into the study of the past, and he found his first important hominin fossil — a 1.5 million-year-old mandible of Paranthropus boisei — in 1964.
    Fifty years after Homo habilis
    In 1967, Leakey’s father asked him to direct an expedition to the Omo Valley of southern Ethiopia. There, Leakey found two Homo sapiens fossils now known to be 230,000 years old (C. M. Vidal et al. Nature https://doi.org/gn3794; 2022), key evidence of our species’ African origins. Flying over the eastern shore of Lake Turkana, he recognized the potential of sediments at Koobi Fora, which proved to be a trove of hominin fossils. The discovery of different hominin species living at the same time between 2 million and 1.5 million years ago (P. boisei, Homo habilis, Homo rudolfensis and Homo erectus) changed views of how humans evolved.In 1968, Leakey became director of the National Museums of Kenya, which became a hub of thriving research. Soon afterwards, he met the young British zoologist Meave Epps. They married after his first marriage ended, and became life-long personal and scientific partners. Their work with researchers dubbed the Hominid Gang, led by Kamoya Kimeu, resulted in the discovery of dozens of hominin fossils, including a new genus and four new species (Paranthropus aethiopicus, Australopithecus anamensis and Kenyanthropus platyops, as well as H. rudolfensis). A 1.6-million-year-old skeleton of a juvenile H. erectus proved to have grown more slowly than apes and faster than humans, giving insights into the evolution of human life-history.Leakey became involved in acrimonious scientific arguments — sometimes he was right, sometimes not — which, during the 1970s, gave an antagonistic tone to human-origins research. His health deteriorated, and he had his first kidney transplant (donated by his brother Philip) in 1980. In 1989, Kenya’s president, Daniel arap Moi, asked him to run the Kenya Wildlife Service (KWS). Leakey declared war on poachers, burnt the stockpile of Kenyan ivory and massively reduced elephant deaths. His controversial tactics had an impact on a web of corrupt practices and created serious enemies. In 1993, the plane he was piloting crashed; both his legs had to be amputated below the knee. Sabotage was rumoured.
    Human evolution’s ties to tectonics
    The relationship with Moi became increasingly hostile. In 1995, Leakey left KWS to create an opposition party, Safina, becoming a member of the Kenyan parliament in 1998. His time in opposition was tense. Leakey was beaten and received death threats. But Kenya needed large investments, and funders demanded assurances. Capitalizing on Leakey’s reputation for integrity, in 1998 Moi asked him to direct KWS again, and in 1999 to head the civil service. Over three years, Leakey raised hundreds of millions of dollars for Kenya and fought corruption.In 2002, he accepted a position at Stony Brook University, New York, that allowed him to live in Kenya and create the Turkana Basin Institute (TBI), which he chaired from 2005 until his death. TBI fostered a burst of discoveries: Miocene primates, hominins, the oldest stone tools in the world at 3.3 million years, evidence of prehistoric warfare, and the earliest monumental architecture in sub-Saharan Africa. In 2004, Leakey founded WildlifeDirect, a non-governmental conservation body, serving on its board for 10 years. In 2007, he became chair of Transparency International Kenya, continuing his battle against corruption.By this time, Leakey had skin cancer and progressively worse health. He underwent a second kidney transplant in 2006, with Meave as the donor, and a liver transplant in 2013. Yet, in 2015, he accepted President Uhuru Kenyatta’s request to return to KWS as chair until 2018. For the past six years, he worked to create a new Kenyan museum, called Ngaren — of which I am a board member — to celebrate science, evolution and humanity’s African origins.Richard was special — fun, insightful, generous, with a sharp sense of humour, and a fabulous cook and sommelier. He embraced life, good and bad, and imbued those around him with the sheer excitement of what could be done, discovered, resolved and enjoyed.

    Nature 602, 29 (2022)
    doi: https://doi.org/10.1038/d41586-022-00211-6

    Competing Interests
    M.M.L. is a member of the board of directors of Ngaren, a non-governmental organization founded by Richard Leakey to support the creation of a museum of evolution in Kenya.

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    Syntax errors do not disrupt acoustic communication in the common cuckoo

    Study areaThe study was conducted in central Hungary, ca. 25–60 km south of Budapest, at around the settlements Alsónémedi (47°18′; 19°09′), Apaj (47°06′; 19°05′), Kunszentmiklós (47°01′; 19°07′) and Tass (47°01′; 19°01′) during the 2020 and 2021 breeding seasons. We also used heterospecific controls with Eurasian collared doves for comparisons conducted in the year 2016. In this study area common cuckoos can be found in high densities in their breeding season (May and June). They almost exclusively parasitize great reed warblers (Acrocephalus arundinaceus) locally, a large host which breeds in narrow reed-beds along small irrigation and flood-relief channels47.All applicable international, national, and/or institutional guidelines for the care and use of animals were followed. Local animal ethics regulations and agreements were followed for fieldwork. All work complied with the Hungarian laws, and the Middle-Danube-Valley Inspectorate for Environmental Protection, Nature Conservation and Water Management, Budapest, provided permission for research (permit no. PE/KTF/17190-3/2015).Playback filesWe used cuckoo calls recorded in May between 2016 and 2019. Recording were made with a Telinga Universal parabola dish, equipped with a Sennheiser ME-62 microphone, a K6 powering module, a FEL MX mono preamp, and a Marantz PMD-620 MKII recorder (sampling rate: 48 kHz, 24-bit quality)30.We constructed ten different sound files for playback from the basic “cu-coo” calls:Heterospecific (negative) control(1) The calls of a neutral species from the local avifauna, the Eurasian collared dove, were used for interspecific vocalization control.Natural (positive) control(2) Normal (natural) “cu-coo” calls.Experimental treatments; one-note calls(3) Deleting the second note, i.e. contained “cu”, only.(4) Deleting the first note, i.e. contained “coo”, only.Two-note calls(5) Reversal of the basic “cu-coo” call, i.e. “coo-cu”.(6) Repeating the first note, and deleting the second note, i.e. “cu-cu”.(7) Repeating the second note, and deleting the first note, i.e. “coo-coo”.Three-note calls(8) Repeating the first note, i.e. “cu-cu-coo”.(9) Repeating the second note. i.e. “cu-coo-coo”.Three-note natural(10) Normal (but rare and context specific) “nat. cu-cu-coo”.The experimental 3-note variant of the calls (“cu-cu-coo”; call type No. (8)) differs from our natural 3-note calls (“nat. cu-cu-coo”; call type No. (10)) in two out of the three acoustic parameters (length: F1,18 = 79.258, P  More

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    Phylogenetic divergence and adaptation of Nitrososphaeria across lake depths and freshwater ecosystems

    1.Rinke C, Chuvochina M, Mussig AJ, Chaumeil P-A, Davín AA, Waite DW, et al. A standardized archaeal taxonomy for the Genome Taxonomy Database. Nat Microbiol. 2021;6:946–59.CAS 
    PubMed 

    Google Scholar 
    2.Karner MB, DeLong EF, Karl DM. Archaeal dominance in the mesopelagic zone of the Pacific Ocean. Nature. 2001;409:507–10.CAS 
    PubMed 

    Google Scholar 
    3.Buckley DH, Graber JR, Schmidt TM. Phylogenetic analysis of nonthermophilic members of the kingdom Crenarchaeota and their diversity and abundance in soils. Appl Environ Microbiol. 1998;64:4333–9.CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    4.Casamayor EO, Schäfer H, Bañeras L, Pedrós-Alió C, Muyzer G. Identification of and spatio-temporal differences between microbial assemblages from two neighboring sulfurous lakes: Comparison by microscopy and denaturing gradient gel electrophoresis. Appl Environ Microbiol. 2000;66:499–508.CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    5.Francis CA, Roberts KJ, Beman JM, Santoro AE, Oakley BB. Ubiquity and diversity of ammonia-oxidizing Archaea in water columns and sediments of the ocean. Proc Natl Acad Sci USA. 2005;102:14683–8.CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    6.Stahl DA, de la Torre JR. Physiology and diversity of ammonia-oxidizing archaea. Annu Rev Microbiol. 2012;66:83–101.CAS 
    PubMed 

    Google Scholar 
    7.DeLong EF. Archaea in coastal marine environments. Proc Natl Acad Sci USA. 1992;89:5685–9.CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    8.Fuhrman JA, McCallum K, Davis AA. Novel major archaebacterial group from marine plankton. Nature. 1992;356:148–9.CAS 
    PubMed 

    Google Scholar 
    9.Qin W, Zheng Y, Zhao F, Wang Y, Urakawa H, Martens-Habbena W, et al. Alternative strategies of nutrient acquisition and energy conservation map to the biogeography of marine ammonia-oxidizing archaea. ISME J. 2020;14:2596–609.
    Google Scholar 
    10.Aylward FO, Santoro AE. Heterotrophic Thaumarchaea with small genomes are widespread in the dark ocean. mSystems. 2020;5:e00415–00420.CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    11.Reji L, Francis CA. Metagenome-assembled genomes reveal unique metabolic adaptations of a basal marine Thaumarchaeota lineage. ISME J. 2020;14:2105–15.CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    12.Wang Y, Huang J-M, Cui G-J, Nunoura T, Takaki Y, Li W-L, et al. Genomics insights into ecotype formation of ammonia-oxidizing archaea in the deep ocean. Environ Microbiol. 2019b;21:716–29.CAS 
    PubMed 

    Google Scholar 
    13.Zhong H, Lehtovirta-Morley L, Liu J, Zheng Y, Lin H, Song D, et al. Novel insights into the Thaumarchaeota in the deepest oceans: their metabolism and potential adaptation mechanisms. Microbiome. 2020;8:78.PubMed 
    PubMed Central 

    Google Scholar 
    14.Wang B, Qin W, Ren Y, Zhou X, Jung M-Y, Han P, et al. Expansion of Thaumarchaeota habitat range is correlated with horizontal transfer of ATPase operons. ISME J. 2019a;13:3067–79.CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    15.Sheridan PO, Raguideau S, Quince C, Holden J, Zhang L, Gaze WH, et al. Gene duplication drives genome expansion in a major lineage of Thaumarchaeota. Nat Commun. 2020;11:5494.CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    16.Ren M, Feng X, Huang Y, Wang H, Hu Z, Clingenpeel S, et al. Phylogenomics suggests oxygen availability as a driving force in Thaumarchaeota evolution. ISME J. 2019;13:2150–61.PubMed 
    PubMed Central 

    Google Scholar 
    17.Yang Y, Zhang C, Lenton TM, Yan X, Zhu M, Zhou M, et al. The evolution pathway of ammonia-oxidizing archaea shaped by major geological events. Mol Biol Evol. 2021;38:3637–48.PubMed 
    PubMed Central 

    Google Scholar 
    18.Alves RJE, Minh BQ, Urich T, von Haeseler A, Schleper C. Unifying the global phylogeny and environmental distribution of ammonia-oxidising archaea based on amoA genes. Nat Commun. 2018;9:1517.PubMed 
    PubMed Central 

    Google Scholar 
    19.Llirós M, Casamayor EO, Borrego C. High archaeal richness in the water column of a freshwater sulfurous karstic lake along an interannual study. FEMS Microbiol Ecol. 2008;66:331–42.PubMed 

    Google Scholar 
    20.Wang Z, Wang Z, Huang C, Pei Y. Vertical distribution of ammonia-oxidizing archaea (AOA) in the hyporheic zone of a eutrophic river in North China. World J Microbiol Biotechnol. 2014;30:1335–46.CAS 
    PubMed 

