More stories

  • in

    Cooperative rescue of a juvenile capuchin (Cebus imitator) from a Boa constrictor

    1.
    Alexander, R. D. The evolution of social behavior. Annu. Rev. Ecol. Syst. 5, 325–383 (1974).
    Article  Google Scholar 
    2.
    Wrangham, R. W. An ecological model of female-bonded primate groups. Behaviour 75, 262–300 (1980).
    Article  Google Scholar 

    3.
    van Schaik, C. P., van Noordwijk, M. A., de Boer, R. J. & den Tonkelaar, I. The effect of group size on time budgets and social behaviour in wild long-tailed macaques (Macaca fascicularis). Behav. Ecol. Sociobiol. 13, 173–181 (1983).
    Article  Google Scholar 

    4.
    Sterck, E. H. M., Watts, D. P. & van Schaik, C. P. The evolution of female social relationships in non-human primates. Behav. Ecol. Sociobiol. 41, 291–309 (1997).
    Article  Google Scholar 

    5.
    Isbell, L. A. The Fruit, the Tree, and the Serpet: Why We See So Well (Harvard University Press, Cambridge, 2009).
    Google Scholar 

    6.
    Seyfarth, R. M., Cheney, D. L. & Marler, P. Vervet monkey alarm calls: semantic communication in a free-ranging primate. Anim. Behav. 28, 1070–1094 (1980).
    Article  Google Scholar 

    7.
    Crofoot, M. C. Why Mob? Reassessing the costs and benefits of primate predator harassment. Folia Primatol. 83, 252–273 (2012).
    PubMed  Article  PubMed Central  Google Scholar 

    8.
    Carlson, N. V., Healy, S. D. & Templeton, C. N. Mobbing. Curr. Biol. 28, R1081–R1082 (2018).
    CAS  PubMed  Article  Google Scholar 

    9.
    Tórrez, L., Robles, N., González, A. & Crofoot, M. C. Risky business? Lethal attack by a jaguar sheds light on the costs of predator mobbing for capuchins (Cebus capucinus). Int. J. Primatol. 33, 440–446 (2012).
    Article  Google Scholar 

    10.
    Corrêa, H. K. M. & Coutinho, P. E. G. Fatal attack of a pit viper, Bothrops jararaca, on an infant buffy-tufted ear marmoset (Callithrix aurita). Primates 38, 215–217 (1997).
    Article  Google Scholar 

    11
    Foerster, S. Two incidents of venomous snakebite on juvenile blue and Sykes monkeys (Cercopithecus mitis stuhlmanni and C. m. albogularis). Primates 49, 300–303 (2008).
    PubMed  Article  PubMed Central  Google Scholar 

    12.
    Ferrari, S. F. & Beltrão-Mendes, R. Do snakes represent the principal predatory threat to callitrichids? Fatal attack of a viper (Bothrops leucurus) on a common marmoset (Callithrix jacchus) in the Atlantic Forest of the Brazilian Northeast. Primates 52, 207–209 (2011).
    PubMed  Article  PubMed Central  Google Scholar 

    13.
    Rose, L. M. et al. Interspecific interactions between Cebus capucinus and other species: data from three Costa Rican sites. Int. J. Primatol. 24, 759–796 (2003).
    Article  Google Scholar 

    14.
    Perry, S., Manson, J. H., Dower, G. & Wikberg, E. White-faced capuchins cooperate to rescue a groupmate from a Boa constrictor. Folia Primatol. 74, 109–111 (2003).
    PubMed  Article  PubMed Central  Google Scholar 

    15.
    van Schaik, C. P. & van Noordwijk, M. A. The special role of male Cebus monkeys in predation avoidance and its effect on group composition. Behav. Ecol. Sociobiol. 24, 265–276 (1989).
    Article  Google Scholar 

    16.
    Fragaszy, D. M., Visalberghi, E. & Fedigan, L. M. The Complete Capuchin: The Biology of the Genus Cebus (Cambridge University Press, Cambridge, 2004).
    Google Scholar 

    17.
    Meno, W., Coss, R. G. & Perry, S. Development of snake-directed antipredator behavior by wild white-faced capuchin monkeys: I. Snake-species discrimination. Am. J. Primatol. 75, 281–291 (2013).
    PubMed  Article  PubMed Central  Google Scholar 

    18.
    Fedigan, L. M. & Jack, K. M. Tracking neotropical monkeys in Santa Rosa: lessons from a regenerating Costa Rican dry forest. In Long-Term Field Studies of Primates (eds Kappeler, P. M. & Watts, D.) 165–184 (Springer, Berlin, 2012).
    Google Scholar 

    19.
    Campos, F. A. A synthesis of long-term environmental change in Santa Rosa, Costa Rica. In Primate Life Histories, Sex Roles, and Adaptability (eds Kalbitzer, U. & Jack, K. M.) 331–358 (Springer, Berlin, 2018).
    Google Scholar 

    20.
    Rose, L. M. Sex differences in diet and foraging behavior in white-faced capuchins (Cebus capucinus). Int. J. Primatol. 15, 95–114 (1994).
    Article  Google Scholar 

    21.
    Shields, W. M. Factors affecting nest and site fidelity in adirondack barn swallows (Hirundo rustica). Auk 101, 780–789 (1984).
    Article  Google Scholar 

    22.
    Teunissen, N., Kingma, S. A. & Peters, A. Predator defense is shaped by risk, brood value and social group benefits in a cooperative breeder. Behav. Ecol. 31, 761–771 (2020).
    Article  Google Scholar 

    23.
    Kennedy, R. A. W., Evans, C. S. & McDonald, P. G. Individual distinctiveness in the mobbing call of a cooperative bird, the noisy miner Manorina melanocephala. J. Avian Biol. 40, 481–490 (2009).
    Article  Google Scholar 

    24.
    Briones-Fourzán, P., Pérez-Ortiz, M. & Lozano-Álvarez, E. Defense mechanisms and antipredator behavior in two sympatric species of spiny lobsters, Panulirus argus and P. guttatus. Mar. Biol. 149, 227–239 (2006).
    Article  Google Scholar 

    25.
    Leuchtenberger, C., Almeida, S. B., Andriolo, A. & Crawshaw, P. G. Jaguar mobbing by giant otter groups. Acta Ethol. 19, 143–146 (2016).
    Article  Google Scholar 

    26.
    Graw, B. & Manser, M. B. The function of mobbing in cooperative meerkats. Anim. Behav. 74, 507–517 (2007).
    Article  Google Scholar 

    27.
    Boesch, C. The effects of leopard predation on grouping patterns in forest chimpanzees. Behaviour 117, 220–242 (1991).
    Article  Google Scholar 

    28.
    Pitman, R. L. et al. Humpback whales interfering when mammal-eating killer whales attack other species: Mobbing behavior and interspecific altruism?. Mar. Mammal Sci. 33, 7–58 (2017).
    Article  Google Scholar 

    29.
    Gusset, M. Banded together: a review of the factors favouring group living in a social carnivore, the banded mongoose Mungos mungo (Carnivora: Herpestidae). Mammalia 71, 80–82 (2007).
    Article  Google Scholar 

    30.
    Johnson, C. & Norris, K. S. Delphinid social organisation and social behavior. In Dolphin Cognition and Behavior: a Comparative Approach (eds Schusterman, R. J. et al.) 335–346 (Lawrence Erlbaum Associates, Mahwah, 1986).
    Google Scholar 

    31.
    Hollis, K. L. & Nowbahari, E. Toward a behavioral ecology of rescue behavior. Evol. Psychol. 11, 647–664 (2013).
    PubMed  Article  Google Scholar 

    32.
    Nowbahari, E. & Hollis, K. L. Distinguishing between rescue, cooperation and other forms of altruistic behavior. Commun. Integr. Biol. 3, 77–79 (2010).
    PubMed  Article  PubMed Central  Google Scholar 

    33.
    Gursky, S. Predation on a wild spectral tarsier (Tarsius spectrum) by a snake. Folia Primatol. 73, 60–62 (2002).
    PubMed  Article  Google Scholar 

    34.
    Quintino, E. P. & Bicca-Marques, J. C. Predation of Alouatta puruensis by Boa constrictor. Primates 54, 325–330 (2013).
    PubMed  Article  Google Scholar 

    35.
    Teixeira, D. S. et al. Fatal attack on black-tufted-ear marmosets (Callithrix penicillata) by a Boa constrictor: a simultaneous assault on two juvenile monkeys. Primates 57, 123–127 (2016).
    PubMed  Article  Google Scholar 

    36.
    Cisneros-Heredia, D. F., León-Reyes, A. & Seger, S. Boa constrictor predation on a Titi monkey, Callicebus discolor. Neotrop. Primates 13, 11–12 (2005).
    Article  Google Scholar 

    37.
    Chapman, C. A. Boa constrictor predation and group response in white-faced Cebus monkeys. Biotropica 18, 171–172 (1986).
    Article  Google Scholar 

    38.
    Ferrari, S. F., Pereira, W. L. A., Santos, R. R. & Veiga, L. M. Fatal attack of a Boa constrictor on a bearded saki (Chiropotes satanas utahicki). Folia Primatol. 75, 111–113 (2004).
    PubMed  Article  Google Scholar 

    39.
    Heymann, E. W. A field observation of predation on a moustached tamarin (Saguinus mystax) by an anaconda. Int. J. Primatol. 8, 193–195 (1987).
    Article  Google Scholar 

    40.
    Burney, D. A. Sifaka predation by a large boa. Folia Primatol. 73, 144–145 (2002).
    PubMed  Article  PubMed Central  Google Scholar 

    41.
    Pennisi, E. How did cooperative behavior evolve. Science 309, 93 (2005).
    CAS  PubMed  Article  PubMed Central  Google Scholar 

    42.
    Foster, K. R., Wenseleers, T. & Ratnieks, F. L. W. Kin selection is the key to altruism. Trends Ecol. Evol. 21, 57–60 (2006).
    PubMed  Article  PubMed Central  Google Scholar 

    43.
    Fedigan, L. M. & Jack, K. M. Neotropical primates in a regenerating Costa Rican dry forest: a comparison of howler and capuchin population patterns. Int. J. Primatol. 22, 689–713 (2001).
    Article  Google Scholar 

    44.
    Gould, L., Fedigan, L. M. & Rose, L. M. Why be vigilant? The case of the alpha animal. Int. J. Primatol. 18, 401–414 (1997).
    Article  Google Scholar 

    45.
    Schoof, V. A. M. & Jack, K. M. Rank-based differences in fecal androgen and cortisol levels in male white-faced capuchins, Cebus capucinus, in the Santa Rosa Sector, Area de Conservacíon Guanacaste, Costa Rica. Am. J. Primatol. 71, 76 (2009).
    Google Scholar 

    46.
    Schaebs, F. S., Perry, S. E., Cohen, D., Mundry, R. & Deschner, T. Social and demographic correlates of male androgen levels in wild white-faced capuchin monkeys (Cebus capucinus). Am. J. Primatol. 79, 79 (2017).
    Article  CAS  Google Scholar 

    47.
    Jack, K. M. et al. Hormonal correlates of male life history stages in wild white-faced capuchin monkeys (Cebus capucinus). Gen. Comp. Endocrinol. 195, 58–67 (2014).
    CAS  PubMed  Article  PubMed Central  Google Scholar 

    48.
    Godoy, I., Vigilant, L. & Perry, S. E. Inbreeding risk, avoidance and costs in a group-living primate, Cebus capucinus. Behav. Ecol. Sociobiol. 70, 1601–1611 (2016).
    Article  Google Scholar 

    49.
    Wikberg, E. C. et al. Inbreeding avoidance and female mate choice shape reproductive skew in capuchin monkeys (Cebus capucinus imitator). Mol. Ecol. 26, 653–667 (2017).
    PubMed  Article  PubMed Central  Google Scholar 

    50.
    Van Schaik, C. P. Why are diurnal primates living in groups?. Behaviour 87, 120–144 (1983).
    Article  Google Scholar 

    51.
    Tello, N. S., Huck, M. & Heymann, E. W. Boa constrictor attack and successful group defence in moustached tamarins, Saguinus mystax. Folia Primatol. 73, 146–148 (2002).
    PubMed  Article  PubMed Central  Google Scholar 

    52.
    Gardner, C. J., Radolalaina, P., Rajerison, M. & Greene, H. W. Cooperative rescue and predator fatality involving a group-living strepsirrhine, Coquerel’s sifaka (Propithecus coquereli), and a Madagascar ground boa (Acrantophis madagascariensis). Primates 56, 127–129 (2015).
    PubMed  Article  PubMed Central  Google Scholar 

    53.
    Eberle, M. & Kappeler, P. M. Mutualism, reciprocity, or kin selection? Cooperative rescue of a conspecific from a boa in a nocturnal solitary forager the gray mouse lemur. Am. J. Primatol. 70, 410–414 (2008).
    PubMed  Article  PubMed Central  Google Scholar 

    54.
    Ribeiro-Júnior, M. A., Ferrari, S. F., Lima, J. R. F., da Silva, C. R. & Lima, J. D. Predation of a squirrel monkey (Saimiri sciureus) by an Amazon tree boa (Corallus hortulanus): even small Boids may be a potential threat to small-bodied platyrrhines. Primates 57, 317–322 (2016).
    PubMed  Article  PubMed Central  Google Scholar 

    55.
    Wiens, F. & Zitzmann, A. Predation on a wild slow loris (Nycticebus coucang) by a reticulated python (Python reticulatus). Folia Primatol. 70, 362–364 (1999).
    CAS  PubMed  Article  PubMed Central  Google Scholar 

    56.
    Raktondravony, D., Goodman, S. M. & Soarimalala, V. Predation on Hapalemur griseus griseus by Boa manditra (Boidae) in the littoral forest of eastern Madagascar. Folia Primatol. 69, 405–408 (1998).
    Article  Google Scholar  More

  • in

    Implication of single year seasonal sampling to genetic diversity fluctuation that coordinates with oceanographic dynamics in torpedo scads near Taiwan

    1.
    Dunn, D. C., Boustany, A. M. & Halpin, P. N. Spatio-temporal management of fisheries to reduce by-catch and increase fishing selectivity. Fish Fish. 12, 110–119 (2011).
    Article  Google Scholar 
    2.
    Allen, A. M. & Singh, N. J. Linking movement ecology with wildlife management and conservation. Front. Ecol. Evol. 3, 155 (2016).
    Article  Google Scholar 

    3.
    Wedding, L. M. et al. Geospatial approaches to support pelagic conservation planning and adaptive management. Endang. Species Res. 30, 1–9 (2016).
    Article  Google Scholar 

    4.
    André, C. et al. Population structure in Atlantic cod in the eastern North Sea-Skagerrak-Kattegat: early life stage dispersal and adult migration. BMC Res. Notes 9, 1 (2016).
    Article  Google Scholar 

    5.
    Canales-Aguirre, C. B., Ferrada-Fuentes, S., Galleguillos, R. & Hernández, C. E. Genetic structure in a small pelagic fish coincides with a marine protected area: seascape genetics in Patagonian Fjords. PLoS ONE 11, e0160670. https://doi.org/10.1371/journal.pone.0160670 (2016).
    CAS  Article  PubMed  PubMed Central  Google Scholar 

    6.
    Eggers, F. et al. Seasonal dynamics of Atlantic herring (Clupea harengus L.) populations spawning in the vicinity of marginal habitats. PLoS ONE 9, e111985 (2014).
    ADS  Article  Google Scholar 

    7.
    Saraux, C. et al. Small pelagic fish dynamics: a review of mechanisms in the Gulf of Lions. Deep Sea Res. Part II Top. Stud. Oceanogr. 159, 52–61 (2019).
    ADS  Article  Google Scholar 

    8.
    Silva, A. et al. Adult-mediated connectivity and spatial population structure of sardine in the Bay of Biscay and Iberian coast. Deep Sea Res. Part II Top. Stud. Oceanogr. 159, 62–74 (2019).
    ADS  Article  Google Scholar 

    9.
    Sreenivasan, P. Observations on the fishery and biology of Megalaspis cordyla (Linnaeus) at Vizhinjam. Indian J. Fish. 25, 122–140 (1978).
    Google Scholar 

    10.
    Nakabō, T. Fishes of Japan: With Pictorial Keys to the Species Vol. 1 (Tokai University Press, Tokyo, 2002).
    Google Scholar 

    11.
    Shao, K. T. Taiwan Fish Database. WWW Web electronic publication. https://fishdb.sinica.edu.tw. Accessed October 20, 2019.