    Google Scholar 
    21.Mußmann M, Brito I, Pitcher A, Sinninghe Damsté JS, Hatzenpichler R, Richter A, et al. Thaumarchaeotes abundant in refinery nitrifying sludges express amoA but are not obligate autotrophic ammonia oxidizers. Proc Natl Acad Sci USA. 2011;108:16771–6.PubMed 
    PubMed Central 

    Google Scholar 
    22.Biller S, Mosier A, Wells G, Francis C. Global biodiversity of aquatic ammonia-oxidizing archaea is partitioned by habitat. Front Microbiol. 2012;3:252.23.Beman JM, Francis CA. Diversity of ammonia-oxidizing archaea and bacteria in the sediments of a hypernutrified subtropical estuary: Bahia del Tobari, Mexico. Appl Environ Microbiol. 2006;72:7767–77.CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    24.Auguet J-C, Nomokonova N, Camarero L, Casamayor EO. Seasonal changes of freshwater ammonia-oxidizing archaeal assemblages and nitrogen species in oligotrophic alpine lakes. Appl Environ Microbiol. 2011;77:1937–45.CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    25.Small GE, Bullerjahn GS, Sterner RW, Beall BFN, Brovold S, Finlay JC, et al. Rates and controls of nitrification in a large oligotrophic lake. Limnol Oceanogr. 2013;58:276–86.CAS 

    Google Scholar 
    26.Herber J, Klotz F, Frommeyer B, Weis S, Straile D, Kolar A, et al. A single Thaumarchaeon drives nitrification in deep oligotrophic Lake Constance. Environ Microbiol. 2020;22:212–28.CAS 
    PubMed 

    Google Scholar 
    27.Auguet J-C, Triadó-Margarit X, Nomokonova N, Camarero L, Casamayor EO. Vertical segregation and phylogenetic characterization of ammonia-oxidizing Archaea in a deep oligotrophic lake. ISME J. 2012;6:1786–97.CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    28.Podowski JC, Paver SF, Newton RJ, Coleman ML. Genome streamlining, proteorhodopsin, and organic nitrogen metabolism in freshwater nitrifiers. bioRxiv. 2021;2021.2001.2019.427344.29.Gohl DM, Vangay P, Garbe J, MacLean A, Hauge A, Becker A, et al. Systematic improvement of amplicon marker gene methods for increased accuracy in microbiome studies. Nat Biotech. 2016;34:942–9.CAS 

    Google Scholar 
    30.Restrepo-Ortiz CX, Auguet J-C, Casamayor EO. Targeting spatiotemporal dynamics of planktonic SAGMGC-1 and segregation of ammonia-oxidizing thaumarchaeota ecotypes by newly designed primers and quantitative polymerase chain reaction. Environ Microbiol. 2014;16:689–700.CAS 
    PubMed 

    Google Scholar 
    31.Liu S, Wang H, Chen L, Wang J, Zheng M, Liu S, et al. Comammox Nitrospira within the Yangtze River continuum: community, biogeography, and ecological drivers. ISME J. 2020;14:2488–504.CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    32.Santos-Júnior CD, Sarmento H, de Miranda FP, Henrique-Silva F, Logares R. Uncovering the genomic potential of the Amazon River microbiome to degrade rainforest organic matter. Microbiome. 2020;8:151.PubMed 
    PubMed Central 

    Google Scholar 
    33.Jung M-Y, Sedlacek CJ, Kits KD, Mueller AJ, Rhee S-K, Hink L, et al. Ammonia-oxidizing archaea possess a wide range of cellular ammonia affinities. ISME J. 2022;16:272–83.CAS 
    PubMed 

    Google Scholar 
    34.Kim BK, Jung MY, Yu DS, Park SJ, Oh TK, Rhee SK, et al. Genome sequence of an ammonia-oxidizing soil archaeon, “Candidatus Nitrosoarchaeum koreensis” MY1. J Bacteriol. 2011;193:5539–40.CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    35.Jung MY, Park SJ, Kim SJ, Kim JG, Sinninghe Damste JS, Jeon CO, et al. A mesophilic, autotrophic, ammonia-oxidizing archaeon of thaumarchaeal group I.1a cultivated from a deep oligotrophic soil horizon. Appl Environ Microbiol. 2014;80:3645–55.PubMed 
    PubMed Central 

    Google Scholar 
    36.Lebedeva EV, Hatzenpichler R, Pelletier E, Schuster N, Hauzmayer S, Bulaev A, et al. Enrichment and genome sequence of the group i.1a ammonia-oxidizing archaeon “Ca. Nitrosotenuis uzonensis” representing a clade globally distributed in thermal habitats. PLoS One. 2013;8:e80835.PubMed 
    PubMed Central 

    Google Scholar 
    37.Li Y, Ding K, Wen X, Zhang B, Shen B, Yang Y. A novel ammonia-oxidizing archaeon from wastewater treatment plant: Its enrichment, physiological and genomic characteristics. Sci Rep. 2016;6:23747.CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    38.Sauder LA, Albertsen M, Engel K, Schwarz J, Nielsen PH, Wagner M, et al. Cultivation and characterization of Candidatus Nitrosocosmicus exaquare, an ammonia-oxidizing archaeon from a municipal wastewater treatment system. ISME J. 2017;11:1142–57.CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    39.Wang Y, Qin W, Jiang X, Ju F, Mao Y, Zhang A, et al. Seasonal prevalence of ammonia-oxidizing archaea in a full-scale municipal wastewater treatment plant treating saline wastewater revealed by a 6-year time-series analysis. Environ Sci Technol. 2021;55:2662–73.CAS 
    PubMed 

    Google Scholar 
    40.Xing P, Tao Y, Luo J, Wang L, Li B, Li H, et al. Stratification of microbiomes during the holomictic period of Lake Fuxian, an alpine monomictic lake. Limnol Oceanogr. 2020;65:S134–S148.
    Google Scholar 
    41.Cabello-Yeves PJ, Zemskaya TI, Rosselli R, Coutinho FH, Zakharenko AS, Blinov VV, et al. Genomes of novel microbial lineages assembled from the sub-ice waters of Lake Baikal. Appl Environ Microbiol. 2017;84:e02132–02117.PubMed 
    PubMed Central 

    Google Scholar 
    42.Cabello-Yeves PJ, Zemskaya TI, Zakharenko AS, Sakirko MV, Ivanov VG, Ghai R et al. Microbiome of the deep Lake Baikal, a unique oxic bathypelagic habitat. Limnol Oceanogr. 2020;65:1471–88.43.Qin W, Amin SA, Martens-Habbena W, Walker CB, Urakawa H, Devol AH, et al. Marine ammonia-oxidizing archaeal isolates display obligate mixotrophy and wide ecotypic variation. Proc Natl Acad Sci USA. 2014;111:12504–9.CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    44.Bayer B, Vojvoda J, Offre P, Alves RJ, Elisabeth NH, Garcia JA, et al. Physiological and genomic characterization of two novel marine thaumarchaeal strains indicates niche differentiation. ISME J. 2016;10:1051–63.CAS 
    PubMed 

    Google Scholar 
    45.Bristow LA, Dalsgaard T, Tiano L, Mills DB, Bertagnolli AD, Wright JJ, et al. Ammonium and nitrite oxidation at nanomolar oxygen concentrations in oxygen minimum zone waters. Proc Natl Acad Sci USA. 2016;113:10601–6.CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    46.Hink L, Gubry-Rangin C, Nicol GW, Prosser JI. The consequences of niche and physiological differentiation of archaeal and bacterial ammonia oxidisers for nitrous oxide emissions. ISME J. 2018;12:1084–93.CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    47.Martens-Habbena W, Berube PM, Urakawa H, de la Torre JR, Stahl DA. Ammonia oxidation kinetics determine niche separation of nitrifying Archaea and Bacteria. Nature. 2009;461:976–9.CAS 
    PubMed 

    Google Scholar 
    48.Mayr MJ, Zimmermann M, Guggenheim C, Brand A, Bürgmann H. Niche partitioning of methane-oxidizing bacteria along the oxygen–methane counter gradient of stratified lakes. ISME J. 2020;14:274–87.CAS 
    PubMed 

    Google Scholar 
    49.Reis PCJ, Thottathil SD, Ruiz-González C, Prairie YT. Niche separation within aerobic methanotrophic bacteria across lakes and its link to methane oxidation rates. Environ Microbiol. 2020;22:738–51.CAS 
    PubMed 

    Google Scholar 
    50.Tran PQ, Bachand SC, McIntyre PB, Kraemer BM, Vadeboncoeur Y, Kimirei IA, et al. Depth-discrete metagenomics reveals the roles of microbes in biogeochemical cycling in the tropical freshwater Lake Tanganyika. ISME J 2021;15:1971–86.CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    51.Sauder LA, Engel K, Lo C-C, Chain P, Neufeld JD. Candidatus Nitrosotenuis aquarius, an ammonia-oxidizing archaeon from a freshwater aquarium biofilter. Appl Environ Microbiol. 2018;84:e01430-18.52.Hug LA, Thomas BC, Brown CT, Frischkorn KR, Williams KH, Tringe SG, et al. Aquifer environment selects for microbial species cohorts in sediment and groundwater. ISME J. 2015;9:1846–56.PubMed 
    PubMed Central 

    Google Scholar 
    53.Barco RA, Garrity GM, Scott JJ, Amend JP, Nealson KH, Emerson D. A genus definition for Bacteria and Archaea based on a standard genome relatedness index. MBio. 2020;11:e02475–02419.CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    54.Haas S, Desai DK, LaRoche J, Pawlowicz R, Wallace DWR. Geomicrobiology of the carbon, nitrogen and sulphur cycles in Powell Lake: a permanently stratified water column containing ancient seawater. Environ Microbiol. 2019;21:3927–52.CAS 
    PubMed 

    Google Scholar 
    55.Herbold CW, Lehtovirta-Morley LE, Jung M-Y, Jehmlich N, Hausmann B, Han P, et al. Ammonia-oxidising archaea living at low pH: Insights from comparative genomics. Environ Microbiol. 2017;19:4939–52.CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    56.Shen M, Li Q, Ren M, Lin Y, Wang J, Chen L, et al. Trophic status is associated with community structure and metabolic potential of planktonic microbiota in plateau lakes. Front Microbiol. 2019;10:2560–2560.PubMed 
    PubMed Central 

    Google Scholar 
    57.Giovannoni SJ, Cameron Thrash J, Temperton B. Implications of streamlining theory for microbial ecology. ISME J. 2014;8:1553–65.PubMed 
    PubMed Central 

    Google Scholar 
    58.Swan BK, Tupper B, Sczyrba A, Lauro FM, Martinez-Garcia M, González JM, et al. Prevalent genome streamlining and latitudinal divergence of planktonic bacteria in the surface ocean. Proc Natl Acad Sci USA. 2013;110:11463–8.CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    59.Grzymski JJ, Dussaq AM. The significance of nitrogen cost minimization in proteomes of marine microorganisms. ISME J. 2012;6:71–80.CAS 
    PubMed 

    Google Scholar 
    60.Bragg JG, Hyder CL. Nitrogen versus carbon use in prokaryotic genomes and proteomes. Proc R Soc Lond B Biol Sci. 2004;271:S374–7.CAS 

    Google Scholar 
    61.Mende DR, Bryant JA, Aylward FO, Eppley JM, Nielsen T, Karl DM, et al. Environmental drivers of a microbial genomic transition zone in the ocean’s interior. Nat Microbiol. 2017;2:1367–73.CAS 
    PubMed 

    Google Scholar 
    62.Baudouin-Cornu P, Schuerer K, Marlière P, Thomas D. Intimate evolution of proteins: Proteome atomic content correlates with genome base composition. J Biol Chem. 2004;279:5421–8.CAS 
    PubMed 

    Google Scholar 
    63.Santoro AE, Dupont CL, Richter RA, Craig MT, Carini P, McIlvin MR, et al. Genomic and proteomic characterization of Candidatus Nitrosopelagicus brevis: An ammonia-oxidizing archaeon from the open ocean. Proc Natl Acad Sci USA. 2015;112:1173–8.CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    64.Luo H, Tolar BB, Swan BK, Zhang CL, Stepanauskas R, Ann Moran M, et al. Single-cell genomics shedding light on marine Thaumarchaeota diversification. ISME J. 2014;8:732–6.CAS 
    PubMed 

    Google Scholar 
    65.Reji L, Tolar BB, Smith JM, Chavez FP, Francis CA. Depth distributions of nitrite reductase (nirK) gene variants reveal spatial dynamics of thaumarchaeal ecotype populations in coastal Monterey Bay. Environ Microbiol. 2019;21:4032–45.CAS 
    PubMed 

    Google Scholar 
    66.Hallam SJ, Konstantinidis KT, Putnam N, Schleper C, Watanabe Y, Sugahara J, et al. Genomic analysis of the uncultivated marine crenarchaeote Cenarchaeum symbiosum. Proc Natl Acad Sci USA. 2006;103:18296–301.CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    67.Martiny JBH, Jones SE, Lennon JT, Martiny AC. Microbiomes in light of traits: A phylogenetic perspective. Science. 2015;350:aac9323.PubMed 

    Google Scholar 
    68.Logares R, Bråte J, Bertilsson S, Clasen JL, Shalchian-Tabrizi K, Rengefors K. Infrequent marine–freshwater transitions in the microbial world. Trends Microbiol. 2009;17:414–22.CAS 
    PubMed 

    Google Scholar 
    69.Paver SF, Muratore D, Newton RJ, Coleman ML, Flynn TM. Reevaluating the salty divide: Phylogenetic specificity of transitions between marine and freshwater systems. mSystems. 2018;3:e00232–00218.CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    70.Henson MW, Lanclos VC, Faircloth BC, Thrash JC. Cultivation and genomics of the first freshwater SAR11 (LD12) isolate. ISME J. 2018;12:1846–60.CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    71.Luo H, Csűros M, Hughes AL, Moran MA, Azam F, Zehr J. Evolution of divergent life history strategies in marine Alphaproteobacteria. MBio. 2013;4:e00373–00313.PubMed 
    PubMed Central 

    Google Scholar 
    72.Zaremba-Niedzwiedzka K, Viklund J, Zhao W, Ast J, Sczyrba A, Woyke T, et al. Single-cell genomics reveal low recombination frequencies in freshwater bacteria of the SAR11 clade. Genome Biol. 2013;14:R130.PubMed 
    PubMed Central 

    Google Scholar 
    73.Fillol M, Auguet J-C, Casamayor EO, Borrego CM. Insights in the ecology and evolutionary history of the Miscellaneous Crenarchaeotic Group lineage. ISME J. 2016;10:665–77.PubMed 

    Google Scholar 
    74.Siuda W, Kiersztyn B. Urea in lake ecosystem: The origin, concentration and distribution in relation to trophic state of the Great Mazurian Lakes (Poland). Pol J Ecol. 2015;63:110–23. 114
    Google Scholar 
    75.Spang A, Poehlein A, Offre P, Zumbragel S, Haider S, Rychlik N, et al. The genome of the ammonia-oxidizing Candidatus Nitrososphaera gargensis: insights into metabolic versatility and environmental adaptations. Environ Microbiol. 2012;14:3122–45.CAS 
    PubMed 

    Google Scholar 
    76.Kitzinger K, Padilla CC, Marchant HK, Hach PF, Herbold CW, Kidane AT, et al. Cyanate and urea are substrates for nitrification by Thaumarchaeota in the marine environment. Nat Microbiol. 2019;4:234–43.CAS 
    PubMed 

    Google Scholar 
    77.Kerou M, Offre P, Valledor L, Abby SS, Melcher M, Nagler M, et al. Proteomics and comparative genomics of Nitrososphaera viennensis reveal the core genome and adaptations of archaeal ammonia oxidizers. Proc Natl Acad Sci USA. 2016;113:E7937–E7946.CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    78.Carini P, Dupont Christopher L, Santoro, Alyson E. Patterns of thaumarchaeal gene expression in culture and diverse marine environments. Environ Microbiol. 2018;20:2112–24.CAS 
    PubMed 

    Google Scholar 
    79.Bogard MJ, Donald DB, Finlay K, Leavitt PR. Distribution and regulation of urea in lakes of central North America. Freshw Biol. 2012;57:1277–92.CAS 

    Google Scholar 
    80.Glibert PM, Harrison J, Heil C, Seitzinger S. Escalating worldwide use of urea – a global change contributing to coastal eutrophication. Biogeochemistry. 2006;77:441–63.CAS 

    Google Scholar 
    81.Alonso-Sáez L, Waller AS, Mende DR, Bakker K, Farnelid H, Yager PL, et al. Role for urea in nitrification by polar marine Archaea. Proc Natl Acad Sci USA. 2012;109:17989–94.PubMed 
    PubMed Central 

    Google Scholar 
    82.Tolar BB, Wallsgrove NJ, Popp BN, Hollibaugh JT. Oxidation of urea-derived nitrogen by thaumarchaeota-dominated marine nitrifying communities. Environ Microbiol. 2017;19:4838–50.CAS 
    PubMed 

    Google Scholar 
    83.Gunde-Cimerman N, Plemenitaš A, Oren A. Strategies of adaptation of microorganisms of the three domains of life to high salt concentrations. FEMS Microbiol Rev. 2018;42:353–75.CAS 
    PubMed 

    Google Scholar 
    84.Hagemann M. Molecular biology of cyanobacterial salt acclimation. FEMS Microbiol Rev. 2011;35:87–123.CAS 
    PubMed 

    Google Scholar 
    85.Blount P, Iscla I. Life with bacterial mechanosensitive channels, from discovery to physiology to pharmacological target. Microbiol Mol Biol Rev. 2020;84:e00055–00019.CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    86.Martinac B, Bavi N, Ridone P, Nikolaev YA, Martinac AD, Nakayama Y, et al. Tuning ion channel mechanosensitivity by asymmetry of the transbilayer pressure profile. Biophys Rev. 2018;10:1377–84.CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    87.Widderich N, Czech L, Elling FJ, Konneke M, Stoveken N, Pittelkow M, et al. Strangers in the archaeal world: osmostress-responsive biosynthesis of ectoine and hydroxyectoine by the marine thaumarchaeon Nitrosopumilus maritimus. Environ Microbiol. 2016;18:1227–48.CAS 
    PubMed 

    Google Scholar 
    88.Jung H, Hilger D, Raba M. The Na+/L-proline transporter PutP. Front Biosci-Landmark. 2012;17:745–59.CAS 

    Google Scholar 
    89.Tyedmers J, Mogk A, Bukau B. Cellular strategies for controlling protein aggregation. Nat Rev Mol Cell Biol. 2010;11:777–88.CAS 
    PubMed 

    Google Scholar 
    90.Li D-C, Yang F, Lu B, Chen D-F, Yang W-J. Thermotolerance and molecular chaperone function of the small heat shock protein HSP20 from hyperthermophilic archaeon, Sulfolobus solfataricus P2. Cell Stress Chaperones. 2012;17:103–8.PubMed 

    Google Scholar 
    91.Qin W, Amin SA, Lundeen RA, Heal KR, Martens-Habbena W, Turkarslan S, et al. Stress response of a marine ammonia-oxidizing archaeon informs physiological status of environmental populations. ISME J. 2017a;12:508–19.PubMed 
    PubMed Central 

    Google Scholar 
    92.Phadtare S, Inouye M. Role of CspC and CspE in regulation of expression of RpoS and UspA, the stress response proteins in Escherichia coli. J Bacteriol. 2001;183:1205–14.CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    93.Albers S-V, Jarrell KF. The archaellum: An update on the unique archaeal motility structure. Trends Microbiol. 2018;26:351–62.CAS 
    PubMed 

    Google Scholar 
    94.Mosier AC, Lund MB, Francis CA. Ecophysiology of an ammonia-oxidizing archaeon adapted to low-salinity habitats. Micro Ecol. 2012;64:955–63.CAS 

    Google Scholar 
    95.Qin W, Heal KR, Ramdasi R, Kobelt JN, Martens-Habbena W, Bertagnolli AD, et al. Nitrosopumilus maritimus gen. nov., sp. nov., Nitrosopumilus cobalaminigenes sp. nov., Nitrosopumilus oxyclinae sp. nov., and Nitrosopumilus ureiphilus sp. nov., four marine ammonia-oxidizing archaea of the phylum Thaumarchaeota. Int J Syst Evol Microbiol. 2017b;67:5067–79.PubMed 

    Google Scholar 
    96.Dupuis M-È, Villion M, Magadán AH, Moineau S. CRISPR-Cas and restriction–modification systems are compatible and increase phage resistance. Nat Commun. 2013;4:2087.PubMed 

    Google Scholar 
    97.Krupovic M, Makarova KS, Wolf YI, Medvedeva S, Prangishvili D, Forterre P, et al. Integrated mobile genetic elements in Thaumarchaeota. Environ Microbiol. 2019;21:2056–78.CAS 
    PubMed 
    PubMed Central 

    Google Scholar  More

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    Reduced rainfall and resistant varieties mediate a critical transition in the coffee rust disease