    12.
    Sreenivasan, P. Observations on the food and feeding habits of the Ttorpedo trevally Megalaspis cordyla (Linnaeus) from Vizhinjam bay. Indian J. Fish. 21, 76–84 (1974).
    Google Scholar 

    13.
    Hu, J., Kawamura, H., Hong, H. & Qi, Y. A review on the currents in the South China Sea: seasonal circulation, South China Sea warm current and Kuroshio intrusion. J. Oceanogr. 56, 607–624 (2000).
    Article  Google Scholar 

    14.
    Gallagher, S. J. et al. Neogene history of the West Pacific warm pool, Kuroshio and Leeuwin currents. Paleoceanography https://doi.org/10.1029/2008PA001660 (2009).
    Article  Google Scholar 

    15.
    Gallagher, S. J. et al. The Pliocene to recent history of the Kuroshio and Tsushima Currents: a multi-proxy approach. Prog. Earth Planet. Sci. 2, 17 (2015).
    ADS  Article  Google Scholar 

    16.
    Jan, S., Wang, J., Chern, C.-S. & Chao, S.-Y. Seasonal variation of the circulation in the Taiwan Strait. J. Mar. Syst. 35, 249–268 (2002).
    Article  Google Scholar 

    17.
    Winans, G. A. Geographic variation in the milkfish Chanos chanos I. Biochemical evidence. Evolution 34, 558–574 (1980).
    CAS  PubMed  Google Scholar 

    18.
    Bell, L., Moyer, J. & Numachi, K. Morphological and genetic variation in Japanese populations of the anemonefish Amphiprion clarkii. Mar. Biol. 72, 99–108 (1982).
    Article  Google Scholar 

    19.
    Richardson, B. Distribution of protein variation in skipjack tuna (Katsumonuspelamis) from the central and south-west Pacific. Aust. J. Mar. Freshw. Res. 34, 231–251 (1983).
    CAS  Article  Google Scholar 

    20.
    Rosenblatt, R. H. & Waples, R. S. A genetic comparison of allopatric populations of shore fish species from the eastern and central Pacific Ocean: dispersal or vicariance?. Copeia 1986, 275–284 (1986).
    Article  Google Scholar 

    21.
    McMillan, W. O. & Palumbi, S. R. Concordant evolutionary patterns among Indo-West Pacific butterflyfishes. Proc. R. Soc. B 260, 229–236 (1995).
    ADS  CAS  Article  Google Scholar 

    22.
    Grant, W. & Bowen, B. W. Shallow population histories in deep evolutionary lineages of marine fishes: insights from sardines and anchovies and lessons for conservation. J. Hered. 89, 415–426 (1998).
    Article  Google Scholar 

    23.
    Palumbi, S. R. & Wilson, A. C. Mitochondrial DNA diversity in the sea urchins Strongylocentrotus purpuratus and S. droebachiensis. Evolution 44, 403–415 (1990).
    Article  Google Scholar 

    24.
    Ayala, F. J., Hedgecock, D., Zumwalt, G. S. & Valentine, J. W. Genetic variation in Tridacna maxima, an ecological analog of some unsuccessful evolutionary lineages. Evolution 27, 177–191 (1973).
    PubMed  Google Scholar 

    25.
    Benzie, J. A. & Williams, S. T. Genetic structure of giant clam (Tridacna maxima) populations from reefs in the Western Coral Sea. Coral Reefs 11, 135–141 (1992).
    ADS  Article  Google Scholar 

    26.
    Williams, S. T. & Benzie, J. A. H. Genetic uniformity of widely separated populations of the coral reef starfish Linckia laevigata from the East Indian and West Pacific Oceans, revealed by allozyme electrophoresis. Mar. Biol. 126, 99–107 (1996).
    Article  Google Scholar 

    27.
    Arnaud, S., Bonhomme, F. & Borsa, P. Mitochondrial DNA analysis of the genetic relationships among populations of scad mackerel (Decapterus macarellus, D. macrosoma, and D. russelli) in South-East Asia. Mar. Biol. 135, 699–707. https://doi.org/10.1007/s002270050671 (1999).
    CAS  Article  Google Scholar 

    28.
    Huang, C., Weng, C. & Lee, S. Distinguishing two types of gray mullet, Mugil cephalus L. (Mugiliformes: Mugilidae), by using glucose-6-phosphate isomerase (GPI) allozymes with special reference to enzyme activities. J. Comp. Physiol. B 171, 387–394 (2001).
    CAS  Article  Google Scholar 

    29.
    McCafferty, S. et al. Historical biogeography and molecular systematics of the Indo-Pacific genus Dascyllus (Teleostei: Pomacentridae). Mol. Ecol. 11, 1377–1392 (2002).
    CAS  Article  Google Scholar 

    30.
    Benzie, J. A. & Williams, S. T. Genetic structure of giant clam (Tridacna maxima) populations in the West Pacific is not consistent with dispersal by present-day ocean currents. Evolution 51, 768–783 (1997).
    PubMed  Google Scholar 

    31.
    Fauvelot, C. & Planes, S. Understanding origins of present-day genetic structure in marine fish: biologically or historically driven patterns? Mar. Biol. 141, 773–788 (2002).
    Article  Google Scholar 

    32.
    Rajanna, K., Benakappa, S., Anjanayappa, H. & Honnananda, B. Maturation and spawning of the horse mackerel, Megalaspis cordyla (Linnaeus) from Mangalore waters. Environ. Ecol. 30, 41–44 (2012).
    Google Scholar 

    33.
    Song, N., Jia, N., Yanagimoto, T., Lin, L. & Gao, T. Genetic differentiation of Trachurus japonicus from the Northwestern Pacific based on the mitochondrial DNA control region. Mitochondrial DNA 24, 705–712 (2013).
    CAS  Article  Google Scholar 

    34.
    Niu, S.-F. et al. Demographic history and population genetic analysis of Decapterus maruadsi from the northern South China Sea based on mitochondrial control region sequence. PeerJ 7, e7953 (2019).
    Article  Google Scholar 

    35.
    Clark, P. U. et al. The last glacial maximum. Science 325, 710–714 (2009).
    ADS  CAS  Article  Google Scholar 

    36.
    Lavery, S., Moritz, C. & Fielder, D. Indo-Pacific population structure and evolutionary history of the coconut crab Birgus latro. Mol. Ecol. 5, 557–570 (1996).
    Article  Google Scholar 

    37.
    Planes, S. Geographic structure and gene flow in the manini (convict surgeonfish, Acanthurustriostegus) in the South Central Pacific. Genetics and Evolution of Aquatic Organisms, 113–122 (1994).

    38.
    Ocean Data Bank of the Ministry of Science and Technology, Republic of China. https://www.odb.ntu.edu.tw/.

    39.
    Thompson, J. D., Higgins, D. G. & Gibson, T. J. CLUSTAL W: improving the sensitivity of progressive multiple sequence alignment through sequence weighting, position-specific gap penalties and weight matrix choice. Nucleic Acids Res. 22, 4673–4680 (1994).
    CAS  Article  Google Scholar 

    40.
    Hall, T. A. BioEdit: a user-friendly biological sequence alignment editor and analysis program for Windows 95/98/NT. Nucleic Acids Symp. Ser. 41, 95–98 (1999).
    CAS  Google Scholar 

    41.
    Suchard, M. A. et al. Bayesian phylogenetic and phylodynamic data integration using BEAST 110. Virus Evol. 4, vey016 (2018).
    Article  Google Scholar 

    42.
    Damerau, M., Freese, M. & Hanel, R. Multi-gene phylogeny of jacks and pompanos (Carangidae), including placement of monotypic vadigo Campogramma glaycos. J. Fish Biol. 92, 190–202 (2018).
    CAS  Article  Google Scholar 

    43.
    Clement, M., Posada, D. & Crandall, K. A. TCS: a computer program to estimate gene genealogies. Mol. Ecol. 9, 1657–1659 (2000).
    CAS  Article  Google Scholar 

    44.
    Múrias dos Santos, A., Cabezas, M. P., Tavares, A. I., Xavier, R. & Branco, M. tcsBU: a tool to extend TCS network layout and visualization. Bioinformatics 32, 627–628. https://doi.org/10.1093/bioinformatics/btv636 (2015).
    CAS  Article  PubMed  Google Scholar 

    45.
    Rozas, J. et al. DnaSP 6: DNA sequence polymorphism analysis of large data sets. Mol. Biol. Evol. 34, 3299–3302 (2017).
    CAS  Article  Google Scholar 

    46.
    Global Administrative Areas (2012). GADM database of Global Administrative Areas. https://www.gadm.org.

    47.
    QGIS.org (2020). QGIS Geographic Information System. Open Source Geospatial Foundation Project. https://qgis.org.

    48.
    Adobe Inc. (2019). Adobe Illustrator. https://adobe.com/products/illustrator. More

  • in

    Combined pigment and metatranscriptomic analysis reveals highly synchronized diel patterns of phenotypic light response across domains in the open oligotrophic ocean

    1.
    Eberhard S, Finazzi G, Wollman F-A. The dynamics of photosynthesis. Annu Rev Genet. 2008;42:463–515.
    CAS  PubMed  Article  PubMed Central  Google Scholar 
    2.
    Dubinsky Z, Stambler N. Photoacclimation processes in phytoplankton: mechanisms, consequences, and applications. Aquat Micro Ecol. 2009;56:163–76.
    Article  Google Scholar 

    3.
    Wright SW, Jeffrey SW. Pigment markers for phytoplankton production. In: Volkman JK (ed). Marine organic matter: biomarkers, isotopes and DNA. Berlin, Heidelberg: Springer Berlin Heidelberg; 2006. p. 71–104.

    4.
    Armstrong GA. Eubacteria show their true colors: genetics of carotenoid pigment biosynthesis from microbes to plants. J Bacteriol. 1994;176:4795–802.
    CAS  PubMed  PubMed Central  Article  Google Scholar 

    5.
    Ottesen EA, Young CR, Eppley JM, Ryan JP, Chavez FP, Scholin CA, et al. Pattern and synchrony of gene expression among sympatric marine microbial populations. Proc Natl Acad Sci USA. 2013;110:E488–97.
    CAS  PubMed  Article  PubMed Central  Google Scholar 

    6.
    Ottesen EA, Young CR, Gifford SM, Eppley JM, Marin R, Schuster SC, et al. Multispecies diel transcriptional oscillations in open ocean heterotrophic bacterial assemblages. Science. 2014;345:207–12.
    CAS  PubMed  Article  PubMed Central  Google Scholar 

    7.
    Aylward FO, Eppley JM, Smith JM, Chavez FP, Scholin CA, DeLong EF. Microbial community transcriptional networks are conserved in three domains at ocean basin scales. Proc Natl Acad Sci USA. 2015;112:5443–8.
    CAS  PubMed  Article  PubMed Central  Google Scholar 

    8.
    Kolody BC, McCrow JP, Allen LZ, Aylward FO, Fontanez KM, Moustafa A, et al. Diel transcriptional response of a California Current plankton microbiome to light, low iron, and enduring viral infection. ISME J. 2019;13:2817–33.
    CAS  PubMed  PubMed Central  Article  Google Scholar 

    9.
    Neveux J, Dupouy C, Blanchot J, Le Bouteiller A, Landry MR, Brown SL. Diel dynamics of chlorophylls in high-nutrient, low-chlorophyll waters of the equatorial Pacific (180°): interactions of growth, grazing, physiological responses, and mixing. J Geophys Res Ocean. 2003;108:8140. https://doi.org/10.1029/2000JC000747.

    10.
    Le Bouteiller A, Herbland A. Diel variation of chlorophyll a as evidence from a 13-day station in the equatorial Atlantic ocean. Oceano Acta. 1982;5:433–41.
    Google Scholar 

    11.
    Litchman E. Resource Competition and the ecological success of phytoplankton. In: Falkowski PG, Knoll AH (eds). Evolution of primary producers in the sea. Burlington: Academic Press; 2007. p. 351–75.

    12.
    Graff JR, Behrenfeld MJ. Photoacclimation responses in subarctic atlantic phytoplankton following a natural mixing-restratification event. Front Mar Sci. 2018;5:209.
    Article  Google Scholar 

    13.
    Behrenfeld MJ, Boss E, Siegel DA, Shea DM. Carbon-based ocean productivity and phytoplankton physiology from space. Global Biogeochem Cycles. 2005;19:GB1006. https://doi.org/10.1029/2004GB002299.

    14.
    Tomkins M, Martin AP, Nurser AJG, Anderson TR. Phytoplankton acclimation to changing light intensity in a turbulent mixed layer: a Lagrangian modelling study. Ecol Model. 2020;417:108917.
    CAS  Article  Google Scholar 

    15.
    Wilson ST, Aylward FO, Ribalet F, Barone B, Casey JR, Connell PE, et al. Coordinated regulation of growth, activity and transcription in natural populations of the unicellular nitrogen-fixing cyanobacterium Crocosphaera. Nat Microbiol. 2017;2:17118.
    CAS  PubMed  Article  PubMed Central  Google Scholar 

    16.
    Emerson S, Quay P, Karl D, Winn C, Tupas L, Landry M. Experimental determination of the organic carbon flux from open-ocean surface waters. Nature. 1997;389:951–4.
    CAS  Article  Google Scholar 

    17.
    Sarmiento JL, Slater R, Barber R, Bopp L, Doney SC, Hirst AC, et al. Response of ocean ecosystems to climate warming. Glob Biogeochem Cycles. 2004;18:GB3003.
    Article  CAS  Google Scholar 

    18.
    Popendorf KJ, Fredricks HF, Van, Mooy BAS. Molecular ion-independent quantification of polar glycerolipid classes in marine plankton using triple quadrupole MS. Lipids. 2013;48:185–95.
    CAS  PubMed  Article  PubMed Central  Google Scholar 

    19.
    Becker KW, Collins JR, Durham BP, Groussman RD, White AE, Fredricks HF, et al. Daily changes in phytoplankton lipidomes reveal mechanisms of energy storage in the open ocean. Nat Commun. 2018;9:5179.
    PubMed  PubMed Central  Article  CAS  Google Scholar 

    20.
    Collins JR, Edwards BR, Fredricks HF, Van Mooy BAS. LOBSTAHS: an adduct-based lipidomics strategy for discovery and identification of oxidative stress biomarkers. Anal Chem. 2016;88:7154–62.
    CAS  PubMed  Article  PubMed Central  Google Scholar 

    21.
    Hummel J, Segu S, Li Y, Irgang S, Jueppner J, Giavalisco P. Ultra performance liquid chromatography and high resolution mass spectrometry for the analysis of plant lipids. Front Plant Sci. 2011;2:54.
    CAS  PubMed  PubMed Central  Article  Google Scholar 

    22.
    Smith CA, Want EJ, O’Maille G, Abagyan R, Siuzdak G. XCMS: processing mass spectrometry data for metabolite profiling using nonlinear peak alignment, matching, and identification. Anal Chem. 2006;78:779–87.
    CAS  Article  Google Scholar 