    Critical transition theory tells us that, as exogenous parameters drive the system towards a bifurcation and the emergence of a new equilibrium, we can see evidence of the upcoming transition through decreasing resistance to each oscillation peak15, like a ball rolling around in a cup whose walls are becoming less and less steep1,6. We find evidence of this critical slowing down within the year-to-year deceleration of rust growth rates just prior to their annual peak, or λ, our formal slowing down measure. This is coupled with a delay in the initiation month (takeoff) of the oscillation itself. The oscillations collapse in 2019 as the system apparently switches to a more benign non-epidemic state.In the mid-elevation zone of southwestern Mexico where we conducted our study, the general expectation from climate change, in addition to increased temperature, is reduced rainfall19,20, a pattern broadly in evidence for the past five years (Fig. S2). With regard to the coffee rust disease, this precipitation trend is most closely associated with the change in λ, and reflects the obligate range of associated moisture and temperature conditions required for the rust to flourish9,17. On the other hand, rust-resistant replanting has been occurring throughout coffee farms in much of Mesoamerica since the rust outbreak in 2012–201321,22. This could limit plant-to-plant spread by reducing opportunities for direct contact with an infected plant, as well as decrease the environmental spore load by reducing the contributing pool of infected plants in the broader region. On our site, the slowing down patterns of both λ and the rust takeoff point were associated with more rust-resistant replanting on a local level, while the significant linear delay in the year-to-year rust takeoff over the study period may reflect larger-scale effects emerging from increased resistance across the farm.Our preliminary treatment of April as the annual rust season initiation point corresponded generally with a seasonal pattern of increasing rainfall. This approach might be assumed by a manager experiencing the system in real time without knowledge of how the rust season will play out that year. Such an assumption works well in the first two seasons of our data, where the rust increase and rainy season both appear to begin in April. However, this association decouples in subsequent years. Evidently, the critical slowing down we initially estimated in Fig. 1B is partitioned into two components: one that imposes a lag on the initiation of the disease, and another that imposes a decelerating approach to the peak rust intensity, necessitating a joint model of the initiation month and the critical slowing down warning signal (λ). That the takeoff had a significant delaying trend over the study period suggests that the initiation of the rust season can also be seen as a measure of critical slowing down, in that a slower approach to the equilibrium point (the seasonal rust peak) will likely be reflected in a failure to even recognize the increase when it is still very small. Thus, the conspicuous change in takeoff point over time (Fig. 2A, B) itself suggests a critical slowing down and the negative values of λ in Fig. 1B may be mainly a reflection of a lag in takeoff time. The overall increasing lag in rust takeoff each year could mean that the transmission factor itself may be exhibiting a critical slowing down as the critical transition is approached, due to the progressive influence of parameters outside of the present analysis. Given the basic biology of the disease10, this trend may stem from a year-to-year secular decline in the environmental spore load causing initiation of the disease season to be deterred by a small amount each year.The evident relationship between critical slowing down and reduced rainfall is suggestive of a connection to climate change, while a slow secular increase in the proportion of rust-resistant varieties could have inhibited local or regional spread, leading to an effective “herd immunity” to the disease. The joint operation of reduced rainfall and fewer susceptible plants raises the possibility of dual bifurcations and hysteretic zones in the dynamic landscape, which we illustrate in a qualitative fashion (generalized to climate and management) in Fig. 4a. The initial outbreak, which was unexpected in light of the historically low, but persistent, levels of rust in the region7,8, was likely a critical transition precipitated by interactions between local and regional processes14. As foregrounded in Fig. 4b, we propose that a possible parameter driving the system to this initial bifurcation was recent increases in precipitation, as evidenced in local rainfall records (Fig. S2) and regional trends19. This could have propelled the system past a hysteretic phase space to where seasonal conditions dictated that the system jump to a high rust intensity equilibrium, represented conceptually in Fig. 4a by the dotted trajectory leading to the upper surface of the landscape that corresponds to an epidemic state of the rust. Likewise, in the years following the outbreak, our findings suggest that, while the system still tracked precipitation, progressive replanting of resistant varieties emerged as another parameter axis (management) that drove the system through a second critical transition back to a low rust equilibrium (Fig. 4c). Although the dynamical landscape in Fig. 4 is a qualitative representation, the trajectory along the upper surface helps to visualize how two exogenous forces, operating separately, both contributed to the critical transition we observed in our data. The trajectory we propose brings attention to the interesting possibility that the main driver of slowing down shifted from climate (precipitation) to management (replanting), leading to the second critical transition. Indeed, though the average yearly replanting rate remained roughly the same year-to-year (Fig. S1), we note that much of the cutting prior to the collapse in 2019 seemed to be concentrated in April that year (Fig. 1D), accompanied presumably by a similarly timed replanting campaign.Figure 4Envisioning the combination of climate and management effects in a joint hysteretic framing, stemming from gradual change in both forcing parameters. (A) Relative positions of rust intensity for each year are illustrated in their approximate positions with red arrows (other trajectories could be imagined based on the data presented herein). Inset plots provide a conceptualized view of the dynamics between the two forcing variables on rust intensity: (B) management (in our case, resistant variety replanting) and (C) climate (precipitation). In the inset plots, arrows indicate directions of change; solid and dashed lines indicate stable and unstable equilibria, respectively.Full size imageRecent work on critical transitions suggests that perturbances driving a system to transition are more realistically not distinct or isolated, and that the stochastic and deterministic elements of the system can therefore be entangled or even interdependent4. Likewise, we find that the variability in the environmental covariates of monthly rainfall and resistant variety replanting better explained patterns in λ than a linear trend leading up to the transition, as represented by the year variable. The correlation between the quadrat grouping offset estimates from the λ and takeoff components of the multivariate model also suggest that slowing down and delayed takeoff were associated at the individual quadrat level (Fig. 3C). Accounting for this spatial effect, these two components do not appear to be correlated by year (Fig. 3D). This suggests that the shared variability between these two indicators reflects variability in spatial environment within the plot rather than idiosyncratic effects of unique years. Besides the direct effect of resistant varieties, local stochasticity and spread dynamics may also play a role. Local growing conditions, such as variability in shade from overstory trees, can affect dispersal through rainfall splash and wind23,24. Additional management factors may also play a role, such as the vegetation structure and the presence of paths25, as well as the physical relationship between coffee plants26.Our observations of the rust dynamics themselves allow us to detect the general signals anticipating a critical transition, though the drivers may emerge from a complex system of dialectical interactions that must be considered in their whole7,27. The concept of critical slowing down thus may lend itself to application across coffee-growing regions, where predicted effects of climate change and other geographic conditions may differ9,19. Since the emergence of the rust outbreak, recommendations and protocols have been published for monitoring rust levels, potentially providing managers with regular data in changes in rust intensity for many areas9,28. As the resilience of a system can be interpreted through measuring critical slowing down prior to catastrophe2, as well as, in our case, the “exit time” from an undesirable regime4, we demonstrate that such concepts may be applied to this monitoring data to gain some insight into the system’s status. Future studies could explore signs of critical slowing down across coffee-growing regions and management systems to see how these signals predict significant changes and respond to local drivers, potentially adding to the vocabulary of agroecological management.In sum, it is clear that both a lag in takeoff point for the seasonal oscillation and the rate of approach to the peak each year seem to conspire to produce a critical slowing down, strong evidence that the decline in the disease in 2019–2020 is indeed a critical transition, regardless of the underlying mechanism. While our model suggests that two exogenous forces, rainfall and resistant variety replanting, may be driving the slowing down in our case, the underlying dynamical landscape is likely not unique to our site. More generally, the phenomenon of multivariate bifurcations leading to subsequent critical transitions (e.g., Fig. 4) is perhaps more common than thought29. Examinations of critical transitions should therefore consider the larger dynamical landscape for the possibility of subsequent transitions. More

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    Mapping native and non-native vegetation in the Brazilian Cerrado using freely available satellite products

    1.Pennington, R. T., Lehmann, C. E. R. & Rowland, L. M. Tropical savannas and dry forests. Curr. Biol. 28, R541–R545 (2018).CAS 
    PubMed 

    Google Scholar 
    2.Piao, S. et al. Interannual variation of terrestrial carbon cycle: Issues and perspectives. Glob. Change Biol. 26, 300–318 (2019).ADS 

    Google Scholar 
    3.Fan, L. et al. Satellite-observed pantropical carbon dynamics. Nat. Plants. 5, 944–951 (2019).CAS 
    PubMed 

    Google Scholar 
    4.Humphrey, V. et al. Sensitivity of atmospheric CO2 growth rate to observed changes in terrestrial water storage. Nature 560, 628–631 (2018).ADS 
    CAS 
    PubMed 

    Google Scholar 
    5.Moro, M. F., Nic Lughadha, E., de Araújo, F. S. & Martins, F. R. A phytogeographical metaanalysis of the semiarid caatinga domain in Brazil. Bot. Rev. 82, 91–148 (2016).6.Terra, M., et al. Water availability drives gradients of tree diversity, structure and functional traits in the Atlantic–Cerrado–Caatinga transition, Brazil. J. Plant Ecol. 11, 803–814 (2018).7.Leite, M. B., Xavier, R. O., Oliveira, P. T. S., Silva, F. K. G. & Silva Matos, D. M. Groundwater depth as a constraint on the woody cover in a Neotropical Savanna. Plant Soil. 426, 1–15 (2018).8.Silvertown, J., Araya, Y. & Gowing, D. Hydrological niches in terrestrial plant communities: a review. J. Ecol. 103, 93–108 (2014).
    Google Scholar 
    9.Poulter, B. et al. Plant functional type classification for earth system models: results from the European Space Agency’s Land Cover Climate Change Initiative. Geosci. Model Dev. 8, 2315–2328 (2015).ADS 

    Google Scholar 
    10.Congalton, R. G., Gu, J., Yadav, K., Thenkabail, P. & Ozdogan, M. Global land cover mapping: A review and uncertainty analysis. Remote Sens. 6(12), 12070–12093 (2014).ADS 

    Google Scholar 
    11.Phiri, D. & Morgenroth, J. Developments in Landsat land cover classification methods: A review. Remote Sens. 9(9), 967 (2017).
    Google Scholar 
    12.Joshi, N. et al. A review of the application of optical and radar remote sensing data fusion to land use mapping and monitoring. Remote Sens. 8(1), 70 (2016).ADS 

    Google Scholar 
    13.Xiao, J. et al. Remote sensing of the terrestrial carbon cycle: A review of advances over 50 years. Remote Sens. Environ. 233, 111383 (2019).ADS 

    Google Scholar 
    14.Françoso, R. D. et al. Delimiting floristic biogeographic districts in the Cerrado and assessing their conservation status. Biodivers. Conserv. 29, 1477–1500 (2019).
    Google Scholar 
    15.Eiten, G. Delimitation of the cerrado concept. Plant Ecol. 36, 169–178 (1978).
    Google Scholar 
    16.Ribeiro, J.F. & Walter, B.M.T. As principais fitofisionomias do bioma Cerrado in Cerrado: ecologia e flora (ed. Sano, S.M., Almeida, S.P. & Ribeiro, J.F.) 151–212 (EMBRAPA, 2008).17.Oliveria, P.S. & Marquis, R.J. The Cerrados of Brazil: ecology and natural history of a neotropical savanna. (Columbia University Press, 2002).18.Buchhorn, M. et al. Copernicus Global Land Cover Layers—Collection 2. Remote Sens. 12, 1044 (2020).ADS 

    Google Scholar 
    19.European Space Agency. Data. https://climate.esa.int/en/projects/land-cover/data/ (2021).20.Pellegrini, A. F. A. Nutrient limitation in tropical savannas across multiple scales and mechanisms. Ecology 97, 313–324 (2016).PubMed 

    Google Scholar 
    21.Vourlitis, G. L. et al. Variations in stand structure and diversity along a soil fertility gradient in a Brazilian savanna (Cerrado) in Southern Mato Grosso. Soil Sci. Soc. Am. J. 77, 1370–1379 (2013).ADS 
    CAS 

    Google Scholar 
    22.Abrahão, A. et al. Soil types select for plants with matching nutrient-acquisition and use traits in hyperdiverse and severely nutrient-impoverished campos rupestres and cerrado in Central Brazil. J. Ecol. 107, 1302–1316 (2018).
    Google Scholar 
    23.de Assis, A. C. C., Coelho, R. M., da Silva Pinheiro, E. & Durigan, G. Water availability determines physiognomic gradient in an area of low-fertility soils under Cerrado vegetation. Plant Ecol. 212, 1135–1147 (2011).24.Oliveira, P. T. S. et al. Groundwater recharge decrease with increased vegetation density in the Brazilian cerrado. Ecohydrology. 10, e1759 (2016).25.de Oliveira Xavier, R., Leite, M. B., Dexter, K. & da Silva Matos, D. M. Differential effects of soil waterlogging on herbaceous and woody plant communities in a Neotropical savanna. Oecologia. 190, 471–483 (2019).26.Zappi, D. C., Moro, M. F., Meagher, T. R. & Nic Lughadha, E. Plant biodiversity drivers in Brazilian campos rupestres: insights from phylogenetic structure. Front. Plant Sci. 8, (2017).27.Neri, A. V., Schaefer, C. E. G. R., Souza, A. L., Ferreira-Junior, W. G. & Meira-Neto, J. A. A. Pedology and plant physiognomies in the cerrado, Brazil. An. Acad. Bras. Ciênc. 85, 87–102 (2013).CAS 
    PubMed 