    23.
    Kuhl C, Tautenhahn R, Böttcher C, Larson TR, Neumann S. CAMERA: an integrated strategy for compound spectra extraction and annotation of LC/MS data sets. Anal Chem. 2012;84:283–9.
    CAS  PubMed  Article  PubMed Central  Google Scholar 

    24.
    Harke MJ, Frischkorn KR, Haley ST, Aylward FO, Zehr JP, Dyhrman ST. Periodic and coordinated gene expression between a diazotroph and its diatom host. ISME J. 2019;13:118–31.
    CAS  PubMed  Article  PubMed Central  Google Scholar 

    25.
    Bolger AM, Lohse M, Usadel B. Trimmomatic: a flexible trimmer for Illumina sequence data. Bioinformatics. 2014;30:2114–20.
    CAS  PubMed  PubMed Central  Article  Google Scholar 

    26.
    Alexander H, Rouco M, Haley ST, Wilson ST, Karl DM, Dyhrman ST. Functional group-specific traits drive phytoplankton dynamics in the oligotrophic ocean. Proc Natl Acad Sci USA. 2015;112:E5972–9.
    CAS  PubMed  Article  PubMed Central  Google Scholar 

    27.
    Keeling PJ, Burki F, Wilcox HM, Allam B, Allen EE, Amaral-Zettler LA, et al. The marine microbial eukaryote transcriptome sequencing project (MMETSP): Illuminating the functional diversity of eukaryotic life in the oceans through transcriptome sequencing. PLoS Biol. 2014;12:e1001889.
    PubMed  PubMed Central  Article  CAS  Google Scholar 

    28.
    Meinicke P. UProC: tools for ultra-fast protein domain classification. Bioinformatics. 2014;31:1382–8.
    PubMed  PubMed Central  Article  CAS  Google Scholar 

    29.
    Li H, Durbin R. Fast and accurate long-read alignment with Burrows–Wheeler transform. Bioinformatics. 2010;26:589–95.
    PubMed  PubMed Central  Article  CAS  Google Scholar 

    30.
    Anders S, Pyl PT, Huber W. HTSeq—a Python framework to work with high-throughput sequencing data. Bioinformatics. 2014;31:166–9.
    PubMed  PubMed Central  Article  CAS  Google Scholar 

    31.
    Love MI, Huber W, Anders S. Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2. Genome Biol. 2014;15:550.
    PubMed  PubMed Central  Article  CAS  Google Scholar 

    32.
    Aylward FO, Boeuf D, Mende DR, Wood-Charlson EM, Vislova A, Eppley JM, et al. Diel cycling and long-term persistence of viruses in the ocean’s euphotic zone. Proc Natl Acad Sci USA. 2017;114:11446 LP–11451.
    Article  CAS  Google Scholar 

    33.
    Gifford SM, Becker JW, Sosa OA, Repeta DJ, DeLong EF. Quantitative transcriptomics reveals the growth- and nutrient-dependent response of a streamlined marine methylotroph to methanol and naturally occurring dissolved organic matter. MBio. 2016;7:e01279–16.
    CAS  PubMed  PubMed Central  Article  Google Scholar 

    34.
    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  Article  PubMed Central  Google Scholar 

    35.
    Kiełbasa SM, Wan R, Sato K, Horton P, Frith MC. Adaptive seeds tame genomic sequence comparison. Genome Res. 2011;21:487–93.
    PubMed  PubMed Central  Article  CAS  Google Scholar 

    36.
    Thaben PF, Westermark PO. Detecting rhythms in time series with RAIN. J Biol Rhythms. 2014;29:391–400.
    PubMed  PubMed Central  Article  Google Scholar 

    37.
    Benjamini Y, Hochberg Y. Controlling the false discovery rate: a practical and powerful approach to multiple testing. J R Stat Soc Ser B. 1995;57:289–300.
    Google Scholar 

    38.
    Coenen AR, Hu SK, Luo E, Muratore D, Weitz JS. A primer for microbiome time-series analysis. Front Genet. 2020;11:310.
    PubMed  PubMed Central  Article  Google Scholar 

    39.
    Fuhrman JA, Eppley RW, Hagström Å, Azam F. Diel variations in bacterioplankton, phytoplankton, and related parameters in the Southern California Bight. Mar Ecol Prog Ser. 1985;27:9–20.
    Article  Google Scholar 

    40.
    Behrenfeld MJ, Falkowski PG. A consumer’s guide to phytoplankton primary productivity models. Limnol Oceanogr. 1997;42:1479–91.
    Article  Google Scholar 

    41.
    Post AF, Dubinsky Z, Wyman K, Falkowski PG. Kinetics of light-intensity adaptation in a marine planktonic diatom. Mar Biol. 1984;83:231–8.
    Article  Google Scholar 

    42.
    Falkowski PG, Kolber Z. Variations in chlorophyll fluorescence yields in phytoplankton in the world oceans. Funct Plant Biol. 1995;22:341–55.
    Article  Google Scholar 

    43.
    Vaulot D, Marie D. Diel variability of photosynthetic picoplankton in the equatorial Pacific. J Geophys Res Ocean. 1999;104:3297–310.
    CAS  Article  Google Scholar 

    44.
    Nicholson DP, Wilson ST, Doney SC, Karl DM. Quantifying subtropical North Pacific gyre mixed layer primary productivity from Seaglider observations of diel oxygen cycles. Geophys Res Lett. 2015;42:4032–9.
    CAS  Article  Google Scholar 

    45.
    Yentsch CS. Distribution of chlorophyll and phaeophytin in the open ocean. Deep Sea Res Oceanogr Abstr. 1965;12:653–66.
    Article  Google Scholar 

    46.
    Yentsch CS, Reichert CA. The effects of prolonged darkness on photosynthesis, respiration, and chlorophyll in the marine flagellate Dunaliella euchlora. Limnol Oceanogr. 1963;8:338–42.
    Article  Google Scholar 

    47.
    Glooschenko WA, Curl H Jr., Small LF. Diel periodicity of chlorophyll a concentration in Oregon coastal waters. J Fish Res Board Can. 1972;29:1253–9.
    CAS  Article  Google Scholar 

    48.
    Cosper E. Influence of light intensity on diel variations in rates of growth, respiration and organic release of a marine diatom: comparison of diurnally constant and fluctuating light. J Plankton Res. 1982;4:705–24.
    Article  Google Scholar 

    49.
    Fouilland E, Courties C, Descolas-Gros C. Size-fractionated phytoplankton carboxylase activities in the Indian sector of the Southern Ocean. J Plankton Res. 2000;22:1185–201.
    CAS  Article  Google Scholar 

    50.
    Ragni M, d’Alcalà MR. Circadian variability in the photobiology of Phaeodactylum tricornutum: pigment content. J Plankton Res. 2007;29:141–56.
    CAS  Article  Google Scholar 

    51.
    Bidigare RR, Buttler FR, Christensen SJ, Barone B, Karl DM, Wilson ST. Evaluation of the utility of xanthophyll cycle pigment dynamics for assessing upper ocean mixing processes at Station ALOHA. J Plankton Res. 2014;36:1423–33.
    CAS  Article  Google Scholar 

    52.
    Lichtenthaler HK. Chlorophylls and carotenoids: pigments of photosynthetic biomembranes. In Methods in Enzymology (vol 148). Academic Press; 1987. p. 350–82.

    53.
    Salomon E, Bar-Eyal L, Sharon S, Keren N. Balancing photosynthetic electron flow is critical for cyanobacterial acclimation to nitrogen limitation. Biochim Biophys Acta. 2013;1827:340–7.
    CAS  PubMed  Article  PubMed Central  Google Scholar 

    54.
    Ünlü C, Drop B, Croce R, van Amerongen H. State transitions in Chlamydomonas reinhardtii strongly modulate the functional size of photosystem II but not of photosystem I. Proc Natl Acad Sci USA. 2014;111:3460–5.
    PubMed  Article  CAS  PubMed Central  Google Scholar 

    55.
    MacIntyre HL, Kana TM, Anning T, Geider RJ. Photoacclimation of photosynthesis irradiance response curves and photosynthetic pigments in microalgae and cyanobacteria. J Phycol. 2002;38:17–38.
    Article  Google Scholar 

    56.
    Karl DM, Church MJ. Microbial oceanography and the Hawaii Ocean Time-series programme. Nat Rev Microbiol. 2014;12:699–713.
    CAS  PubMed  Article  PubMed Central  Google Scholar 

    57.
    Armstrong GA. Greening in the dark: light-independent chlorophyll biosynthesis from anoxygenic photosynthetic bacteria to gymnosperms. J Photochem Photobio B Biol. 1998;43:87–100.
    CAS  Article  Google Scholar 

    58.
    Foy RH, Smith RV. The role of carbohydrate accumulation in the growth of planktonic Oscillatoria species. Br Phycol J. 1980;15:139–50.
    Article  Google Scholar 

    59.
    Cuhel RL, Ortner PB, Lean DRS. Night synthesis of protein by algae. Limnol Oceanogr. 1984;29:731–44.
    CAS  Article  Google Scholar 

    60.
    Lacour T, Sciandra A, Talec A, Mayzaud P, Bernard O. Diel variations of carbohydrates and neutral lipids in nitrogen‐sufficient and nitrogen‐starved cyclostat cultures of Isochrysis sp. 1. J Phycol. 2012;48:966–75.
    PubMed  Article  PubMed Central  Google Scholar 

    61.
    Lorenzen CJ. A note on the chlorophyll and phaeophytin content of the chlorophyll maximum. Limnol Oceanogr. 1965;10:482–3.
    Article  Google Scholar 

    62.
    Jeffrey SW. Profiles of photosynthetic pigments in the ocean using thin-layer chromatography. Mar Biol. 1974;26:101–10.
    CAS  Article  Google Scholar 

    63.
    Head EJH, Horne EPW. Pigment transformation and vertical flux in an area of convergence in the North Atlantic. Deep Sea Res Part II Top Stud Oceanogr. 1993;40:329–46.
    Article  Google Scholar 

    64.
    Sun M-Y, Lee C, Aller RC. Anoxic and oxic degradation of 14C-labeled chloropigments and a 14C-labeled diatom in Long Island Sound sediments. Limnol Oceanogr. 1993;38:1438–51.
    CAS  Article  Google Scholar 

    65.
    Champalbert G, Neveux J, Gaudy R, Le Borgne R. Diel variations of copepod feeding and grazing impact in the high-nutrient, low-chlorophyll zone of the equatorial Pacific Ocean (0°; 3° S, 180°). J Geophys Res Ocean. 2003;108:8145. https://doi.org/10.1029/2001JC000810.

    66.
    Holzwarth AR, Müller MG, Reus M, Nowaczyk M, Sander J, Rögner M. Kinetics and mechanism of electron transfer in intact photosystem II and in the isolated reaction center: Pheophytin is the primary electron acceptor. Proc Natl Acad Sci USA. 2006;103:6895–6900.
    CAS  Article  Google Scholar 

    67.
    van Grondelle R, Dekker JP, Gillbro T, Sundstrom V. Energy transfer and trapping in photosynthesis. Biochim Biophys Acta. 1994;1187:1–65.
    CAS  Article  Google Scholar 

    68.
    Shimoda Y, Ito H, Tanaka A. Arabidopsis STAY-GREEN, Mendel’s green cotyledon gene, encodes magnesium-dechelatase. Plant Cell. 2016;28:2147–60.
    CAS  PubMed  PubMed Central  Article  Google Scholar 

    69.
    Oster U, Tanaka R, Tanaka A, Rüdiger W. Cloning and functional expression of the gene encoding the key enzyme for chlorophyll b biosynthesis (CAO) from Arabidopsis thaliana. Plant J. 2000;21:305–10.
    CAS  PubMed  Article  PubMed Central  Google Scholar 

    70.
    Vavilin D, Vermaas W. Continuous chlorophyll degradation accompanied by chlorophyllide and phytol reutilization for chlorophyll synthesis in Synechocystis sp. PCC 6803. Biochim Biophys Acta. 2007;1767:920–9.
    CAS  PubMed  Article  PubMed Central  Google Scholar 

    71.
    Pružinská A, Tanner G, Anders I, Roca M, Hörtensteiner S. Chlorophyll breakdown: pheophorbide a oxygenase is a Rieske-type iron–sulfur protein, encoded by the accelerated cell death 1 gene. Proc Natl Acad Sci USA. 2003;100:15259–64.
    PubMed  Article  CAS  PubMed Central  Google Scholar 

    72.
    Baroli I, Niyogi KK. Molecular genetics of xanthophyll–dependent photoprotection in green algae and plants. Philos Trans R Soc Lond Ser B Biol Sci. 2000;355:1385–94.
    CAS  Article  Google Scholar 

    73.
    Andersen RA, Bidigare RR, Keller MD, Latasa M. A comparison of HPLC pigment signatures and electron microscopic observations for oligotrophic waters of the North Atlantic and Pacific Oceans. Deep Sea Res Part II Top Stud Oceanogr. 1996;43:517–37.
    CAS  Article  Google Scholar 

    74.
    Obata M, Taguchi S. The xanthophyll-cycling pigment dynamics of Isochrysis galbana (Prymnesiophyceae) during light-dark transition. Plankt Benthos Res. 2012;7:101–10.
    Article  Google Scholar 

    75.
    Sajilata MG, Singhal RS, Kamat MY. The carotenoid pigment zeaxanthin—a review. Compr Rev Food Sci Food Saf. 2008;7:29–49.
    CAS  Article  Google Scholar 

    76.
    Ramel F, Birtic S, Ginies C, Soubigou-Taconnat L, Triantaphylidès C, Havaux M. Carotenoid oxidation products are stress signals that mediate gene responses to singlet oxygen in plants. Proc Natl Acad Sci USA. 2012;109:5535–40.
    CAS  PubMed  Article  PubMed Central  Google Scholar 

    77.
    Goss R. Substrate specificity of the violaxanthin de-epoxidase of the primitive green alga Mantoniella squamata (Prasinophyceae). Planta. 2003;217:801–12.
    CAS  PubMed  Article  PubMed Central  Google Scholar 

    78.
    Viviani DA, Karl DM, Church MJ. Variability in photosynthetic production of dissolved and particulate organic carbon in the North Pacific Subtropical Gyre. Front Mar Sci. 2015;2:73.
    Article  Google Scholar 

    79.
    Elling FJ, Becker KW, Könneke M, Schröder JM, Kellermann MY, Thomm M, et al. Respiratory quinones in Archaea: phylogenetic distribution and application as biomarkers in the marine environment. Environ Microbiol. 2016;18:692–707.
    CAS  PubMed  Article  PubMed Central  Google Scholar 

    80.
    Becker KW, Elling FJ, Schröder JM, Lipp JS, Goldhammer T, Zabel M, et al. Isoprenoid quinones resolve the stratification of redox processes in a biogeochemical continuum from the photic zone to deep anoxic sediments of the Black Sea. Appl Environ Microbiol. 2018;84:e2736–17.

    81.
    Nowicka B, Kruk J. Occurrence, biosynthesis and function of isoprenoid quinones. Biochim Biophys Acta. 2010;1797:1587–605.
    CAS  PubMed  Article  PubMed Central  Google Scholar 

    82.
    Kruk J, Trebst A. Plastoquinol as a singlet oxygen scavenger in photosystem II. Biochim Biophys Acta. 2008;1777:154–62.
    CAS  PubMed  Article  PubMed Central  Google Scholar 

    83.
    Szymańska R, Kruk J. Plastoquinol is the main prenyllipid synthesized during acclimation to high light conditions in arabidopsis and is converted to plastochromanol by tocopherol cyclase. Plant Cell Physiol. 2010;51:537–45.
    PubMed  Article  CAS  PubMed Central  Google Scholar 

    84.
    Agrawal S, Jaswal K, Shiver AL, Balecha H, Patra T, Chaba R. A genome-wide screen in Escherichia coli reveals that ubiquinone is a key antioxidant for metabolism of long chain fatty acids. J Biol Chem. 2017;292:20086–99.
    CAS  PubMed  PubMed Central  Article  Google Scholar 

    85.
    Ksas B, Légeret B, Ferretti U, Chevalier A, Pospíšil P, Alric J, et al. The plastoquinone pool outside the thylakoid membrane serves in plant photoprotection as a reservoir of singlet oxygen scavengers. Plant Cell Environ. 2018;41:2277–87.
    CAS  PubMed  Article  PubMed Central  Google Scholar 

    86.
    Foyer CH, Noctor G. Redox sensing and signalling associated with reactive oxygen in chloroplasts, peroxisomes and mitochondria. Physiol Plant. 2003;119:355–64.
    CAS  Article  Google Scholar 

    87.
    Long SP, Humphries S, Falkowski PG. Photoinhibition of photosynthesis in nature. Annu Rev Plant Physiol Plant Mol Biol. 1994;45:633–62.
    CAS  Article  Google Scholar 

    88.
    Triantaphylidès C, Havaux M. Singlet oxygen in plants: production, detoxification and signaling. Trends Plant Sci. 2009;14:219–28.
    PubMed  Article  CAS  PubMed Central  Google Scholar 

    89.
    Pospíšil P. Molecular mechanisms of production and scavenging of reactive oxygen species by photosystem II. Biochim Biophys Acta. 2012;1817:218–31.
    PubMed  Article  CAS  PubMed Central  Google Scholar 

    90.
    Lichtenthaler HK. Biosynthesis, accumulation and emission of carotenoids, α-tocopherol, plastoquinone, and isoprene in leaves under high photosynthetic irradiance. Photosynth Res. 2007;92:163–79.
    CAS  PubMed  Article  PubMed Central  Google Scholar 

    91.
    Shi T, Ilikchyan I, Rabouille S, Zehr JP. Genome-wide analysis of diel gene expression in the unicellular N2-fixing cyanobacterium Crocosphaera watsonii WH 8501. ISME J. 2010;4:621.
    CAS  PubMed  Article  PubMed Central  Google Scholar 

    92.
    Muñoz-Marín M, del C, Shilova IN, Shi T, Farnelid H, Cabello AM, et al. The transcriptional cycle is suited to daytime N2 fixation in the unicellular cyanobacterium “Candidatus Atelocyanobacterium thalassa” (UCYN-A). MBio. 2019;10:e02495–18.
    PubMed  PubMed Central  Google Scholar 

    93.
    Ashworth J, Coesel S, Lee A, Armbrust EV, Orellana MV, Baliga NS. Genome-wide diel growth state transitions in the diatom Thalassiosira pseudonana. Proc Natl Acad Sci USA. 2013;110:7518–23.
    CAS  PubMed  Article  PubMed Central  Google Scholar 

    94.
    Smith SR, Gillard JTF, Kustka AB, McCrow JP, Badger JH, Zheng H, et al. Transcriptional orchestration of the global cellular response of a model pennate diatom to diel light cycling under iron limitation. PLOS Genet. 2016;12:e1006490.
    PubMed  PubMed Central  Article  CAS  Google Scholar 

    95.
    Nymark M, Valle KC, Brembu T, Hancke K, Winge P, Andresen K, et al. An integrated analysis of molecular acclimation to high light in the marine diatom Phaeodactylum tricornutum. PLoS ONE. 2009;4:e7743.
    PubMed  PubMed Central  Article  CAS  Google Scholar 

    96.
    Gibon Y, Usadel B, Blaesing OE, Kamlage B, Hoehne M, Trethewey R, et al. Integration of metabolite with transcript and enzyme activity profiling during diurnal cycles in Arabidopsis rosettes. Genome Biol. 2006;7:R76.
    PubMed  PubMed Central  Article  Google Scholar 

    97.
    Waldbauer JR, Rodrigue S, Coleman ML, Chisholm SW. Transcriptome and proteome dynamics of a light-dark synchronized bacterial cell cycle. PLoS ONE. 2012;7:e43432.
    CAS  PubMed  PubMed Central  Article  Google Scholar 

    98.
    Kana TM, Geider RJ, Critchley C. Regulation of photosynthetic pigments in micro-algae by multiple environmental factors: a dynamic balance hypothesis. N Phytol. 1997;137:629–38.
    CAS  Article  Google Scholar 

    99.
    Escoubas J-M, Lomas M, LaRoche J, Falkowski PG. Light intensity regulation of cab gene transcription is signaled by the redox state of the plastoquinone pool. Proc Natl Acad Sci USA. 1995;92:10237–41.
    CAS  PubMed  Article  PubMed Central  Google Scholar 

    100.
    Van Mooy BAS, Devol AH. Assessing nutrient limitation of Prochlorococcus in the North Pacific Subtropical Gyre by using an RNA capture method. Limnol Oceanogr. 2008;53:78–88.
    Article  Google Scholar 

    101.
    Moore CM, Mills MM, Arrigo KR, Berman-Frank I, Bopp L, Boyd PW, et al. Processes and patterns of oceanic nutrient limitation. Nat Geosci. 2013;6:701–10.
    CAS  Article  Google Scholar 

    102.
    Muratore D, Boysen AK, Harke MJ, Becker KW, Casey JR, Coesel SN, et al. Community-scale synchronization and temporal partitioning of gene expression, metabolism, and lipid biosynthesis in oligotrophic ocean surface waters. 2020. https://www.biorxiv.org/content/10.1101/2020.05.15.098020v1.

    103.
    Saito MA, Bertrand EM, Dutkiewicz S, Bulygin VV, Moran DM, Monteiro FM, et al. Iron conservation by reduction of metalloenzyme inventories in the marine diazotroph Crocosphaera watsonii. Proc Natl Acad Sci USA. 2011;108:2184–9.
    CAS  PubMed  Article  PubMed Central  Google Scholar 

    104.
    Marchetti A, Parker MS, Moccia LP, Lin EO, Arrieta AL, Ribalet F, et al. Ferritin is used for iron storage in bloom-forming marine pennate diatoms. Nature. 2008;457:467.
    PubMed  Article  CAS  PubMed Central  Google Scholar 

    105.
    White AE, Barone B, Letelier RM, Karl DM. Productivity diagnosed from the diel cycle of particulate carbon in the North Pacific Subtropical Gyre. Geophys Res Lett. 2017;44:3752–60.
    CAS  Article  Google Scholar  More

  • in

    High fidelity defines the temporal consistency of host-parasite interactions in a tropical coastal ecosystem

    1.
    Dobson, A., Lafferty, K. D., Kuris, A. M., Hechinger, R. F. & Jetz, W. Homage to Linnaeus: how many parasites? How many hosts?. Proc. Natl. Acad. Sci. USA 105, 11482–11489 (2008).
    ADS  CAS  PubMed  Article  PubMed Central  Google Scholar 
    2.
    Minchella, D. J. & Scott, M. E. Parasitism: a cryptic determinant of animal community structure. Trends Ecol. Evol. 8, 250–254 (1991).
    Article  Google Scholar 

    3.
    Hudson, P. J., Rizzoli, A. P., Grenfell, B. T., Heesterbeek, J. A. P. & Dobson, A. P. Ecology of wildlife diseases. In The Ecology of Wildlife Diseases (eds Hudson, P. J. et al.) 1–5 (Oxford University Press, Oxford, 2002).
    Google Scholar 

    4.
    Hamilton, W. D. & Zuk, M. Heritable true fitness and bright birds: a role for parasites?. Science 80, 384–387 (1982).
    ADS  Article  Google Scholar 

    5.
    Spencer, K. A., Buchanan, K. L., Leitner, S., Goldsmith, A. R. & Catchpole, C. K. Parasites affect song complexity and neural development in a songbird. Proc. R. Soc. Lond. B. 1576, 2037–2043 (2005).
    Google Scholar 

    6.
    Asghar, M. et al. Hidden costs of infection: chronic malaria accelerates telomere degradation and senescence in wild birds. Science 6220, 436–438 (2015).
    ADS  Article  CAS  Google Scholar 

    7.
    van Riper, C., van Riper, S. G., Goff, M. L. & Laird, M. The epizootiology and ecological significance of malaria in Hawaiian land birds. Ecol. Monogr. 4, 327–344 (1986).
    Article  Google Scholar 

    8.
    Atkinson, C., Woods, K., Dusek, R., Sileo, L. & Iko, W. Wildlife disease and conservation in Hawaii: Pathogenicity of avian malaria (Plasmodium relictum) in experimentally infected Iiwi (Vestiaria coccinea). Parasitology 111, S59–S69 (1995).
    PubMed  Article  PubMed Central  Google Scholar 

    9.
    Ings, T. C. et al. Ecological networks: beyond food webs. J. Anim. Ecol. 78, 253–269 (2009).
    PubMed  Article  PubMed Central  Google Scholar 

    10.
    Bellay, S. et al. Host-parasite networks: an integrative overview with tropical examples. In Ecological Networks in the Tropics: An Integrative Overview of Species Interactions from Some of the Most Species-Rich Habitats on Earth (eds Dáttilo, W. & Rico-Gray, V.) 127–140 (Springer, Berlin, 2018).
    Google Scholar 

    11
    Valkiūnas, G. Avian Malaria Parasites and Other Haemosporidia (CRC Press, Boca Raton, 2005).
    Google Scholar 

    12.
    Ricklefs, R. E. et al. Species formation in avian malaria parasites. Proc. Natl Acad. Sci. USA 111, 14816–14821 (2014).
    ADS  CAS  PubMed  Article  PubMed Central  Google Scholar 

    13.
    Hellgren, O., Pérez-Triz, J. & Bensch, S. A jack-of-all-trades and still a master of some: prevalence and host range in avian malaria and related blood parasites. Ecol. 90, 2840–2849 (2009).
    Article  Google Scholar 

    14.
    Clark, N., Clegg, S. M. & Lima, M. R. A review of global diversity in avian haemosporidians (Plasmodium and Haemoproteus: Haemosporida): new insights from molecular data. Int. J. Parasitol. 44, 329–338 (2014).
    PubMed  Article  PubMed Central  Google Scholar 

    15.
    Moens, M. A. J. & Pérez-Tris, J. Discovering potential sources of emerging pathogens: South America is a reservoir of generalist avian blood parasites. Int. J. Parasitol. 46, 41–49 (2016).
    PubMed  Article  PubMed Central  Google Scholar 

    16.
    Lacorte, G. A. et al. Exploring the diversity and distribution of Neotropical avian malaria parasites: a molecular survey from Southeast Brazil. PLoS ONE 8, e57770 (2013).
    ADS  CAS  PubMed  PubMed Central  Article  Google Scholar 

    17.
    Janzen, D. H. Herbivores and the number of tree species in tropical forests. Am. Nat. 104, 501–528 (1970).
    Article  Google Scholar 

    18.
    Connell, J. H. On the role of natural enemies in preventing competitive exclusion in some marine animals and in forest trees. In Dynamics of Populations (eds den Boer, P. J. & Gradwell, G. R.) 298–312 (Centre for Agricultural Publishing and Documentation, Wageningen, 1971).
    Google Scholar 

    19.
    MacArthur, R. Fluctuations of animal populations and a measure of community stability. Ecol. 36, 533–536 (1955).
    Article  Google Scholar 

    20.
    Rohde, K. Latitudinal gradients in species diversity: the search for the primary cause. Oikos 65, 514–527 (1992).
    Article  Google Scholar 

    21.
    Willig, M. R., Kaufman, D. M. & Stevens, R. D. Latitudinal gradients of biodiversity: Pattern, process, scale, and synthesis. Annu. Rev. Ecol. Evol. Syst. 34, 273–309 (2003).
    Article  Google Scholar 

    22.
    Svensson-Coelho, M., Ellis, V. A., Loiselle, B. A., Blake, J. G. & Ricklefs, R. E. Reciprocal specialization in multihost malaria parasite communities of birds: a temperate-tropical comparison. Am. Nat. 184, 624–635 (2014).
    PubMed  Article  PubMed Central  Google Scholar 

    23.
    Morris, R. J., Gripenberg, S., Lewis, O. T. & Roslin, T. Antagonistic interaction networks are structured independently of latitude and host guild. Ecol. Lett. 17, 340–349 (2014).
    PubMed  Article  PubMed Central  Google Scholar 

    24.
    Blüthgen, N., Menzel, F. & Blüthgen, N. Measuring specialization in species interaction networks. BMC Ecol. 6, 1–12 (2006).
    Article  Google Scholar 

    25.
    Carstensen, D. W., Sabatino, M., Trøjelsgaard, K. & Morellato, L. P. C. Beta diversity of plant-pollinator networks and the spatial turnover of pairwise interactions. PLoS ONE 9, e112903 (2014).
    ADS  PubMed  PubMed Central  Article  CAS  Google Scholar 

    26.
    Poulin, R. Network analysis shining light on parasite ecology and diversity. Trends Parasitol. 26, 492–498 (2010).
    PubMed  Article  PubMed Central  Google Scholar 

    27.
    Simanonok, M. P. & Burkle, L. A. Partitioning interaction turnover among alpine pollination networks: spatial, temporal, and environmental patterns. Ecosphere 5, art149 (2014).
    Article  Google Scholar 

    28.
    Poulin, R., Krasnov, B. R., Pilosof, S. & Thieltges, D. W. Phylogeny determines the role of helminth parasites in intertidal food webs. J. Anim. Ecol. 82, 1265–1275 (2013).
    PubMed  Article  PubMed Central  Google Scholar 

    29.
    Robinson, M. L. & Strauss, S. Generalists are more specialized in low-resource habitats, increasing stability of ecological network structure. Proc. Natl Acad. Sci. USA 117, 2043–2048 (2020).
    CAS  PubMed  Article  PubMed Central  Google Scholar 

    30
    Dallas, T. & Cornelius, E. Co-extinction in a host-parasite network : identifying key hosts for network stability. Sci. Rep. 5, 1–10 (2015).
    Article  CAS  Google Scholar 

    31.
    Mccurdy, D. G., Shutler, D., Mullie, A. & Forbes, M. R. Sex-biased parasitism of avian hosts: relations to blood parasite taxon and mating system. Oikos 82, 303–312 (1998).
    CAS  Article  Google Scholar 

    32.
    Fecchio, A., Lima, M. R., Silveira, P., Braga, ÉM. & Marini, M. Â. High prevalence of blood parasites in social birds from a neotropical savanna in Brazil. Emu. 111, 132–138 (2011).
    Article  Google Scholar 

    33.
    Laurance, S. G. W. et al. Habitat fragmentation and ecological traits influence the prevalence of avian blood parasites in a tropical rainforest landscape. PLoS ONE 8, e76227 (2013).
    ADS  CAS  PubMed  PubMed Central  Article  Google Scholar 

    34.
    Lutz, H. L. et al. Parasite prevalence corresponds to host life history in a diverse assemblage of afrotropical birds and haemosporidian parasites. PLoS ONE 10, e0121254 (2015).
    PubMed  PubMed Central  Article  CAS  Google Scholar 

    35.
    González, A. D. et al. Mixed species flock, nest height, and elevation partially explain avian haemoparasite prevalence in Colombia. PLoS ONE 9, e100695 (2014).
    ADS  PubMed  PubMed Central  Article  CAS  Google Scholar 

    36.
    Matthews, A. E. et al. Avian haemosporidian prevalence and its relationship to host life histories in eastern Tennessee. J. Ornithol. 157, 533–548 (2016).
    Article  Google Scholar 