    Google Scholar 
    28.Simon, M. F. & Pennington, T. Evidence for Adaptation to Fire Regimes in the Tropical Savannas of the Brazilian Cerrado. Int. J. Plant Sci. 173, 711–723 (2012).
    Google Scholar 
    29.de Castro, E. A. & Kauffman, J. B. Ecosystem structure in the Brazilian Cerrado: a vegetation gradient of aboveground biomass, root mass and consumption by fire. J. Trop. Ecol. 14, 263–283 (1998).
    Google Scholar 
    30.da Silva, D. M. & Batalha, M. A. Soil–vegetation relationships in cerrados under different fire frequencies. Plant Soil 311, 87–96 (2008).CAS 

    Google Scholar 
    31.Durigan, G. Zero-fire: Not possible nor desirable in the Cerrado of Brazil. Flora. 268, 151612 (2020).32.Lloyd, J. & Veenendaal, E. M. Are fire mediated feedbacks burning out of control? (2016)33.Bueno, M. L. et al. The environmental triangle of the Cerrado Domain: Ecological factors driving shifts in tree species composition between forests and savannas. J. Ecol. 106, 2109–2120 (2018).
    Google Scholar 
    34.Alencar, A. et al. Mapping Three Decades of Changes in the Brazilian Savanna Native Vegetation Using Landsat Data Processed in the Google Earth Engine Platform. Remote Sens. 12, 924 (2020).ADS 

    Google Scholar 
    35.INPE. Projeto TerraClass Cerrado Mapeamento do Uso e Cobertura Vegetal do Cerrado. http://www.inpe.br/cra/projetos_pesquisas/dados_terraclass.php (2019)36.Sano, E. E., Rosa, R., Brito, J. L. S. & Ferreira, L. G. Land cover mapping of the tropical savanna region in Brazil. Environ. Monit. Assess. 166, 113–124 (2009).PubMed 

    Google Scholar 
    37.Sano, E. E. et al. Cerrado ecoregions: A spatial framework to assess and prioritize Brazilian savanna environmental diversity for conservation. J. Environ. Manag. 232, 818–828 (2019).
    Google Scholar 
    38.Monteiro, L. M. et al. Evaluating the impact of future actions in minimizing vegetation loss from land conversion in the Brazilian Cerrado under climate change. Biodivers. Conserv. 29, 1701–1722 (2018).
    Google Scholar 
    39.Silva, J. F., Farinas, M. R., Felfili, J. M. & Klink, C. A. Spatial heterogeneity, land use and conservation in the cerrado region of Brazil. J. Biogeogr. 33, 536–548 (2006).
    Google Scholar 
    40.Strassburg, B. B. N. et al. Moment of truth for the Cerrado hotspot. Nat. Ecol. Evol. 1, 0099 (2017).
    Google Scholar 
    41.Soares-Filho, B. et al. Cracking Brazil’s Forest Code. Science 344, 363–364 (2014).ADS 
    CAS 
    PubMed 

    Google Scholar 
    42.Gomes, L., Miranda, H. S. & Bustamante, M. M. C. How can we advance the knowledge on the behavior and effects of fire in the Cerrado biome? For. Ecol. Manag. 417, 281–290 (2018).43.Hartley, A. J., MacBean, N., Georgievski, G. & Bontemps, S. Uncertainty in plant functional type distributions and its impact on land surface models. Remote Sens. Environ. 203, 71–89 (2017).ADS 

    Google Scholar 
    44.Cava, M. G. B., Pilon, N. A. L., Ribeiro, M. C. & Durigan, G. Abandoned pastures cannot spontaneously recover the attributes of old-growth savannas. J. Appl. Ecol. 55, 1164–1172 (2017).
    Google Scholar 
    45.Brancalion, P. H. S. et al. Governance innovations from a multi-stakeholder coalition to implement large-scale Forest Restoration in Brazil. World Dev. Perspect. 3, 15–17 (2016).
    Google Scholar 
    46.Seddon, N. et al. Understanding the value and limits of nature-based solutions to climate change and other global challenges. Phil. Trans. R. Soc. B. 375, 20190120 (2020).PubMed 
    PubMed Central 

    Google Scholar 
    47.MMA. Plano de Manejo Parque Nacional Chapada dos Veadeiros. Ministro de Estado do Meio Ambiente. Brasília. (2009).48.Hunke, P., Roller, R., Zeilhofer, P., Schröder, B. & Mueller, E. N. Soil changes under different land-uses in the Cerrado of Mato Grosso, Brazil. Geoderma Reg. 4, 31–43 (2015).
    Google Scholar 
    49.Sampaio, A.B. et. al. Guia de restauração do Cerrado: volume 1: semeadura direta. Embrapa Cerrados-Livro técnico (INFOTECA-E, 2015).50.Schmidt, I. B. et al. Tailoring restoration interventions to the grassland-savanna-forest complex in central Brazil. Restor. Ecol. 27, 942–948 (2019).
    Google Scholar 
    51.Schmidt, I. B. et al. Community-based native seed production for restoration in Brazil: the role of science and policy. Plant Biol. J. 21, 389–397 (2018).
    Google Scholar 
    52.Strassburg, B. B. N. et al. Author Correction: Strategic approaches to restoring ecosystems can triple conservation gains and halve costs. Nat. Ecol. Evol. 4, 765–765 (2020).PubMed 

    Google Scholar 
    53.Assis, G. B., Pilon, N. A. L., Siqueira, M. F. & Durigan, G. Effectiveness and costs of invasive species control using different techniques to restore cerrado grasslands. Restor. Ecol. 29, (2020).54.Torello-Raventos, M. et al. On the delineation of tropical vegetation types with an emphasis on forest/savanna transitions. Plant Ecol. Divers. 6, 101–137 (2013).
    Google Scholar 
    55.da Silva, D. P., Amaral, A. G., Bijos, N. R. & Munhoz, C. B. R. Is the herb-shrub composition of veredas (Brazilian palm swamps) distinguishable?. Acta Bot. Bras. 32, 47–54 (2017).
    Google Scholar 
    56.Munhoz, C. B. R. & Felfili, J. M. Florística do estrato herbáceo-subarbustivo de um campo limpo úmido em Brasília, Brasil. Biota. Neotrop. 7, 205–215 (2007).
    Google Scholar 
    57.Franco, A. C. et al. Leaf functional traits of Neotropical savanna trees in relation to seasonal water deficit. Trees 19, 326–335 (2004).
    Google Scholar 
    58.Oliveras, I. & Malhi, Y. Many shades of green: the dynamic tropical forest–savannah transition zones. Phil. Trans. R. Soc. B. 371, 20150308 (2016).PubMed 
    PubMed Central 

    Google Scholar 
    59.Cianciaruso, MV. & Batalha, MA. A year in a Cerrado wet grassland: a non-seasonal island in a seasonal savanna environment. Braz. J. Biol. 68, 495–501 (2008).60.MapBiomas. MapBiomas v5.0. https://mapbiomas.org (2021).61.Souza, C. M. Jr. et al. Reconstructing three decades of land use and land cover changes in Brazilian biomes with landsat archive and earth engine. Remote Sens. 12, 2735 (2020).ADS 

    Google Scholar 
    62.Gorelick, N. et al. Google earth engine: planetary-scale geospatial analysis for everyone. Remote Sens. Environ. 202, 18–27 (2017).ADS 

    Google Scholar 
    63.Crouzeilles, R. et al. There is hope for achieving ambitious Atlantic Forest restoration commitments. Perspect. Ecol. Conserv. 17, 80–83 (2019).
    Google Scholar 
    64.Smith, C. C. et al. Secondary forests offset less than 10% of deforestation-mediated carbon emissions in the Brazilian Amazon. Glob. Change Biol. 26, 7006–7020 (2020).ADS 

    Google Scholar 
    65.Rosan, T. M. et al. Extensive 21st-Century Woody Encroachment in South America’s Savanna. Geophys. Res. Lett. 46, 6594–6603 (2019).ADS 

    Google Scholar 
    66.Schwieder, M. et al. Mapping Brazilian savanna vegetation gradients with Landsat time series. Int. J. Appl. Earth Obs. Geoinf. 52, 361–370 (2016).ADS 

    Google Scholar 
    67.Ribeiro, F. F. et al. Geographic Object-Based Image Analysis Framework for Mapping Vegetation Physiognomic Types at Fine Scales in Neotropical Savannas. Remote Sens. 12, 1721 (2020).ADS 

    Google Scholar 
    68.Jacon, A. D., Galvão, L. S., dos Santos, J. R. & Sano, E. E. Seasonal characterization and discrimination of savannah physiognomies in Brazil using hyperspectral metrics from Hyperion/EO-1. Int. J. Remote Sens. 38, 4494–4516 (2017).
    Google Scholar 
    69.Neves, A. K. et al. Hierarchical mapping of Brazilian Savanna (Cerrado) physiognomies based on deep learning. J. App. Remote Sens. 15, 044504–1–044504–23 (2021).70.de Souza Mendes, F., Baron, D., Gerold, G., Liesenberg, V. & Erasmi, S. Optical and SAR remote sensing synergism for mapping vegetation types in the endangered cerrado/amazon ecotone of nova mutum—mato grosso. Remote Sens. 11, 1161 (2019).ADS 

    Google Scholar 
    71.Sano, E. E., Ferreira, L. G., Asner, G. P. & Steinke, E. T. Spatial and temporal probabilities of obtaining cloud-free Landsat images over the Brazilian tropical savanna. Int. J. Remote Sens. 28, 2739–2752 (2007).
    Google Scholar 
    72.Flores-Anderson, A.I., Herndon, K.E., Thapa, R.B. & Cherrington, E. The SAR handbook: comprehensive methodologies for forest monitoring and biomass estimation. (SERVIR, 2019).73.Bendini, H. N. et al. Detailed agricultural land classification in the Brazilian cerrado based on phenological information from dense satellite image time series. Int. J. Appl. Earth. Obs. Geoinf. 82, 101872 (2019).74.Bendini, H. N. et al. Combining environmental and Landsat analysis ready data for vegetation mapping: a case study in the Brazilian savanna biome. Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci. XLIII-B3–2020, 953–960 (2020).75.ECMWF. Climate reanalysis. https://climate.copernicus.eu/climate-reanalysis (2021).76.UNESCO. MINOR MODIFICATIONS PROPOSAL TO THE BOUNDARIES of Cerrado Protected Areas World Heritage: Chapada dos Veadeiros and Emas National Parks. (UNESCO Brasília, 2019)77.ICUN. Advisory mission to Cerrado Protected Areas World Heritage Property (Chapada Dos Veadeiros component) (Brazil). (International Union for Conservation of Nature, 2016)78.EMBRAPA. Sistema brasileiro de classificação dos solos. (EMBRAPA, 2006)79.IBGE. Mapa de Solos do Brasil do IBGE escala 1:250.000 https://www.ibge.gov.br/geociencias/downloads-geociencias.html (IBGE, 2020).80.Rodrigues, J. A. et al. How well do global burned area products represent fire patterns in the Brazilian Savannas biome? An accuracy assessment of the MCD64 collections. Int. J. Appl. Earth. Obs. Geoinf. 78, 318–331 (2019).ADS 

    Google Scholar 
    81.NASA, MCD64A1 v6. https://lpdaac.usgs.gov/products/mcd64a1v006/ (2021)82.GEE. Earth Engine Data Catalog. https://developers.google.com/earth-engine/datasets (2021)83.Vreugdenhil, M. et al. Sensitivity of sentinel-1 backscatter to vegetation dynamics: an Austrian case study. Remote Sens. 10, 1396 (2018).ADS 