    37.
    Pinheiro, R. B. P. et al. Trade-offs and resource breadth processes as drivers of performance and specificity in a host–parasite system: a new integrative hypothesis. Int. J. Parasitol. 2, 115–121 (2016).
    MathSciNet  Article  Google Scholar 

    38.
    Mello, A. A. R. et al. The modularity of seed dispersal: differences in structure and robustness between bat– and bird–fruit networks. Oecologia 167, 131–140 (2015).
    ADS  Article  Google Scholar 

    39.
    Thompson, J. N. The evolution of species interactions. Science 284, 2116–2118 (1999).
    CAS  PubMed  Article  PubMed Central  Google Scholar 

    40.
    Fortuna, M. A. et al. Nestedness vs modularity in ecological networks: two side of the same coin?. J. Anim. Ecol. 79, 811–817 (2010).
    PubMed  PubMed Central  Google Scholar 

    41.
    Bellay, S., Lima, D. P., Takemoto, R. M. & Luque, J. L. A host-endoparasite network of Neotropical marine fish: are there organizational patterns?. Parasitology 138, 1945–1952 (2011).
    PubMed  Article  PubMed Central  Google Scholar 

    42.
    Krasnov, B. R. et al. Phylogenetic signal in module composition and species connectivity in compartmentalized host-parasite networks. Am. Nat. 179, 501–511 (2012).
    PubMed  Article  PubMed Central  Google Scholar 

    43.
    Bellay, S. et al. Developmental stage of parasites influences the structure of fish-parasite networks. PLoS ONE 8, e75710 (2013).
    ADS  CAS  PubMed  PubMed Central  Article  Google Scholar 

    44
    Thompson, J. N. The Geographic Mosaic of Coevolution (University of Chicago Press, Chicago, 2005).
    Google Scholar 

    45.
    Michelan, T. S., Thomaz, S. M., Mormul, R. P. & Carvalho, P. Effects of an exotic invasive macrophyte (tropical signalgrass) on native plant community composition, species richness and functional diversity. Freshw. Biol. 55, 1315–1326 (2010).
    Article  Google Scholar 

    46.
    Krasnov, B. R. et al. Assembly rules of ectoparasite communities across scales: combining patterns of abiotic factors, host composition, geographic space, phylogeny and traits. Ecography 38, 184–197 (2015).
    Article  Google Scholar 

    47.
    LaPointe, D. A., Atkinson, C. T. & Samuel, M. D. Ecology and conservation biology of avian malaria. Ann. N. Y. Acad. Sci. 1249, 211–226 (2012).
    ADS  PubMed  Article  PubMed Central  Google Scholar 

    48.
    CaraDonna, P. et al. Interaction rewiring and the rapid turnover of plant–pollinator networks. Ecol. Let. 20, 385–394 (2017).
    Article  Google Scholar 

    49.
    Fallon, S. M., Rickfles, R. E., Latta, S. C. & Bermingham, E. Temporal stability of insular avian malarial parasite communities. Proc. R. Soc. Lond. B. 271, 493–500 (2004).
    CAS  Article  Google Scholar 

    50.
    Ferreira Junior, F. C. et al. Habitat modification and seasonality influence avian haemosporidian parasite distributions in southeastern Brazil. PLoS ONE 12, e0178791 (2017).
    PubMed  PubMed Central  Article  CAS  Google Scholar 

    51.
    Knowles, S. C. L., Palinauskas, V. & Sheldon, B. C. Chronic malaria infections increase family inequalities and reduce parental fitness: experimental evidence from a wild bird population. J. Evol. Biol. 23, 557–569 (2010).
    CAS  PubMed  Article  PubMed Central  Google Scholar 

    52.
    Poisot, T., Stouffer, D. B. & Gravel, D. Beyond species: why ecological interaction networks vary through space and time. Oikos 124, 243–251 (2015).
    Article  Google Scholar 

    53.
    Castilheiro, W., Santos-filho, M. & Oliveira, R. F. Beta diversity of birds (Passeriformes, Linnaeus, 1758) in Southern Amazon. Ciências Anim. Bras. 18, 1–18 (2017).
    Google Scholar 

    54.
    Yen, J. D. L., Fleishman, E., Fogarty, F. & Dobkin, D. S. Relating beta diversity of birds and butterflies in the Great Basin to spatial resolution, environmental variables and trait-based groups. Global Ecol. Biogeogr. 28, 328–340 (2019).
    Article  Google Scholar 

    55.
    Woodward, G. et al. Body size in ecological networks. Trends Ecol. Evol. 7, 402–409 (2005).
    Article  Google Scholar 

    56.
    Campião, K. M., Ribas, A. C. A., Morais, D. H., Silva, R. J. & Tavares, L. E. R. How many parasites species a frog might have? Determinants of parasite diversity in South American anurans. PLoS ONE 10, e0140577 (2015).
    PubMed  PubMed Central  Article  CAS  Google Scholar 

    57.
    Lima, D. P., Giacomini, H. C., Takemoto, R. M., Agostinho, A. A. & Bini, L. M. Patterns of interactions of a large fish-parasite network in a tropical floodplain. J. Anim. Ecol. 81, 905–913 (2012).
    PubMed  Article  PubMed Central  Google Scholar 

    58.
    Brito, S. V. et al. Phylogeny and micro-habitats utilized by lizards determine the composition of their endoparasites in the semiarid Caatinga of Northeast Brazil. Parasitol. Res. 11, 3963–3972 (2014).
    Article  Google Scholar 

    59.
    Graham, S. P., Hassan, H. K., Burket-Cadena, N. D., Guyer, C. & Unnasch, T. R. Nestedness of ectoparasite-vertebrate host networks. PLoS ONE 18, e7873 (2009).
    ADS  Article  CAS  Google Scholar 

    60.
    Thébault, E. & Fontaine, C. Stability of ecological communities and the architecture of mutualistic and trophic networks. Science 329, 853–856 (2010).
    ADS  PubMed  Article  CAS  PubMed Central  Google Scholar 

    61.
    Poisot, T., Canard, E., Mouquet, N. & Hochberg, M. E. A comparative study of ecological specialization estimators. Methods Ecol. Evol. 3, 537–544 (2012).
    Article  Google Scholar 

    62.
    Wilkinson, L. C., Handel, C. M., Van Hemert, C., Loiseau, C. & Sehgal, R. N. M. Avian malaria in a boreal resident species: long-term temporal variability, and increased prevalence in birds with avian keratin disorder. Int. J. Parasitol. 46, 281–290 (2016).
    PubMed  Article  PubMed Central  Google Scholar 

    63.
    Møller, A. P., Merino, S., Brown, C. R. & Robertson, R. J. Immune defense and host sociality: a comparative study of swallows and martins. Am. Nat. 158, 136–145 (2001).
    PubMed  Article  PubMed Central  Google Scholar 

    64
    Medeiros, M. C., Hamer, G. L. & Ricklefs, R. E. Host compatibility rather than vector-host-encounter rate determines the host range of avian Plasmodium parasites. Proc. R. Soc. Lond. B. 280, 20122947 (2013).
    Google Scholar 

    65.
    Clark, N. & Clegg, S. M. Integrating phylogenetic and ecological distances reveals new insights into parasite host specificity. Mol. Ecol. 26, 3074–3086 (2017).
    PubMed  Article  PubMed Central  Google Scholar 

    66.
    Costa, F. V. et al. Few ant species play a central role linking different plant resources in a network in rupestrian grasslands. PLoS ONE 12, e0167161 (2016).
    Article  CAS  Google Scholar 

    67.
    Fagundes, R. et al. Differences among ant species in plant protection are related to production of extrafloral nectar and degree of leaf herbivory. Biol. J. Linn. Soc. 122, 71–83 (2016).
    Article  Google Scholar 

    68.
    Alvares, C. A., Stape, J. L., Sentelhas, P. C., Gonçalves, J. L. M. & Sparovek, G. Köppen’s climate classification map for Brazil. Meteorol. Z. 226, 711–728 (2013).
    Article  Google Scholar 

    69.
    Rodrigues, R. A. et al. Using a multistate occupancy approach to determine molecular diagnostic accuracy and factors afecting avian haemosporidian infections. Sci. Rep. 10, 8480 (2020).
    ADS  CAS  PubMed  PubMed Central  Article  Google Scholar 

    70.
    Sambrook, J. & Russell, D. W. Molecular Cloning: A Laboratory Manual (Cold Spring Harbor Laboratory Press, New York, 2001).
    Google Scholar 

    71.
    Fallon, A. S. M., Ricklefs, R. E., Swanson, B. L. & Bermingham, E. Detecting avian malaria: an improved polymerase chain reaction diagnostic. J. Parasitol. 89, 1044–1047 (2003).
    CAS  PubMed  Article  PubMed Central  Google Scholar 

    72.
    Sanguinetti, C. J., Neto, E. D. & Simpson, A. J. G. Rapid silver staining and recovery of PCR products separated on polyacrylamide gels. Biotechniques 17, 915–919 (1994).
    Google Scholar 

    73.
    Hellgren, O., Waldenström, J. & Bensch, S. A new PCR assay for simultaneous studies of Leucocytozoon, Plasmodium, and Haemoproteus from avian blood. J. Parasitol. 90, 797–802 (2004).
    CAS  PubMed  Article  PubMed Central  Google Scholar 

    74.
    Ewing, B., Hillier, L., Wendl, M. C. & Green, P. Base-calling of automated sequencer traces using phred. I. Accuracy assessment. Genome Res. 3, 175–185 (1998).
    Article  Google Scholar 

    75.
    Bensch, S., Hellgren, O. & Pérez-Tris, J. MalAvi: a public database of malaria parasites and related haemosporidians in avian hosts based on mitochondrial cytochrome b lineages. Mol. Ecol. Resour. 9, 1353–1358 (2009).
    PubMed  Article  PubMed Central  Google Scholar 

    76.
    Baselga, A. Partitioning the turnover and nestedness components of beta diversity. Glob. Ecol. Biogeogr. 19, 134–143 (2010).
    Article  Google Scholar 

    77.
    Whittaker, R. H. Vegetation of the Siskiyou Mountains, Oregon and California. Ecol. Monogr. 30, 279–338 (1960).
    Article  Google Scholar 

    78.
    Baselga, A. & Orme, C. D. L. betapart: an R package for the study of beta diversity. Methods Ecol. Evol. 3, 808–812 (2012).
    Article  Google Scholar 

    79.
    R Core Team. R: A Language and Environment for Statistical Computing. https://www.R-project.org/ (2017).

    80.
    Fründ, J., McCann, K. S. & Williams, N. M. Sampling bias is a challenge for quantifying specialization and network structure: lessons from a quantitative niche model. Oikos 125, 502–513 (2016).
    Article  Google Scholar 

    81.
    Dormann, C. F. & Strauss, R. A method for detecting modules in quantitative bipartite networks. Methods Ecol. Evol. 5, 90–98 (2014).
    Article  Google Scholar 

    82.
    Oksanen, J. F. et al. Vegan: Community. Ecology Package. https://cran.r-project.org/package=vegan (2016).

    83.
    Batagelj, V. & Mrvar, A. Pajek–a program for large network analysis. Connections 21, 47–57 (1998).
    Google Scholar  More

  • in

    Genome-wide genetic diversity yields insights into genomic responses of candidate climate-selected loci in an Andean wetland plant

    1.
    IPBES. Summary for Policymakers of the Global Assessment Report on Biodiversity and Ecosystem Services of the Intergovernmental Science-Policy Platform on Biodiversity and Ecosystem Services (IPBES Secretariat, Bonn, 2019).
    Google Scholar 
    2.
    Eizaguirre, C. & Baltazar-Soares, M. Evolutionary conservation-evaluating the adaptive potential of species. Evol. Appl. 7, 963–967 (2014).
    PubMed Central  Article  Google Scholar 

    3.
    Razgour, O. et al. Considering adaptive genetic variation in climate change vulnerability assessment reduces species range loss projections. Proc. Natl. Acad. Sci. USA 116, 10418–10423 (2019).
    PubMed  Article  CAS  PubMed Central  Google Scholar 

    4.
    Frankham, R., Ballou, J. D. & Briscoe, D. A. Introduction to Conservation Genetics (Cambridge University Press, Cambridge, 2010).
    Google Scholar 

    5.
    Hoffmann, A. A., Sgro, C. M. & Kristensen, T. N. Revisiting adaptive potential, population size, and conservation. Trends Ecol. Evol. 32, 506–517 (2017).
    PubMed  Article  PubMed Central  Google Scholar 

    6.
    Allendorf, F. W., Luikart, G. H. & Aitken, S. N. Conservation and the Genetics of POPULATIONS (Wiley Blackwell, Malden, 2012).
    Google Scholar 

    7.
    Caballero, A. & Garcia-Dorado, A. Allelic diversity and its implications for the rate of adaptation. Genetics 195, 1373–1384 (2013).
    PubMed  PubMed Central  Article  Google Scholar 

    8.
    Lopez-Cortegano, E. et al. Optimal management of genetic diversity in subdivided populations. Front. Genet. 10, 843 (2019).
    PubMed  PubMed Central  Article  Google Scholar 

    9.
    Frankham, R. Genetics and extinction. Biol. Conserv. 126, 131–140 (2005).
    Article  Google Scholar 

    10.
    Mable, B. K. Conservation of adaptive potential and functional diversity: Integrating old and new approaches. Conserv. Genet. 20, 89–100 (2019).
    Article  CAS  Google Scholar 

    11.
    Holderegger, R., Kamm, U. & Gugerli, F. Adaptive vs. neutral genetic diversity: Implications for landscape genetics. Landsc. Ecol. 21, 797–807 (2006).
    Article  Google Scholar 

    12.
    Kirk, H. & Freeland, J. R. Applications and implications of neutral versus non-neutral markers in molecular ecology. Int. J. Mol. Sci. 12, 3966–3988 (2011).
    PubMed  PubMed Central  Article  Google Scholar 

    13.
    Yildirim, Y., Tinnert, J. & Forsman, A. Contrasting patterns of neutral and functional genetic diversity in stable and disturbed environments. Ecol. Evol. 8, 12073–12089 (2018).
    PubMed  PubMed Central  Article  Google Scholar 

    14.
    Reed, D. H. & Frankham, R. How closely correlated are molecular and quantitative measures of genetic variation? A meta-analysis. Evolution 55, 1095–1103 (2001).
    PubMed  Article  CAS  PubMed Central  Google Scholar 

    15.
    Willi, Y. & Hoffmann, A. A. Demographic factors and genetic variation influence population persistence under environmental change. J. Evol. Biol. 22, 124–133 (2009).
    PubMed  Article  PubMed Central  Google Scholar 

    16.
    Matuszewski, S., Hermisson, J. & Kopp, M. Catch me if you can: Adaptation from standing genetic variation to a moving phenotypic optimum. Genetics 200, 1255–1272 (2015).
    PubMed  PubMed Central  Article  Google Scholar 

    17.
    Kirkpatrick, M. & Barton, N. H. Evolution of a species’ range. Am. Nat. 150, 1–23 (1997).
    PubMed  Article  CAS  PubMed Central  Google Scholar 

    18.
    Bridle, J. R., Polechova, J., Kawata, M. & Butlin, R. K. Why is adaptation prevented at ecological margins? New insights from individual-based simulations. Ecol. Lett. 13, 485–494 (2010).
    PubMed  Article  PubMed Central  Google Scholar 

    19.
    Tigano, A. & Friesen, V. L. Genomics of local adaptation with gene flow. Mol. Ecol. 25, 2144–2164 (2016).
    PubMed  Article  PubMed Central  Google Scholar 

    20.
    Jacob, S. et al. Gene flow favours local adaptation under habitat choice in ciliate microcosms. Nat. Ecol. Evol. 1, 1407–1410 (2017).
    PubMed  Article  PubMed Central  Google Scholar 

    21.
    Leffler, E. M. et al. Revisiting an old riddle: What determines genetic diversity levels within species?. PLoS Biol. 10, e1001388 (2012).
    PubMed  PubMed Central  Article  CAS  Google Scholar 

    22.
    Corbett-Detig, R. B., Hartl, D. L. & Sackton, T. B. Natural selection constrains neutral diversity across a wide range of species. PLoS Biol. 13, e1002112 (2015).
    PubMed  PubMed Central  Article  CAS  Google Scholar 

    23.
    Ellegren, H. & Galtier, N. Determinants of genetic diversity. Nat. Rev. Genet. 17, 422–433 (2016).
    PubMed  Article  CAS  PubMed Central  Google Scholar 

    24.
    Zhu, Z. B., Yuan, D. J., Luo, D. H., Lu, X. T. & Huang, S. Enrichment of minor alleles of common SNPs and improved risk prediction for Parkinson’s disease. PLoS ONE 10, e0133421 (2015).
    PubMed  PubMed Central  Article  CAS  Google Scholar 

    25.
    Lei, X. Y., Yuan, D. J., Zhu, Z. B. & Huang, S. Collective effects of common SNPs and risk prediction in lung cancer. Heredity 121, 537–547 (2018).
    PubMed  PubMed Central  Article  CAS  Google Scholar 

    26.
    Huang, S. New thoughts on an old riddle: What determines genetic diversity within and between species?. Genomics 108, 3–10 (2016).
    PubMed  Article  CAS  PubMed Central  Google Scholar 

    27.
    Hoffmann, A. A. & Willi, Y. Detecting genetic responses to environmental change. Nat. Rev. Genet. 9, 421–432 (2008).
    PubMed  Article  CAS  PubMed Central  Google Scholar 

    28.
    Sgro, C. M., Lowe, A. J. & Hoffmann, A. A. Building evolutionary resilience for conserving biodiversity under climate change. Evol. Appl. 4, 326–337 (2011).
    PubMed  Article  PubMed Central  Google Scholar 

    29.
    Luikart, G., England, P. R., Tallmon, D., Jordan, S. & Taberlet, P. The power and promise of population genomics: From genotyping to genome typing. Nat. Rev. Genet. 4, 981–994 (2003).
    PubMed  Article  CAS  PubMed Central  Google Scholar 

    30.
    Pfeiffer, V. W. et al. Partitioning genetic and species diversity refines our understanding of species-genetic diversity relationships. Ecol. Evol. 8, 12351–12364 (2018).
    PubMed  PubMed Central  Google Scholar 

    31.
    Forester, B. R., Lasky, J. R., Wagner, H. H. & Urban, D. L. Comparing methods for detecting multilocus adaptation with multivariate genotype-environment associations. Mol. Ecol. 27, 2215–2233 (2018).
    PubMed  Article  CAS  PubMed Central  Google Scholar 

    32.
    Dolédec, S. & Chessel, D. Co-Inertia analysis—An alternative method for studying species environment relationships. Freshw. Biol. 31, 277–294 (1994).
    Article  Google Scholar 

    33.
    Legendre, P. & Legendre, L. Numerical Ecology (Elsevier, Amsterdam, 2012).
    Google Scholar 

    34.
    Mackintosh, A. et al. The determinants of genetic diversity in butterflies. Nat. Commun. 10, 3466 (2019).
    ADS  PubMed  PubMed Central  Article  CAS  Google Scholar 

    35.
    Franks, S. J. & Hoffmann, A. A. Genetics of climate change adaptation. Ann. Rev. Genet. 46, 185–208 (2012).

    36.
    Scheben, A., Yuan, Y. X. & Edwards, D. Advances in genomics for adapting crops to climate change. Curr. Plant Biol. 6, 2–10 (2016).
    Article  Google Scholar 

    37.
    Manel, S. et al. Genomic resources and their influence on the detection of the signal of positive selection in genome scans. Mol. Ecol. 25, 170–184 (2016).
    PubMed  Article  CAS  PubMed Central  Google Scholar 

    38.
    Gomulkiewicz, R. & Houle, D. Demographic and genetic constraints on evolution. Am. Nat. 174, E218–E229 (2009).
    PubMed  Article  Google Scholar 

    39.
    Willi, Y., Van Buskirk, J. & Hoffmann, A. A. Limits to the adaptive potential of small populations. Annu. Rev. Ecol. Evol. S. 37, 433–458 (2006).
    Article  Google Scholar 

    40.
    Lehnert, S. J. et al. Genomic signatures and correlates of widespread population declines in salmon. Nat. Commun. 10, 2996 (2019).
    ADS  PubMed  PubMed Central  Article  CAS  Google Scholar 

    41.
    Vellend, M. et al. Drawing ecological inferences from coincident patterns of population- and community-level biodiversity. Mol. Ecol. 23, 2890–2901 (2014).
    PubMed  Article  PubMed Central  Google Scholar 

    42.
    Bertin, A. et al. Genetic variation of loci potentially under selection confounds species-genetic diversity correlations in a fragmented habitat. Mol. Ecol. 26, 431–443 (2017).
    PubMed  Article  PubMed Central  Google Scholar 

    43.
    Elshire, R. J. et al. A robust, simple genotyping-by-sequencing (GBS) approach for high diversity species. PLoS ONE 6, e19379 (2011).
    ADS  PubMed  PubMed Central  Article  CAS  Google Scholar 

    44.
    Troncoso, A. J., Bertin, A., Osorio, R., Arancio, G. & Gouin, N. Comparative population genetics of two dominant plant species of high Andean wetlands reveals complex evolutionary histories and conservation perspectives in Chile’s Norte Chico. Conserv. Genet. 18, 1047–1060 (2017).
    Article  Google Scholar 

    45.
    Vigneau, E. & Qannari, E. M. Clustering of variables around latent components. Commun. Stat-Simul. C. 32, 1131–1150 (2003).
    MathSciNet  MATH  Article  Google Scholar 

    46.
    Fick, S. E. & Hijmans, R. J. WorldClim 2: New 1-km spatial resolution climate surfaces for global land areas. Int. J. Climatol. 37, 4302–4315 (2017).
    Article  Google Scholar 

    47.
    Vigneau, E., Chen, M. K. & Qannari, E. M. ClustVarLV: An R package for the clustering of variables around latent variables. R J. 7, 134–148 (2015).
    Article  Google Scholar 

    48.
    Oksanen, J. et al.Vegan: Community Ecology Packagehttps://cran.r-project.org/web/packages/vegan/index.html (2018).

    49.
    Dray, S. & Dufour, A. B. The ade4 package: Implementing the duality diagram for ecologists. J. Stat. Softw. 22, 1–20 (2007).
    Article  Google Scholar 

    50.
    Dyer, R. Gstudio: An R Package for the Spatial Analysis of Population Genetic Datahttps://github.com/dyerlab/gstudio/ (2017).

    51.
    Lumley, T. & Miller, A. Leaps: Regression Subset Selectionhttps://cran.r-project.org/web/packages/leaps/index.html (2009).

    52.
    Mazerolle, M. J. AICcmodavg: Model Selection and Multimodel Inference Based on (Q)AIC(c)https://cran.r-project.org/web/packages/AICcmodavg/index.html (2020). More

  • in

    Potential utility of reflectance spectroscopy in understanding the paleoecology and depositional history of different fossils

    1.
    Kortüm, G. Reflectance Spectroscopy: Principles, Methods, Applications (Springer Science & Business Media, New York, 2012).
    Google Scholar 
    2.
    Hunt, G. R., Salisbury, J. W. & Lenhoff, C. J. Visible and near infrared spectra of minerals and rocks. VI. Additional silicates. Modern Geol. 4, 85–106 (1973).
    ADS  CAS  Google Scholar 

    3.
    Johnson, P. E., Smith, M. O., Taylor-George, S. & Adams, J. B. A semiempirical method for analysis of the reflectance spectra of binary mineral mixtures. J. Geophys. Res. Solid Earth 88, 3557–3561 (1983).
    Article  Google Scholar 

    4.
    Clark, R. N. & Lucey, P. G. Spectral properties of ice-particulate mixtures and implications for remote sensing: 1. Intimate mixtures. J. Geophys. Res. Solid Earth 89, 6341–6348 (1984).
    CAS  Article  Google Scholar 

    5.
    Clark, R. N. Spectroscopy of rocks and minerals, and principles of spectroscopy. Manual Remote Sensing 3, 2–2 (1999).
    CAS  Google Scholar 

    6.
    Cloutis, E. A. Review article hyperspectral geological remote sensing: Evaluation of analytical techniques. Int. J. Remote Sensing 17, 2215–2242 (1996).
    ADS  Article  Google Scholar 

    7.
    Clark, R. N., King, T. V., Klejwa, M., Swayze, G. A. & Vergo, N. High spectral resolution reflectance spectroscopy of minerals. J. Geophys. Res. Solid Earth 95, 12653–12680 (1990).
    Article  Google Scholar 

    8.
    Hunt, J. M. & Turner, D. S. Determination of mineral constituents of rocks by infrared spectroscopy. Anal. Chem. 25, 1169–1174 (1953).
    CAS  Article  Google Scholar 

    9.
    Soderblom, L. A. The composition and mineralogy of the martian surface from spectroscopic observations: 0.3 pm to 50 pm. in Mars (eds Kieffer, H. H., Jakosky, B. M., Snyder, C. W. & Matthews, M. S.) 557–593 (Univ. Arizona Press, Tucson, Arizona, 1992).
    Google Scholar 

    10.
    Bell, J. F. III. & Crisp, D. Groundbased imaging spectroscopy of Mars in the near-infrared: Preliminary results. Icarus 104, 2–19 (1993).
    ADS  Article  Google Scholar 

    11.
    Sabins, F. F. Remote sensing for mineral exploration. Ore Geol. Rev. 14, 157–183 (1999).
    Article  Google Scholar 

    12.
    Crosta, A. P., Sabine, C. & Taranik, J. V. Hydrothermal alteration mapping at Bodie, California, using AVIRIS hyperspectral data. Remote Sens. Environ. 65, 309–319 (1998).
    ADS  Article  Google Scholar 

    13.
    Guha, A. et al. Reflectance spectroscopy and ASTER based mapping of rock-phosphate in parts of Paleoproterozoic sequences of Aravalli group of rocks, Rajasthan, India. Ore Geol. Rev. 108, 73–87 (2019).
    Article  Google Scholar 

    14.
    Guha, A., Ghosh, B., Kumar, K. V. & Chaudhury, S. Implementation of reflection spectroscopy based new ASTER indices and principal components to delineate chromitite and associated ultramafic–mafic complex in parts of Dharwar Craton, India. Adv. Space Res. 56, 1453–1468 (2015).
    ADS  CAS  Article  Google Scholar 

    15.
    Guha, A. et al. Analysis of ASTER data for mapping bauxite rich pockets within high altitude lateritic bauxite, Jharkhand, India. Int. J. Appl. Earth Obs. Geoinf. 21, 184–194 (2013).
    ADS  Article  Google Scholar 

    16.
    Green, R. O. et al. The Moon Mineralogy Mapper (M3) imaging spectrometer for lunar science: Instrument description, calibration, on-orbit measurements, science data calibration and on-orbit validation. J. Geophys. Res. Planets 116, E00G19. https://doi.org/10.1029/2011JE003797 (2011).
    Article  Google Scholar 

    17.
    van der Meer, F. D. et al. Multi- and hyperspectral geologic remote sensing: A review. Int. J. Appl. Earth Obs. Geoinf. 14, 112–128 (2012).
    Article  Google Scholar 

    18.
    Cloutis, E. A. et al. Detection and discrimination of sulfate minerals using reflectance spectroscopy. Icarus 184, 121–157 (2006).
    ADS  CAS  Article  Google Scholar 

    19.
    Malakhov, D. V., Dyke, G. J. & King, C. Remote sensing applied to paleontology: Exploration of Upper Cretaceous sediments in Kazakhstan for potential fossil sites. Palaeontol. Electron. 12, 1935–3952 (2009).
    Google Scholar 

    20.
    Wills, S., Choiniere, J. N. & Barrett, P. M. Predictive modelling of fossil-bearing locality distributions in the Elliot Formation (Upper Triassic-Lower Jurassic), South Africa, using a combined multivariate and spatial statistical analyses of present-day environmental data. Palaeogeogr. Palaeoclimatol. Palaeoecol. 489, 186–197 (2018).
    Article  Google Scholar 

    21.
    Clark, R. N. & Roush, T. L. Reflectance spectroscopy: Quantitative analysis techniques for remote sensing applications. J. Geophys. Res. Solid Earth 89, 6329–6340 (1984).
    CAS  Article  Google Scholar 

    22.
    Ramsey, J., Gazis, P., Roush, T., Spirtes, P. & Glymour, C. Automated remote sensing with near infrared reflectance spectra: Carbonate recognition. Data Min. Knowl. Disc. 6, 277–293 (2002).
    MathSciNet  Article  Google Scholar 

    23.
    Mulder, V. L., de Bruin, S., Schaepman, M. E. & Mayr, T. R. The use of remote sensing in soil and terrain mapping—A review. Geoderma 162, 1–19 (2011).
    ADS  CAS  Article  Google Scholar 

    24.
    Anemone, R. L., Conroy, G. C. & Emerson, C. W. GIS and paleoanthropology: Incorporating new approaches from the geospatial sciences in the analysis of primate and human evolution. Am. J. Phys. Anthropol. 146, 19–46 (2011).
    PubMed  Article  Google Scholar 

    25.
    Haley, B. A., Klinkhammer, G. P. & Mix, A. C. Revisiting the rare earth elements in foraminiferal tests. Earth Planet. Sci. Lett. 239, 79–97 (2005).
    ADS  CAS  Article  Google Scholar 

    26.
    Pena, L. D. et al. Characterization of contaminant phases in foraminifera carbonates by electron microprobe mapping. Geochem. Geophys. Geosyst. 9(7), Q07012. https://doi.org/10.1029/2008GC002018 (2008).
    ADS  CAS  Article  Google Scholar 

    27.
    Ries, J. B. Review: Geological and experimental evidence for secular variation in seawater Mg/Ca (calcite-aragonite seas) and its effects on marine biological calcification. Biogeosciences 7, 2795–2849 (2010).
    ADS  CAS  Article  Google Scholar 

    28.
    Todd, R. & Blackmon, P. Calcite and aragonite in foraminifera. J. Paleontol. 30, 217–219 (1956).
    Google Scholar 

    29.
    Falini, G., Albeck, S., Weiner, S. & Addadi, L. Control of aragonite or calcite polymorphism by mollusk shell macromolecules. Science 271, 67–69 (1996).
    ADS  Article  Google Scholar 

    30.
    Armstrong, H. & Brasier, M. Microfossils (Wiley, New York, 2013).
    Google Scholar 

    31.
    Gaffey, S. J. Spectral reflectance of carbonate minerals in the visible and near infrared (0.35–2.55 μm): Anhydrous carbonate minerals. J. Geophys. Res. Solid Earth 92, 1429–1440 (1987).
    CAS  Article  Google Scholar 

    32.
    Gaffey, S. J. Reflectance spectroscopy in the visible and near-infrared (0.35–2.55 μm): Applications in carbonate petrology. Geology 13, 270–273 (1985).
    ADS  CAS  Article  Google Scholar 

    33.
    Rossel, R. A. V., Bui, E. N., De Caritat, P. & McKenzie, N. J. Mapping iron oxides and the color of Australian soil using visible–near-infrared reflectance spectra. J. Geophys. Res. 115, F04031. https://doi.org/10.1029/2009JF001645 (2010).
    ADS  CAS  Article  Google Scholar 