    Google Scholar 
    84.Harfenmeister, K., Spengler, D. & Weltzien, C. Analyzing temporal and spatial characteristics of crop parameters using sentinel-1 backscatter data. Remote Sens. 11, 1569 (2019).ADS 

    Google Scholar 
    85.European Space Agency. Level-2A Algorithm Overview https://sentinel.esa.int/web/sentinel/technical-guides/sentinel-2-msi/level-2a/algorithm (2021)86.GEE. Sentinel-2 MSI: MultiSpectral Instrument, Level-2A. https://developers.google.com/earth-engine/datasets/catalog/COPERNICUS_S2_SR (2021).87.GEE. USGS Landsat 8 Level 2, Collection 2, Tier 1. https://developers.google.com/earth-engine/datasets/catalog/LANDSAT_LC08_C02_T1_L2 (2021)88.Xue, J. & Su, B. Significant remote sensing vegetation indices: a review of developments and applications. J. Sens. 2017, 1353691 (2017).
    Google Scholar 
    89.Parente, L. & Ferreira, L. Assessing the spatial and occupation dynamics of the Brazilian pasturelands based on the automated classification of MODIS images from 2000 to 2016. Remote Sens. 10, 606 (2018).ADS 

    Google Scholar 
    90.Hill, M. J., Zhou, Q., Sun, Q., Schaaf, C. B. & Palace, M. Relationships between vegetation indices, fractional cover retrievals and the structure and composition of Brazilian Cerrado natural vegetation. Int. J. Remote Sens. 38, 874–905 (2017).
    Google Scholar 
    91.Nomura, K. & Mitchard, E. More than meets the eye: using sentinel-2 to map small plantations in complex forest landscapes. Remote Sens. 10, 1693 (2018).ADS 

    Google Scholar 
    92.Hagen-Zanker, A. A computational framework for generalized moving windows and its application to landscape pattern analysis. Int. J. Appl. Earth. Obs. Geoinf. 44, 205–216 (2016).ADS 

    Google Scholar 
    93.Wantzen, K. M. et al. Soil carbon stocks in stream-valley-ecosystems in the Brazilian Cerrado agroscape. Agric. Ecosyst. Environ. 151, 70–79 (2012).CAS 

    Google Scholar 
    94.ESA. Copernicus DEM: Global and European Digital Elevation Model (COP-DEM). https://spacedata.copernicus.eu/web/cscda/dataset-details?articleId=394198 (2021)95.Breiman, L. Mach. Learn. 45, 5–32 (2001).96.Chen, D. & Wei, H. The effect of spatial autocorrelation and class proportion on the accuracy measures from different sampling designs. ISPRS J. Photogramm. Remote Sens. 64, 140–150 (2009).ADS 

    Google Scholar 
    97.Olofsson, P., Foody, G. M., Stehman, S. V. & Woodcock, C. E. Making better use of accuracy data in land change studies: Estimating accuracy and area and quantifying uncertainty using stratified estimation. Remote Sens. Environ. 129, 122–131 (2013).ADS 

    Google Scholar 
    98.Foody, G. M. Status of land cover classification accuracy assessment. Remote Sens. Environ. 80, 185–201 (2002).ADS 

    Google Scholar 
    99.Jank, L., Barrios, S. C., do Valle, C. B., Simeão, R. M. & Alves, G. F. The value of improved pastures to Brazilian beef production. Crop Pasture Sci. 65, 1132 (2014).100.Oliveira, J. et al. Choosing pasture maps: An assessment of pasture land classification definitions and a case study of Brazil. Int. J. Appl. Earth. Obs. Geoinf. 93, 102205 (2020).101.Pereira, O., Ferreira, L., Pinto, F. & Baumgarten, L. Assessing pasture degradation in the Brazilian cerrado based on the analysis of MODIS NDVI time-series. Remote Sens. 10, 1761 (2018).ADS 

    Google Scholar 
    102.Meirelles, M.L., Ferreira, E.A.B. and Franco, A.C. Dinâmica sazonal do carbono em campo úmido do cerrado. Embrapa Cerrados-Documentos (INFOTECA-E, 2006).103.França, A. M. S., Paiva, R. J. O., Sano, E. E. & Carvalho, A. M. Estimates for carbon stocks in soil under humid grassland areas in the federal district of Brazil. OJE 04, 777–787 (2014).
    Google Scholar 
    104.Silveira, F. A. O. et al. Ecology and evolution of plant diversity in the endangered campo rupestre: a neglected conservation priority. Plant Soil. 403, 129–152 (2015).
    Google Scholar 
    105.Pereira, E. G., Siqueira-Silva, A. I., de Souza, A. E., Melo, N. M. J. & Souza, J. P. Distinct ecophysiological strategies of widespread and endemic species from the megadiverse campo rupestre. Flora 238, 79–86 (2018).
    Google Scholar 
    106.Moreira, S. N., Pott, V. J., Pott, A., da Silva, R. H. & Júnior, G. A. D. Flora and vegetation structure of Vereda in southwestern Cerrado. Oecol. Aust. 23, 776–798 (2019).
    Google Scholar 
    107.Pinto, J. R. R., Lenza, E. & Pinto, A. de S. Composição florística e estrutura da vegetação arbustivo-arbórea em um cerrado rupestre, Cocalzinho de Goiás, Goiás. Rev. Bras. Bot. 32, (2009).108.Gomes, L., Lenza, E., Maracahipes, L., Marimon, B. S. & Oliveira, E. A. de. Comparações florísticas e estruturais entre duas comunidades lenhosas de cerrado típico e cerrado rupestre, Mato Grosso, Brasil. Acta Bot. Bras. 25, 865–875 (2011).109.Gomes, D.L. Classificação fitofisionômica do cerrado no Parque Nacional da Chapada dos Veadeiros, GO, com a aplicação de uma análise combinatória com filtros adaptativos em imagens TM Landsat. (Dissertação de Mestrado, Brasília, 2008).110.Neyret, M. et al. Examining variation in the leaf mass per area of dominant species across two contrasting tropical gradients in light of community assembly. Ecol. Evol. 6, 5674–5689 (2016).PubMed 
    PubMed Central 

    Google Scholar 
    111.Abreu, R. C. R. et al. The biodiversity cost of carbon sequestration in tropical savanna. Sci. Adv. 3, e1701284 (2017).112.Morais, V. A. et al. Carbon and biomass stocks in a fragment of cerradão in Minas Gerais state, Brazil. Cerne 19, 237–245 (2013).
    Google Scholar 
    113.Bispo, P. da C. et al. Woody aboveground biomass mapping of the Brazilian savanna with a multi-sensor and machine learning approach. Remote Sens. 12, 2685 (2020).114.Taberelli, M. & Gascon, C. Lessons from fragmentation research: improving management and policy guidelines for biodiversity conservation. Conserv. Biol. 19, 734–739 (2005).
    Google Scholar 
    115.Holder, D. N. H., Dockary, M. & Barber, J. The red edge of plant leaf reflectance. Int. J. Remote Sens. 4, 273–288 (1983).
    Google Scholar 
    116.Li, J. & Roy, D. A global analysis of sentinel-2A, sentinel-2B and landsat-8 data revisit intervals and implications for terrestrial monitoring. Remote Sens. 9, 902 (2017).ADS 

    Google Scholar 
    117.Hunter, F. D. L., Mitchard, E. T. A., Tyrrell, P. & Russell, S. Inter-seasonal time series imagery enhances classification accuracy of grazing resource and land degradation maps in a savanna ecosystem. Remote Sens. 12, 198 (2020).ADS 

    Google Scholar 
    118.Ramos, D. M., Diniz, P., Ooi, M. K. J., Borghetti, F. & Valls, J. F. M. Avoiding the dry season: dispersal time and syndrome mediate seed dormancy in grasses in Neotropical savanna and wet grasslands. J. Veg. Sci. 28, 798–807 (2017).
    Google Scholar 
    119.de Camargo, M. G. G., de Carvalho, G. H., Alberton, B. de C., Reys, P. & Morellato, L. P. C. Leafing patterns and leaf exchange strategies of a cerrado woody community. Biotropica. 50, 442–454 (2018).120.Rüetschi, M., Schaepman, M. & Small, D. Using multitemporal sentinel-1 C-band backscatter to monitor phenology and classify deciduous and coniferous forests in Northern Switzerland. Remote Sens. 10, 55 (2017).ADS 

    Google Scholar 
    121.Sano, E. E., Ferreira, L. G. & Huete, A. R. Synthetic aperture radar (L band) and optical vegetation indices for discriminating the Brazilian savanna physiognomies: a comparative analysis. Earth Interact. 9, 1–15 (2005).
    Google Scholar 
    122.Joshi, N. et al. A review of the application of optical and radar remote sensing data fusion to land use mapping and monitoring. Remote Sens. 8, 70 (2016).ADS 

    Google Scholar 
    123.Nicolau, A. P., Flores-Anderson, A., Griffin, R., Herndon, K. & Meyer, F. J. Assessing SAR C-band data to effectively distinguish modified land uses in a heavily disturbed Amazon forest. Int. J. Appl. Earth. Obs. Geoinf. 94, 102214 (2021).124.Notarnicola, C. & Posa, F. Inferring vegetation water content from C- and L-band SAR images. IEEE Trans. Geosci. Remote Sens. 45, 3165–3171 (2007).ADS 

    Google Scholar 
    125.El Hajj, M., Baghdadi, N., Bazzi, H. & Zribi, M. Penetration analysis of SAR signals in the C and L bands for wheat, maize, and grasslands. Remote Sens. 11, 31 (2018).ADS 

    Google Scholar 
    126.JAXA. Global PALSAR-2/PALSAR/JERS-1 Mosaic and Forest/Non-Forest map. https://www.eorc.jaxa.jp/ALOS/en/palsar_fnf/fnf_index.htm (JAXA, 2021).127.Zimbres, B. et al. Mapping the stock and spatial distribution of aboveground woody biomass in the native vegetation of the Brazilian Cerrado biome. For. Ecol. Manag. 499, 119615 (2021).128.Ryan, C. M. et al. Quantifying small-scale deforestation and forest degradation in African woodlands using radar imagery. Glob. Change Biol. 18, 243–257 (2011).ADS 

    Google Scholar 
    129.Yu, Y. & Saatchi, S. Sensitivity of L-Band SAR backscatter to aboveground biomass of global forests. Remote Sens. 8, 522 (2016).ADS 

    Google Scholar 
    130.Pilon, N. A. L. et al. The diversity of post-fire regeneration strategies in the cerrado ground layer. J. Ecol. 109, 154–166 (2020).
    Google Scholar 
    131.Schmidt, I. B. & Eloy, L. Fire regime in the Brazilian Savanna: Recent changes, policy and management. Flora. 268, 151613 (2020).132.Boschetti, L. et al. Global validation of the collection 6 MODIS burned area product. Remote Sens. Environ. 235, 111490 (2019).133.Humber, M. L., Boschetti, L., Giglio, L. & Justice, C. O. Spatial and temporal intercomparison of four global burned area products. Int. J. Digit. Earth. 12, 460–484 (2018).PubMed 
    PubMed Central 