    34.
    Longhi, I., Sgavetti, M., Chiari, R. & Mazzoli, C. Spectral analysis and classification of metamorphic rocks from laboratory reflectance spectra in the 0.4–2.5 μm interval: A tool for hyperspectral data interpretation. Int. J. Remote Sensing 22, 3763–3782 (2001).
    ADS  Article  Google Scholar 

    35.
    Boudaugher-Fadel, M. K. Evolution and Geological Significance of Larger Benthic Foraminifera (UCL Press, London, 2018).
    Google Scholar 

    36.
    Clarkson, E. N. K. Invertebrate Palaeontology and Evolution (Wiley, New York, 2009).
    Google Scholar 

    37.
    Prazeres, M., Roberts, T. E. & Pandolfi, J. M. Variation in sensitivity of large benthic Foraminifera to the combined effects of ocean warming and local impacts. Sci. Rep. 7, 45227 (2017).
    ADS  CAS  PubMed  PubMed Central  Article  Google Scholar 

    38.
    Hohenegger, J., Kinoshita, S., Briguglio, A., Eder, W. & Wöger, J. Lunar cycles and rainy seasons drive growth and reproduction in nummulitid foraminifera, important producers of carbonate buildups. Sci. Rep. 9, 8286 (2019).
    ADS  PubMed  PubMed Central  Article  CAS  Google Scholar 

    39.
    Zachos, J. Trends, rhythms, and aberrations in global climate 65 Ma to present. Science 292, 686–693 (2001).
    ADS  CAS  PubMed  Article  Google Scholar 

    40.
    BouDagher-Fadel, M. K. Biostratigraphic and Geological Significance of Planktonic Foraminifera (UCL Press, London, 2015).
    Google Scholar 

    41.
    Brett, C. E. & Baird, G. C. Comparative taphonomy: A key to paleoenvironmental interpretation based on fossil preservation. Palaios 1, 207–227 (1986).
    ADS  Article  Google Scholar 

    42.
    Biswas, S. K. Tertiary stratigraphy of Kutch. J. Palaeontol. Society India 37, 1–29 (1992).
    Google Scholar 

    43.
    Banerjee, S., Khanolkar, S. & Saraswati, P. K. Facies and depositional settings of the Middle Eocene-Oligocene carbonates in Kutch. Geodin. Acta 30, 119–136 (2018).
    Article  Google Scholar 

    44.
    Srivastava, V. K. & Singh, B. P. Depositional environments and sources for the middle Eocene Fulra Limestone Formation, Kachchh Basin, western India: Evidences from facies analysis, mineralogy, and geochemistry. Geol. J. 54, 62–82 (2019).
    CAS  Article  Google Scholar 

    45.
    Chaudhuri, A., Banerjee, S. & Le Pera, E. Petrography of Middle Jurassic to Early Cretaceous sandstones in the Kutch Basin, western India: Implications on provenance and basin evolution. J. Palaeogeogr. 7, 2 (2018).
    Article  Google Scholar 

    46.
    Biswas, S. K. Mesozoic and tertiary stratigraphy of Kutch*(Kachchh)—A review. in Conference GSI 1–24 (2016).

    47.
    Srivastava, H., Bhaumik, A. K., Tiwari, D., Mohanty, S. P. & Patil, D. J. Characterization of organic carbon in black shales of the Kachchh basin, Gujarat, India. J. Earth Syst. Sci. 127, 93 (2018).
    ADS  Article  CAS  Google Scholar 

    48.
    Rao, G. N. Sedimentation, stratigraphy, and petroleum potential of Krishna–Godavari basin, east coast of India. AAPG Bull. 85, 1623–1643 (2001).
    CAS  Google Scholar 

    49.
    Mazumdar, A. et al. Geochemical characterization of the Krishna–Godavari and Mahanadi offshore basin (Bay of Bengal) sediments: A comparative study of provenance. Mar. Pet. Geol. 60, 18–33 (2015).
    CAS  Article  Google Scholar 

    50.
    Torrent, J. & Barrón, V. Diffuse reflectance spectroscopy of iron oxides. Encyclopedia Surface Colloid Sci. 1, 1438–1446 (2002).
    Google Scholar 

    51.
    Clark, R. N. et al.USGS digital spectral library splib06a. Data Series 231. (US Geological Survey, 2007). https://doi.org/10.3133/ds231.

    52.
    Small, C. et al. Spectroscopy of sediments in the Ganges–Brahmaputra delta: Spectral effects of moisture, grain size and lithology. Remote Sens. Environ. 113, 342–361 (2009).
    ADS  Article  Google Scholar 

    53.
    Edgar, K. M., Pälike, H. & Wilson, P. A. Testing the impact of diagenesis on the δ18O and δ13C of benthic foraminiferal calcite from a sediment burial depth transect in the equatorial Pacific. Paleoceanography 28, 468–480 (2013).
    ADS  Article  Google Scholar 

    54.
    Bao, H., Koch, P. L. & Hepple, R. P. Hematite and calcite coatings on fossil vertebrates. J. Sediment. Res. 68, 727–738 (1998).
    ADS  Article  Google Scholar 

    55.
    Borrelli, C., Panieri, G., Dahl, T. M. & Neufeld, K. Novel biomineralization strategy in calcareous foraminifera. Sci. Rep. 8, 10201 (2018).
    ADS  CAS  PubMed  PubMed Central  Article  Google Scholar 

    56.
    Welton, J. E. SEM Petrology Atlas (American Association of Petroleum Geologists, Tulsa, 1984). https://doi.org/10.1306/Mth4442.
    Google Scholar 

    57.
    Boyle, E. A., Berry, J. N., Erez, J. & Tishler, C. Sulfur in foraminifera shells, a new paleoceanographic proxy for carbonate ion in seawater. in AGU Fall Meeting Abstracts (2002).

    58.
    de Nooijer, L. J. et al. Copper incorporation in foraminiferal calcite: Results from culturing experiments. Biogeosci. Discuss. 4, 961–991 (2007).
    ADS  Article  Google Scholar 

    59.
    Geider, R. J. & La Roche, J. The role of iron in phytoplankton photosynthesis, and the potential for iron-limitation of primary productivity in the sea. Photosynth. Res. 39, 275–301 (1994).
    CAS  PubMed  Article  Google Scholar 

    60.
    van Hulten, M. M. P. et al. Aluminium in an ocean general circulation model compared with the West Atlantic Geotraces cruises. J. Mar. Syst. 126, 3–23 (2013).
    Article  Google Scholar 

    61.
    Boström, K., Kraemer, T. & Gartner, S. Provenance and accumulation rates of opaline silica, Al, Ti, Fe, Mn, Cu, Ni and Co in Pacific pelagic sediments. Chem. Geol. 11, 123–148 (1973).
    ADS  Article  Google Scholar 

    62.
    Immenhauser, A., Schoene, B. R., Hoffmann, R. & Niedermayr, A. Mollusc and brachiopod skeletal hard parts: intricate archives of their marine environment. Sedimentology 63, 1–59 (2016).
    CAS  Article  Google Scholar 

    63.
    Frankel, R. B. Iron biominerals: an overview. in Iron Biominerals (Frankel, R. B., Blakemore, R. P.) 1–6 (Springer, Boston, MA, 1991).
    Google Scholar 

    64.
    Jenkins, S. R. et al. Regional scale differences in the determinism of grazing effects in the rocky intertidal. Mar. Ecol. Prog. Ser. 287, 77–86 (2005).
    ADS  Article  Google Scholar 

    65.
    Smoothey, A. F. Habitat-associations of turban snails on intertidal and subtidal rocky reefs. PLoS ONE 8, e61257 (2013).
    ADS  CAS  PubMed  PubMed Central  Article  Google Scholar 

    66.
    Van der Meer, F. D. & De Jong, S. M. Imaging Spectrometry: Basic Principles and Prospective Applications Vol. 4 (Springer Science & Business Media, New York, 2011).
    Google Scholar 

    67.
    Khanolkar, S., Saraswati, P. K. & Rogers, K. Ecology of foraminifera during the middle Eocene climatic optimum in Kutch, India. Geodin. Acta 29, 181–193 (2017).
    Article  Google Scholar 

    68.
    Sen, G. et al. Deccan plume, lithosphere rifting, and volcanism in Kutch, India. Earth Planetary Sci. Lett. 277, 101–111 (2009).
    ADS  CAS  Article  Google Scholar 

    69.
    Gilbert, P. U. P. A. et al. Biomineralization by particle attachment in early animals. PNAS 116, 17659–17665 (2019).
    ADS  CAS  PubMed  Article  Google Scholar 

    70.
    Gaffey, S. J. Spectral reflectance of carbonate minerals in the visible and near infrared (0.35–2.55 microns); calcite, aragonite, and dolomite. Am. Mineral. 71, 151–162 (1986).
    CAS  Google Scholar 

    71.
    Carli, C. & Sgavetti, M. Spectral characteristics of rocks: Effects of composition and texture and implications for the interpretation of planet surface compositions. Icarus 211, 1034–1048 (2011).
    ADS  CAS  Article  Google Scholar 

    72.
    Guha, A. et al. Spectroscopic study of rocks of Hutti-Maski schist belt, Karnataka. J. Geol. Soc. India 79, 335–344 (2012).
    CAS  Article  Google Scholar 

    73.
    Milton, E. J., Schaepman, M. E., Anderson, K., Kneubühler, M. & Fox, N. Progress in field spectroscopy. Remote Sens. Environ. 113, S92–S109 (2009).
    ADS  Article  Google Scholar 

    74.
    Bish, D. L. & Post, J. E. Modern Powder Diffraction Vol. 20 (Walter de Gruyter GmbH & Co KG, Washington, 2018).
    Google Scholar 

    75.
    Al-Jaroudi, S. S., Ul-Hamid, A., Mohammed, A.-R.I. & Saner, S. Use of X-ray powder diffraction for quantitative analysis of carbonate rock reservoir samples. Powder Technol. 175, 115–121 (2007).
    CAS  Article  Google Scholar 

    76.
    Pownceby, M. I., MacRae, C. M. & Wilson, N. C. Mineral characterisation by EPMA mapping. Miner. Eng. 20, 444–451 (2007).
    CAS  Article  Google Scholar 

    77.
    Reed, S. J. B. Electron Microprobe Analysis and Scanning Electron Microscopy in Geology (Cambridge University Press, Cambridge, 2005).
    Google Scholar  More

  • in

    Sedimentary DNA tracks decadal-centennial changes in fish abundance

    DNA concentration in core sediments
    qPCR analyses for each core (Experiment (a) in Fig. 1) showed that the mean DNA copies for anchovy ranged from 233 ± 215 copies g−1 dry sediment (hereafter, copies g−1, mean ± 1 SD) to 3075 ± 781 copies g−1 (Supplementary Table 1). For all data, anchovy had 1067 ± 968 mean DNA copies g−1. Anchovy DNA copies were detected in all samples, except three (13 cm depth in BG17-1, 41 and 45 cm in BMC18-6). For most samples, DNA was detected in more than three out of four replicates. In each core, the mean DNA copies for sardines ranged from 1.7 ± 4.0 to 12.0 ± 29.2 copies g−1. For all data, sardine had 5.1 ± 3.8 mean DNA copies g−1. This concentration was 0.5% that of anchovy. Sardine DNA copies were not detected in many of the samples from each core. One or two replicates were detected in most samples, while a few samples had more than three out of four replicates for sardines. Jack mackerel DNA copies were also not detected for many samples from each core. Mean DNA copies for each core ranged from 0 to 55.9 ± 127.2 copies g−1. For all data, Jack mackerel had 14.8 ± 19.1 mean DNA copies g−1. This concentration was 1.4% that of anchovy. Few samples had more than three out of four replicates, with most samples having one or two replicates.
    For the negative control for sectioning, subsampling, DNA extraction, and PCR processes (Experiment (a) in Fig. 1), DNA of the three marine species was not detected in any of the core sediment samples from Lake Biwa (LBHR18-1) (Supplementary Table 1). DNA was not detected on the PCR blanks either (Supplementary Table 1). Thus, contamination was not an issue during sampling, extraction, purification, and the PCR processes. Also, through the direct sequencing of PCR amplicons by the qPCR assay of Japanese sardine, we confirmed that only the DNA was amplified.
    We performed spike test to evaluate the effect of PCR inhibition (Experiment (a) in Fig. 1). All ΔCt values were less than three (ΔCt: −2.4–2.9) (Supplementary Table 2), providing no evidence of inhibition31.
    Down-core changes in DNA concentration
    Down-core changes in DNA concentration for anchovy showed different patterns between each core (Supplementary Figs. 2 and 3) (Experiment (a) in Supplementary Fig. 1). For 50-cm-long core samples, peaks in DNA occurred at around 5 and 20 cm in BMC18-6, but occurred at around 0 cm in BMC17 S1-7 (Supplementary Fig. 2). There was no noticeable peak in BMC17 S1-10 (Supplementary Fig. 2). For 110-cm-long core samples, there was no consistent vertical pattern, except for the uppermost layers, with the highest values being detected for BG18-6W and BG18-8A (Supplementary Fig. 3). For sardine and jack mackerel, there were no consistent vertical patterns in DNA for the short cores. In contrast, the 1.1-m-long cores had peaks centered at around 16 and 57 cm deep for sardine and at around 20 cm for jack mackerel. Comparison between short and long cores (Supplementary Figs. 2 and 3) showed that anchovy and jack mackerel had the highest DNA concentrations in the uppermost layers in the short cores. The long cores did not show a similar trend, due to loss of surface layers (approximately 20 cm) during core collection. In contrast, the highest DNA concentrations for sardine occurred at 57–58 cm depth in the long cores, not in the uppermost layers of the short cores.
    There was no clear evidence that DNA concentrations were higher in core samples that were instantly frozen after core collection (core BMC17 S1-7) compared to samples that were frozen 6 days or 4 weeks after core collection (Supplementary Figs. 2 and 3) (Experiment (a) in Fig. 1). Thus, chilled storage for 4 weeks only caused minor degradation of DNA in core samples.
    Temporal changes in DNA concentration
    General additive models (GAMs) showed that the decadal–centennial dynamics of the inter-core, seven-year averaged, and sedDNA concentrations for the last 300 years significantly varied non-linearly (Japanese anchovy, s = 7.22, P = 2.96 × 10−7; Japanese sardine, s = 12.61, P = 1.10 × 10−4; jack mackerel, s = 8.831, P = 2.84 × 10−9, Fig. 2). DNA concentrations for Japanese anchovy were high after 2010 CE (BMC18-6, BMC17 S1-7, and BMC17 S1-10) (Fig. 3). While there was no consistent pattern in the time series of the cores before 2000 CE, one or two of the time series showed high values around the 1960s CE and the 2000 CE. These periods with high values showed large scatters between the cores, indicating spatial heterogeneity in DNA deposition.
    Fig. 2: The results of general additive models (GAM) from inter-core, seven-year averaged sedDNA concentrations.

    a Engraulis japonicus (Japanese anchovy); b Sardinops melanostictus (Japanese sardine); c Tranchurus japonicus (jack mackerel). Blue line denotes a regression line of GAM with the 95% confidence interval (gray zone).

    Full size image

    Fig. 3: Temporal changes in mean DNA concentrations for all cores.

    a Japanese anchovy; b Japanese sardine; c jack mackerel. Error bar of each data point denotes 1 SD (n = 4 or 8). The horizontal bar in panel b represents historical good (solid) and poor (gray) catch periods (open: no data). Translucent colored plots denote each data point in qPCR replicates.