    Google Scholar 
    134.Arruda, V. L. S., Piontekowski, V. J., Alencar, A., Pereira, R. S. & Matricardi, E. A. T. An alternative approach for mapping burn scars using Landsat imagery, Google Earth Engine, and Deep Learning in the Brazilian Savanna. Remote Sens. Appl. Soc. Environ. 22, 100472 (2021).135.Santos, F. L. M. et al. Assessing VIIRS capabilities to improve burned area mapping over the Brazilian Cerrado. Int. J. Remote Sens. 41, 8300–8327 (2020).
    Google Scholar 
    136.Marques, E. Q. et al. Redefining the Cerrado-Amazonia transition: implications for conservation. Biodivers. Conserv. 29, 1501–1517 (2019).
    Google Scholar 
    137.Marimon, B. S. et al. Disequilibrium and hyperdynamic tree turnover at the forest–cerrado transition zone in southern Amazonia. Plant Ecol. Divers. 7, 281–292 (2013).
    Google Scholar 
    138.Mellor, A., Boukir, S., Haywood, A. & Jones, S. Exploring issues of training data imbalance and mislabelling on random forest performance for large area land cover classification using the ensemble margin. ISPRS J. Photogramm. Remote Sens. 105, 155–168 (2015).ADS 

    Google Scholar 
    139.DRYFLOR. Plant diversity patterns in neotropical dry forests and their conservation implications. Science. 353, 1383–1387 (2016). More

  • in

    No short-term effect of sinking microplastics on heterotrophy or sediment clearing in the tropical coral Stylophora pistillata

    1.GESAMP. Global pollution trends: coastal ecosystem assessment for the past century. 101 (2018).2.Andrady, A. L. Microplastics in the marine environment. Mar. Pollut. Bull. 62, 1596–1605 (2011).CAS 
    PubMed 

    Google Scholar 
    3.Hartmann, N. B. et al. Microplastics as vectors for environmental contaminants: Exploring sorption, desorption, and transfer to biota. Integr. Environ. Assess. Manag. 13, 488–493 (2017).PubMed 

    Google Scholar 
    4.Bour, A., Avio, C. G., Gorbi, S., Regoli, F. & Hylland, K. Presence of microplastics in benthic and epibenthic organisms: Influence of habitat, feeding mode and trophic level. Environ. Pollut. 243, 1217–1225 (2018).CAS 
    PubMed 

    Google Scholar 
    5.Jambeck, J. R. et al. Plastic waste inputs from land into the ocean. Science (80-) 47, 768–770 (2015).ADS 

    Google Scholar 
    6.PlasticsEurope. Plastics—The Facts 2020. PlasticsEurope (2020).7.Sweet, M., Steifox, M. & Lamb, J. Plastics and Shallow Water Coral Reefs. Synthesis of the Science for Policy-Makers (2019).8.Stafford, R. & Jones, P. J. S. Viewpoint—Ocean plastic pollution: A convenient but distracting truth? Mar. Policy 19, 0–1 (2019).9.Backhaus, T. & Wagner, M. Microplastics in the environment: Much ado about nothing? A debate. Glob. Challenges 1900022, 1900022 (2019).
    Google Scholar 
    10.Browne, M. A., Niven, S. J., Galloway, T. S., Rowland, S. J. & Thompson, R. C. Microplastic moves pollutants and additives to worms, reducing functions linked to health and biodiversity. Curr. Biol. 23, 2388–2392 (2013).CAS 
    PubMed 

    Google Scholar 
    11.Lebreton, L. C. M. et al. River plastic emissions to the world’s oceans. Nat. Commun. 8, 15611 (2017).CAS 
    PubMed 
    PubMed Central 
    ADS 

    Google Scholar 
    12.Donovan, M. K. et al. Nitrogen pollution interacts with heat stress to increase coral bleaching across the seascape. Proc. Natl. Acad. Sci. USA. 117, 5351–5357 (2020).CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    13.Lamb, J. B. et al. Plastic waste associated with disease on coral reefs. Science (80-) 359, 460–462 (2018).CAS 
    ADS 

    Google Scholar 
    14.Burke, L., Reytar, K., Spalding, M. & Perry, A. Reefs at Risk Revisited (2011).15.Tan, F. et al. Microplastic pollution around remote uninhabited coral reefs of Nansha Islands, South China Sea. Sci. Total Environ. 725, 138383 (2020).CAS 
    PubMed 
    ADS 

    Google Scholar 
    16.Bucol, L. A. et al. Microplastics in marine sediments and rabbitfish (Siganus fuscescens) from selected coastal areas of Negros Oriental, Philippines. Mar. Pollut. Bull. 150, 110685 (2020).CAS 
    PubMed 

    Google Scholar 
    17.Galloway, T. S., Cole, M. & Lewis, C. Interactions of microplastic debris throughout the marine ecosystem. Nat. Ecol. Evol. 1, 1–8 (2017).
    Google Scholar 
    18.Lagarde, F. et al. Microplastic interactions with freshwater microalgae: Hetero-aggregation and changes in plastic density appear strongly dependent on polymer type. Environ. Pollut. 215, 331–339 (2016).CAS 
    PubMed 

    Google Scholar 
    19.Cordova, M. R., Hadi, T. A. & Prayudha, B. Occurrence and abundance of microplastics in coral reef sediment: A case study in Sekotong, Lombok-Indonesia. AES Bioflux 10, 23–29 (2018).
    Google Scholar 
    20.Ogston, A. S., Storlazzi, C. D., Field, M. E. & Presto, M. K. Sediment resuspension and transport patterns on a fringing reef flat, Molokai, Hawaii. Coral Reefs 23, 559–569 (2004).
    Google Scholar 
    21.Bellwood, D. R. Direct estimate of bioerosion by two parrotfish species, Chlorurus gibbus and C. sordidus on the Great Barrier Reef, Australia. Mar. Biol. 121, 419–429 (1995).
    Google Scholar 
    22.Rosenfeld, M., Bresler, V. & Abelson, A. Sediment as a possible food source for corals. Ecol. Lett. 2, 345–348 (1999).
    Google Scholar 
    23.Rogers, C. S. Responses of coral reefs and reef organisms to sedimentation. Mar. Ecol. Prog. Ser. 62, 185–202 (1990).ADS 

    Google Scholar 
    24.Bastidas, C., Bone, D. & Garcia, E. M. Sedimentation rates and metal content of sediments in a Venezuelan coral reef. Mar. Pollut. Bull. 38, 16–24 (1999).CAS 

    Google Scholar 
    25.Smith, L. D., Negri, A. P., Philipp, E., Webster, N. S. & Heyward, A. J. The effects of antifoulant-paint-contaminated sediments on coral recruits and branchlets. Mar. Biol. 143, 651–657 (2003).CAS 

    Google Scholar 
    26.Stafford-Smith, M. Sediment rejection efficiency of 22 species of Australian scleractinian corals. Mar. Biol. 115, 229–243 (1993).
    Google Scholar 
    27.Junjie, R. K., Browne, N. K., Erftemeijer, P. L. A. & Todd, P. A. Impacts of sediments on coral energetics: Partitioning the effects of turbidity and settling particles. PLoS ONE 9, e107195 (2014).PubMed 
    PubMed Central 
    ADS 

    Google Scholar 
    28.Hall, N. M., Berry, K. L. E., Rintoul, L. & Hoogenboom, M. O. Microplastic ingestion by scleractinian corals. Mar. Biol. 162, 725–732 (2015).CAS 

    Google Scholar 
    29.Allen, A. S., Seymour, A. C. & Rittschof, D. Chemoreception drives plastic consumption in a hard coral. Mar. Pollut. Bull. 124, 198–205 (2017).CAS 
    PubMed 

    Google Scholar 
    30.Mouchi, V. et al. Long-term aquaria study suggests species-specific responses of two cold-water corals to macro-and microplastics exposure. Environ. Pollut. 253, 322–329 (2019).CAS 
    PubMed 

    Google Scholar 
    31.Tang, J., Ni, X., Zhou, Z., Wang, L. & Lin, S. Acute microplastic exposure raises stress response and suppresses detoxification and immune capacities in the scleractinian coral Pocillopora damicornis. Environ. Pollut. 243, 66–74 (2018).CAS 
    PubMed 

    Google Scholar 
    32.Chapron, L. et al. Macro- and microplastics affect cold-water corals growth, feeding and behaviour. Sci. Rep. 8, 1–8 (2018).CAS 

    Google Scholar 
    33.Hankins, C., Moso, E. & Lasseigne, D. Microplastics impair growth in two atlantic scleractinian coral species, Pseudodiploria clivosa and Acropora cervicornis. Environ. Pollut. 275, 116649 (2021).CAS 
    PubMed 

    Google Scholar 
    34.Rocha, R. J. M. et al. Do microplastics affect the zoanthid Zoanthus sociatus?. Sci. Total Environ. 713, 136659 (2020).CAS 
    PubMed 
    ADS 

    Google Scholar 
    35.Reichert, J. et al. Interactive effects of microplastic pollution and heat stress on reef-building corals. Environ. Pollut. 290, 118010 (2021).CAS 
    PubMed 

    Google Scholar 
    36.Rotjan, R. D. et al. Patterns, dynamics and consequences of microplastic ingestion by the temperate coral, Astrangia poculata. Proc. R. Soc. B Biol. Sci. 286, 20190726 (2019).CAS 

    Google Scholar 
    37.Reichert, J., Schellenberg, J., Schubert, P. & Wilke, T. Responses of reef building corals to microplastic exposure. Environ. Pollut. 237, 955–960 (2018).CAS 
    PubMed 

    Google Scholar 
    38.Hankins, C., Duffy, A. & Drisco, K. Scleractinian coral microplastic ingestion: Potential calcification effects, size limits, and retention. Mar. Pollut. Bull. 135, 587–593 (2018).CAS 
    PubMed 

    Google Scholar 
    39.Reichert, J., Arnold, A. L., Hoogenboom, M. O., Schubert, P. & Wilke, T. Impacts of microplastics on growth and health of hermatypic corals are species-specific. Environ. Pollut. 254, 113074 (2019).CAS 
    PubMed 

    Google Scholar 
    40.Mendrik, F. M. et al. Species-specific impact of microplastics on coral physiology. Environ. Pollut. 269, 116238 (2021).CAS 
    PubMed 

    Google Scholar 
    41.Corona, E., Martin, C., Marasco, R. & Duarte, C. M. Passive and active removal of marine microplastics by a mushroom Coral (Danafungia scruposa). Front. Mar. Sci. 7, 1–9 (2020).ADS 

    Google Scholar 
    42.Martin, C., Corona, E., Mahadik, G. A. & Duarte, C. M. Adhesion to coral surface as a potential sink for marine microplastics. Environ. Pollut. 255, 113281 (2019).CAS 
    PubMed 

    Google Scholar 
    43.Oldenburg, K. S., Urban-Rich, J., Castillo, K. D. & Baumann, J. H. Microfiber abundance associated with coral tissue varies geographically on the Belize Mesoamerican Barrier Reef System. Mar. Pollut. Bull. 163, 111938 (2021).CAS 
    PubMed 

    Google Scholar 
    44.Axworthy, J. B. & Padilla-Gamiño, J. L. Microplastics ingestion and heterotrophy in thermally stressed corals. Sci. Rep. 9, 1–8 (2019).
    Google Scholar 
    45.Houlbrèque, F. & Ferrier-Pagès, C. Heterotrophy in tropical scleractinian corals. Biol. Rev. 84, 1–17 (2009).PubMed 

    Google Scholar 
    46.Borja, A. et al. Past and future grand challenges in marine ecosystem ecology. Front. Mar. Sci. 7, 362 (2020).
    Google Scholar 
    47.Rochman, C. M., Hentschel, B. T. & The, S. J. Long-term sorption of metals is similar among plastic types: Implications for plastic debris in aquatic environments. PLoS ONE 9, e85433 (2014).PubMed 
    PubMed Central 
    ADS 

    Google Scholar 
    48.Niu, Y., Ying, D., Li, K., Wang, Y. & Jia, J. Adsorption of heavy-metal ions from aqueous solution onto chitosan-modified polyethylene terephthalate (PET). Res. Chem. Intermed. 43, 4213–4245 (2017).CAS 

    Google Scholar 
    49.Frias, J., Sobral, P. & Ferreira, A. Organic pollutants in microplastics from two beaches of the Portuguese coast. Mar. Pollut. Bull. 60, 1988–1992 (2010).CAS 
    PubMed 

    Google Scholar 
    50.Wright, S. L., Thompson, R. C. & Galloway, T. S. The physical impacts of microplastics on marine organisms: A review. Environ. Pollut. 178, 483–492 (2013).CAS 
    PubMed 

    Google Scholar 
    51.Botterell, Z. L. R. et al. Bioavailability of microplastics to marine zooplankton: Effect of shape and infochemicals. Environ. Sci. Technol. 54, 12024–12033 (2020).CAS 
    PubMed 
    ADS 

    Google Scholar 
    52.Wild, C. et al. Coral mucus functions as an energy carrier and particle trap in the ecosystem. Nature 428, 66–70 (2004).CAS 
    PubMed 
    ADS 

    Google Scholar 
    53.Benson, A. & Muscatine, L. Wax in coral mucus: Energy transfer from corals to reef fishes. Limnol. Oceanogr. 19, 810–814 (1974).ADS 

    Google Scholar 
    54.Verla, A. W., Enyoh, C. E., Verla, E. N. & Nwarnorh, K. O. Microplastic–toxic chemical interaction: A review study on quantified levels, mechanism and implication. SN Appl. Sci. 1, 1–30 (2019).CAS 

    Google Scholar 
    55.Brown, B. E. & Bythell, J. C. Perspectives on mucus secretion in reef corals. Mar. Ecol. Prog. Ser. 296, 291–309 (2005).CAS 
    ADS 

    Google Scholar 
    56.Weber, M., Lott, C. & Fabricius, K. E. Sedimentation stress in a scleractinian coral exposed to terrestrial and marine sediments with contrasting physical, organic and geochemical properties. J. Exp. Mar. Bio. Ecol. 336, 18–32 (2006).CAS 

    Google Scholar 
    57.Riegl, B. & Branch, G. Effects of sediment on the energy budgets of four scleractinian (Bourne 1900) and five alcyonacean (Lamoroux 1816) corals. J. Exp. Mar. Bio. Ecol. 186, 259–275 (1995).
    Google Scholar 
    58.Felsing, S. et al. A new approach in separating microplastics from environmental samples based on their electrostatic behavior. Environ. Pollut. 234, 20–28 (2018).CAS 
    PubMed 

    Google Scholar 
    59.Bessell-Browne, P., Negri, A. P., Fisher, R., Clode, P. L. & Jones, R. Cumulative impacts: Thermally bleached corals have reduced capacity to clear deposited sediment. Sci. Rep. 7, 1–14 (2017).
    Google Scholar 
    60.Fitt, W. K. et al. Response of two species of Indo-Pacific corals, Porites cylindrica and Stylophora pistillata, to short-term thermal stress: The host does matter in determining the tolerance of corals to bleaching. J. Exp. Mar. Bio. Ecol. 373, 102–110 (2009).
    Google Scholar 
    61.Weber, M. et al. Mechanisms of damage to corals exposed to sedimentation. Proc. Natl. Acad. Sci. USA. 109, E1558 (2012).CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    62.Lear, G. et al. Plastics and the microbiome: Impacts and solutions. Environ. Microbiomes 16, 1–19 (2021).
    Google Scholar 
    63.Palardy, J., Rodrigues, L. & Grottolli, A. The importance of zooplankton to the daily metabolic carbon requirements of healthy and bleached corals at two depths. J. Exp. Mar. Bio. Ecol. 367, 180–188 (2008).CAS 

    Google Scholar 
    64.Jennings, H. Modifiability in behaviour, 1: Behavior of sea anemones. J. Exp. Zool. 4, 447–632 (1905).
    Google Scholar 
    65.Boschma, H. On the feeding reactions and digestion in the coral polyp Astrangia danae, with notes on its symbiosis with zooxanthellae. Biol. Bull. 49, 407–439 (1925).CAS 

    Google Scholar 
    66.Anthony, K. R. N. Coral suspension feeding on fine particulate matter. J. Exp. Mar. Bio. Ecol. 232, 85–106 (1999).
    Google Scholar 
    67.Schlekat, C., McGee, B. & Reinharz, E. Testing sediment toxicity in chesapeake bay with the amphipod Leptocheirus plumulosus: An evaluation. Environ. Toxicol. Chem. 11, 225–236 (1992).CAS 

    Google Scholar 
    68.Nie, H., Wang, J., Xu, K., Huang, Y. & Yan, M. Microplastic pollution in water and fish samples around Nanxun Reef in Nansha Islands, South China Sea. Sci. Total Environ. 696, 134022 (2019).CAS 
    PubMed 
    ADS 

    Google Scholar 
    69.Cai, M. et al. Lost but can’t be neglected: Huge quantities of small microplastics hide in the South China Sea. Sci. Total Environ. 633, 1206–1216 (2018).CAS 
    PubMed 
    ADS 

    Google Scholar 
    70.Zhu, L. et al. Microplastic pollution in North Yellow Sea, China: Observations on occurrence, distribution and identification. Sci. Total Environ. 636, 20–29 (2018).CAS 
    PubMed 
    ADS 

    Google Scholar 
    71.Saliu, F. et al. Microplastic and charred microplastic in the Faafu Atoll, Maldives. Mar. Pollut. Bull. 136, 464–471 (2018).CAS 
    PubMed 

    Google Scholar 
    72.Saliu, F., Montano, S., Leoni, B., Lasagni, M. & Galli, P. Microplastics as a threat to coral reef environments: Detection of phthalate esters in neuston and scleractinian corals from the Faafu Atoll, Maldives. Mar. Pollut. Bull. 142, 234–241 (2019).CAS 
    PubMed 

    Google Scholar 
    73.Bessa, F. et al. Occurrence of microplastics in commercial fish from a natural estuarine environment. Mar. Pollut. Bull. 128, 575–584 (2018).CAS 
    PubMed 

    Google Scholar 
    74.Ivar Do Sul, J. A. & Costa, M. F. The present and future of microplastic pollution in the marine environment. Environ. Pollut. 185, 352–364 (2014).CAS 
    PubMed 

    Google Scholar 
    75.Chubarenko, I., Bagaev, A., Zobkov, M. & Esiukova, E. On some physical and dynamical properties of microplastic particles in marine environment. Mar. Pollut. Bull. 108, 105–112 (2016).CAS 
    PubMed 

    Google Scholar 
    76.Tang, J. et al. Differential enrichment and physiological impacts of ingested microplastics in scleractinian corals in situ. J. Hazard. Mater. 404, 124205 (2021).CAS 
    PubMed 

    Google Scholar 
    77.Harrison, J. P., Schratzberger, M., Sapp, M. & Osborn, A. M. Rapid bacterial colonization of low-density polyethylene microplastics in coastal sediment microcosms. BMC Microbiol. 14, 1–15 (2014).
    Google Scholar 
    78.Veron, J. Corals of the World (2000).79.Benavides, M. et al. Diazotrophs: A non-negligible source of nitrogen for the tropical coral Stylophora pistillata. J. Exp. Biol. 219, 2608–2612 (2016).PubMed 

    Google Scholar 
    80.Einbinder, S. et al. Changes in morphology and diet of the coral Stylophora pistillata along a depth gradient. Mar. Ecol. Prog. Ser. 381, 167–174 (2009).ADS 

    Google Scholar 
    81.Erni-Cassola, G., Gibson, M. I., Thompson, R. C. & Christie-Oleza, J. A. Lost, but found with Nile red: A novel method for detecting and quantifying small microplastics (1 mm to 20 μm) in environmental samples. Environ. Sci. Technol. 51, 13641–13648 (2017).CAS 
    PubMed 
    ADS 

    Google Scholar 
    82.Swain, T. D., Schellinger, J. L., Strimaitis, A. M. & Reuter, K. E. Evolution of anthozoan polyp retraction mechanisms: Convergent functional morphology and evolutionary allometry of the marginal musculature in order Zoanthidea (Cnidaria: Anthozoa: Hexacorallia). BMC Evol. Biol. 15, 1–19 (2015).
    Google Scholar 
    83.Laissue, P. P., Gu, Y., Qian, C. & Smith, D. J. Light-induced polyp retraction and tissue rupture in the photosensitive, reef-building coral Acropora muricata. bioRxiv https://doi.org/10.1101/862045 (2019).Article 

    Google Scholar 
    84.Renegar, D. A. & Turner, N. R. Species sensitivity assessment of five Atlantic scleractinian coral species to 1-methylnaphthalene. Sci. Rep. 11, 1–17 (2021).
    Google Scholar 
    85.Armoza-Zvuloni, R., Schneider, A., Sher, D. & Shaked, Y. Rapid Hydrogen Peroxide release from the coral Stylophora pistillata during feeding and in response to chemical and physical stimuli. Sci. Rep. 6, 1–10 (2016).
    Google Scholar 
    86.Schneider, C. A., Rasband, W. S. & Eliceiri, K. W. NIH Image to ImageJ: 25 years of image analysis. Nat. Methods 9, 671–675 (2012).CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    87.Meijering, E., Dzyubachyck, O. & Smal, I. Methods for cell and particle tracking. In Methods in Enzymology (Elsevier, 2012).88.Sorgeloos, P., Dhert, P. & Candrevab, P. Use of the brine shrimp, Artemia spp., in marine fish larviculture. Aquaculture 200, 147–159 (2001).
    Google Scholar 
    89.R Core Team. R: A language and environment for statistical computing (2019).90.Wood, A. S., Scheipl, F. & Wood, M. S. Package ‘gamm4’. (2020).91.Zuur, A., Saveliev, A. & Ieno, E. A Beginner’s Guide to Generalized Additive Mixed Models with R. (Highland Statistics Ltd, 2014).92.Zuur, A. F., Hilbe, J. M. & Ieno, E. N. A Beginner’s Guide to GLM and GLMM with R (Highland Statistics Ltd, 2013).93.Pyke, A. & Thompson, J. Statistical analysis of survival and removal rate experiments. Ecology 67, 240–245 (1986).
    Google Scholar 
    94.Therneau, T. M. Mixed effects cox models. R-Package Description. https://doi.org/10.1111/oik.01149 (2015).Article 

    Google Scholar 
    95.Katki, H. A. & Mark, S. D. Survival analysis of studies nested within cohorts using the NestedCohort Package. 1–16 (2013).96.Bürkner, P. C. Advanced Bayesian multilevel modeling with the R package BRMS. R J. 10, 395–411 (2018).
    Google Scholar 
    97.Zuur, A. F., Ieno, E. N., Walker, N. J., Saveliev, A. A. & Smith, G. M. Mixed Effects Models and Extensions in Ecology with R. (Springer, 2009).98.Manly, B. F. J. Measuring selectivity from multiple choice feeding-preference experiments. Biometrics 51, 709 (1995).
    Google Scholar 
    99.Richardson, J. Package ‘ selectapref ’. Anal. F. Lab. Foraging 8–11 (2020). More