    Full size image

    These high values were not obtained in the anchovy fish scale concentrations during the same periods17 (Supplementary Fig. 4). There was also no significant relationship in the Type II regression model for the inter-core, seven-year averaged, concentrations in DNA (Fig. 4a) with those of fish scale concentrations (Fig. 4c) for the 300 years (R2 = 0.0157, P = 0.429, n = 42, Fig. 5a, also see Supplementary Fig. 5 for the log-transformed model). In contrast, the two DNA peaks in the 1960s and 2000 were temporally consistent with those of the catch record in Japan (Statistics of Agriculture, Forestry and Fisheries, with the landing data being updated from previous studies32,33) (Fig. 4b). This result was supported by a Type II regression model, with a significant correlation existing between inter-core, seven-year averaged concentrations in DNA (Fig. 4a) and seven-year averaged catches in Japan (Fig. 4b) (R2 = 0.255, P = 0.0459, n = 16, Fig. 6a, also see Supplementary Fig. 6a for the log-transformed model). Anchovy sedDNA and landings in Japan before 1990 showed a positive-phase relationship with the Bungo Channel (Supplementary Fig. 7, see Supplementary Fig. 1a for the location), but a negative-phase relationship with the central Seto Inland Sea (Supplementary Fig. 7c). A decadal peak around 2000, as shown by the sedDNA and landings in Japan, was not obtained in the landings from the Bungo Channel and Beppu Bay, Iyo-nada, and Suo-nada (Supplementary Fig 7a, b, see Supplementary Fig. 1 for the locations and see Supplementary Discussion for the reasons). An abnormally high value in 2014–2017 was not found in the landing records (Supplementary Fig 7a, b). This inconsistency indicates the presence of enriched DNA in the surface layer that is susceptible to rapid decomposition due to early diagenesis in a few years.
    Fig. 4: Comparison between temporal changes in sedDNA concentrations, landings, and fish scales.

    a, d, and g: inter-core, seven-year averaged concentrations of DNA for anchovy (left), sardine (middle), and jack mackerel (right). b, e, and h: total landings in Japan. c and f: fish scales. Of note, the landings of Caranginae (jack mackerel plus amberstripe scad, Decapterus muroadsi) consist mostly of those of jack mackerel. Error bar of each data point denotes 1 SD. Translucent colored plots denote annual data points for each core.

    Full size image

    Fig. 5: Relationships between sedDNA and fish scale concentrations.

    a: Japanese anchovy; and b: Japanese sardine. Inter-core, 7-year average data were used for the models. Red line denotes a regression line of Gaussian Type II regression model with the 95% confidence interval (gray zone).

    Full size image

    Fig. 6: Relationships between sedDNA concentrations and the total landings in Japan.

    a: Japanese anchovy; b: Japanese sardine; c: jack mackerel; and d: Caranginae (jack mackerel and amberstripe scad). Inter-core, 7-year average data for eDNA and 7-year average data for landing were used in the models. Red line denotes a regression line of Gaussian Type II regression model with the 95% confidence interval (gray zone).

    Full size image

    DNA concentrations for Japanese sardine showed large scatters of contemporary values between the cores (Fig. 3b), indicating spatial heterogeneity in DNA depositions. The concentrations were high ( >20 copies g−1) during the 1840s to 1850s and 1970s to 1980s for the time series of the three cores, and were low (0.25 g−1) recorded for sardine fish scale concentrations (Supplementary Fig. 4b, f); however, there was no noticeable peak during the 1920s and 1930s (Figs. 3b and 4b), despite a peak occurring in the fish scale record (Fig. 4f and Supplementary Fig. 4b). The peak in DNA during the1970s to 1980s corresponded to a distinct peak in sardine catches during the twentieth century (Fig. 4e). The DNA peak in the 1840s to 1850s was consistent with good catch periods recorded by historical documents in and around the Bungo Channel34,35 (Fig. 3b). Sardine DNA was detected during ~1700 CE (14 copies g−1), which was consistent with a good catch period recorded in the historical documents (Fig. 3b), and a minor peak in the fish scale record (Fig. 4f and Supplementary Fig. 4b). Type II regression for sardine showed a significant correlation between inter-core, seven-year averaged concentrations of DNA (Fig. 4d) and fish scale concentrations (Fig. 4f) for the last 300 years (R2 = 0.436, P = 1.93 × 10−6, n = 42, Fig. 5b, also see Supplementary Fig. 5b for the log-transformed model). It also showed a significant correlation between inter-core, seven-year averaged concentrations of DNA and seven-year averaged catches in Japan (R2 = 0.269, P = 0.0395, n = 16, Fig. 6b, also see Supplementary Fig. 6b for the log-transformed model). sedDNA and landings in Japan showed a positive-phase relationship with Bungo Channel. However, a clear relationship was not detected with the landings in Beppu Bay, Iyo-nada, and Suo-nada or the central Seto Inland Sea (Supplementary Fig. 8, see Supplementary Discussion for the reasons). There was a negative phase relationship between sardine and anchovy in sedDNA after the 1950 CE (Fig. 2a, b).
    Jack mackerel DNA concentrations for each core (Fig. 3c) were high, exceeding 50 copies g−1 around 1970 and 1990 for the two core time series, and exceeding 100 copies g−1 after 2005, with low values ( 0.05, Supplementary Table 3). In the lower massive layers (Supplementary Figs. 11 and 12), anchovy DNA showed a significant positive correlation with TOC (r = −0.50, P = 0.029) and biogenic opal (r = 0.47, P = 0.044), and a negative correlation with C/N (r = −0.48, P = 0.040). It showed no correlation with Ti and sedimentation rate.
    Source materials of DNA in marine sediments
    The DNA in the pore water of each sample (Experiment (c) in Fig. 1) was not detected by qPCR assays for any of the species (Table 1). In contrast, anchovy DNA was detected in the residual bulk sediments of all samples (range: 367–6423 copies g−1, mean: 2704 ± 2233 copies g−1), while sardine DNA was detected in two samples (range: 12.4–283.5 copies g−1, mean: 51.6 ± 113 copies g−1) and jack mackerel was detected in one sample (84.6 copies g−1) (Table 1, Experiment (c) in Fig. 1). DNA was only detected in the fish scales of anchovy (1.5 ± 4.2 copies scale−1) (Table 2) (Experiment (b) in Fig. 1). DNA from bones was not detected in any of the species (Table 2) (Experiment (b) in Fig. 1). DNA was detected in the 63–180 μm size fractions of one sample for sardine (0.9 ± 1.7 copies g−1 dry sediment before sieved) and jack mackerel (0.3 ± 0.6 copies g−1 dry sediment before sieved), but was not detected for anchovy (Table 2). DNA was not detected in the 180–500 μm fractions for any of the species (Table 2).
    Table 1 DNA copies for each species for pore water and pore water-free sediment samples.
    Full size table

    Table 2 DNA copies for each species for fish scales, bones, and fine (63–180 μm) and coarse (180–500 μm) particle size fraction of sediment samples.
    Full size table More

  • in

    Evidence for signatures of ancient microbial life in paleosols

    1.
    Kehl, M. Quaternary Loesses, Loess-Like Sediments, Soils and Climate Change in Iran (Gebrüder Borntraeger Verlagsbuchhandlung, 2010).
    2.
    Kehl, M., Sarvati, R., Ahmadi, H., Frechen, M. & Skowronek, A. Loess paleosol-sequences along a climatic gradient in Northern Iran. Eiszeitalt. Ggw. 55, 149–173 (2005).
    Google Scholar 

    3.
    Bradley, R. S. Paleoclimatology: Reconstructing Climates of the Quaternary Vol. 68 (Academic Press, Cambridge, 1999).
    Google Scholar 

    4.
    Vlaminck, S. et al. Late Pleistocene dust dynamics and pedogenesis in Southern Eurasia—Detailed insights from the loess profile Toshan (NE Iran). Quat. Sci. Rev. 180, 75–95 (2018).
    ADS  Article  Google Scholar 

    5.
    Schulz, S. et al. The role of microorganisms at different stages of ecosystem development for soil formation. Biogeosciences 10, 3983–3996 (2013).
    ADS  Article  Google Scholar 

    6.
    Tscherko, D., Rustemeier, J., Richter, A., Wanek, W. & Kandeler, E. Functional diversity of the soil microflora in primary succession across two glacier forelands in the Central Alps. Eur. J. Soil Sci. 54, 685–696 (2003).
    Article  Google Scholar 

    7.
    Nemergut, D. R. et al. Microbial community succession in an unvegetated, recently deglaciated soil. Microb. Ecol. 53, 110–122 (2007).
    PubMed  Article  Google Scholar 

    8.
    Turner, S. et al. Microbial community dynamics in soil depth profiles over 120,000 years of ecosystem development. Front. Biol. 8, 1–17 (2017).
    Google Scholar 

    9.
    Chaopricha, N. T. & Marín-Spiotta, E. Soil burial contributes to deep soil organic carbon storage. Soil Biol. Biochem. 69, 251–264 (2014).
    CAS  Article  Google Scholar 

    10.
    Shahriari, A. et al. Biomarkers in modern and buried soils of semi-desert and forest ecosystems of northern Iran. Quat. Int. 429, 62–73 (2017).
    Article  Google Scholar 

    11.
    Svirčev, Z. et al. Importance of biological loess crusts for loess formation in semi-arid environments. Quat. Int. 296, 206–215 (2013).
    Article  Google Scholar 

    12.
    Dulić, T. et al. Cyanobacterial diversity and toxicity of biocrusts from the Caspian Lowland loess deposits, North Iran. Quat. Int. 429, 74–85 (2017).
    Article  Google Scholar 

    13.
    Demkina, T. S., Khomutova, T. E., Kashirskaya, N. N., Stretovich, I. V. & Demkin, V. A. Characteristics of microbial communities in steppe paleosols buried under kurgans of the Sarmatian time (I-IV centuries AD). Eurasian Soil Sci. 42, 778–787 (2009).
    ADS  Article  Google Scholar 

    14.
    Khomutova, T. E. et al. An assessment of changes in properties of steppe kurgan paleosoils in relation to prevailing climates over recent millennia. Quat. Res. 67, 328–336 (2007).
    Article  Google Scholar 

    15.
    Thomsen, P. F. & Willerslev, E. Environmental DNA: an emerging tool in conservation for monitoring past and present biodiversity. Biol. Conserv. 183, 4–18 (2015).
    Article  Google Scholar 

    16.
    Pedersen, M. W. et al. Ancient and modern environmental DNA. Philos. Trans. R. Soc. B 370, 20130383 (2015).
    Article  CAS  Google Scholar 

    17.
    Bálint, M. et al. Environmental DNA time series in ecology. Trends Ecol. Evol. 33, 945–957 (2018).
    PubMed  Article  PubMed Central  Google Scholar 

    18.
    Coolen, M. J. L. et al. Combined DNA and lipid analyses of sediments reveal changes in Holocene haptophyte and diatom populations in an Antarctic lake. Earth Planet. Sci. Lett. 223, 225–239 (2004).
    ADS  CAS  Article  Google Scholar 

    19.
    Monchamp, M.-E. et al. Homogenization of lake cyanobacterial communities over a century of climate change and eutrophication. Nat. Ecol. Evol. 2, 317–324 (2018).
    PubMed  Article  PubMed Central  Google Scholar 

    20.
    Belle, S. et al. Temporal changes in the contribution of methane-oxidizing bacteria to the biomass of chironomid larvae determined using stable carbon isotopes and ancient DNA. J. Paleolimnol. 52, 215–228 (2014).
    ADS  Article  Google Scholar 

    21.
    Bellemain, E. et al. Fungal palaeodiversity revealed using high-throughput metabarcoding of ancient DNA from arctic permafrost. Environ. Microbiol. 15, 1176–1189 (2013).
    CAS  PubMed  Article  PubMed Central  Google Scholar 

    22.
    Zhang, D. C., Brouchkov, A., Griva, G., Schinner, F. & Margesin, R. Isolation and characterization of bacteria from ancient Siberian permafrost sediment. Biology (Basel) 2, 85–106 (2013).
    Google Scholar 

    23.
    Gilichinsky, D. et al. Bacteria in permafrost. In Psychrophiles: From Biodiversity to Biotechnology (eds Margesin, R. et al.) 83–102 (Springer, Berlin, 2008). https://doi.org/10.1007/978-3-540-74335-4_6
    Google Scholar 

    24.
    Willerslev, E. et al. Long-term persistence of bacterial DNA. Curr. Biol. 14, 13–14 (2004).
    Article  CAS  Google Scholar 

    25.
    Vlaminck, S. et al. Loess-soil sequence at Toshan (Northern Iran): insights into late Pleistocene climate change. Quat. Int. 399, 122–135 (2016).
    Article  Google Scholar 

    26.
    Lauer, T. et al. Luminescence-chronology of the loess palaeosol sequence Toshan, Northern Iran: a highly resolved climate archive for the last glacial-interglacial cycle. Quat. Int. 429, 3–12 (2017).
    Article  Google Scholar 

    27.
    Khormali, F. & Kehl, M. Micromorphology and development of loess-derived surface and buried soils along a precipitation gradient in Northern Iran. Quat. Int. 234, 109–123 (2011).
    Article  Google Scholar 

    28.
    Khormali, F., Ghergherechi, S., Kehl, M. & Ayoubi, S. Soil formation in loess-derived soils along a subhumid to humid climate gradient, Northeastern Iran. Geoderma 179–180, 113–122 (2012).
    ADS  Article  CAS  Google Scholar 

    29.
    Fierer, N., Schimel, J. P. & Holden, P. A. Variations in microbial community composition through two soil depth profiles. Soil Biol. Biochem. 35, 167–176 (2003).
    CAS  Article  Google Scholar 

    30.
    Eilers, K. G., Debenport, S., Anderson, S. & Fierer, N. Digging deeper to find unique microbial communities: the strong effect of depth on the structure of bacterial and archaeal communities in soil. Soil Biol. Biochem. 50, 58–65 (2012).
    CAS  Article  Google Scholar 

    31.
    Helgason, B. L., Konschuh, H. J., Bedard-Haughn, A. & VandenBygaart, A. J. Microbial distribution in an eroded landscape: Buried A horizons support abundant and unique communities. Agric. Ecosyst. Environ. 196, 94–102 (2014).
    Article  Google Scholar 

    32.
    Liu, G. et al. Vertical changes in bacterial community composition down to a depth of 20 m on the degraded Loess Plateau in China. Land Degrad. Dev. 31, 1300–1313.
    Article  Google Scholar 

    33.
    Lauer, T. et al. The Agh Band loess-palaeosol sequence—A terrestrial archive for climatic shifts during the last and penultimate glacial–interglacial cycles in a semiarid region in northern Iran. Quat. Int. 439, 13–30 (2017).
    Article  Google Scholar 

    34.
    Mitzscherling, J. et al. Microbial community composition and abundance after millennia of submarine permafrost warming. Biogeosci. Discuss. 16, 3941–3958 (2019).
    ADS  CAS  Article  Google Scholar 

    35.
    Vuillemin, A., Ariztegui, D., Leavitt, P. R. & Bunting, L. Recording of climate and diagenesis through sedimentary DNA and fossil pigments at Laguna Potrok Aike, Argentina. Biogeosciences 13, 2475–2492 (2016).
    ADS  CAS  Article  Google Scholar 

    36.
    Ciobanu, M.-C. et al. Sedimentological imprint on subseafloor microbial communities in Western Mediterranean Sea Quaternary sediments. Biogeosciences 9, 3491–3512 (2012).
    ADS  CAS  Article  Google Scholar 

    37.
    Carini, P. et al. Relic DNA is abundant in soil and obscures estimates of soil microbial diversity. Nat. Microbiol. 2, 16242 (2016).
    PubMed  Article  CAS  Google Scholar 

    38.
    Drancourt, M. & Raoult, D. Paleomicrobiology: Current issues and perspectives. Nat. Rev. Microbiol. 3, 23–35 (2005).
    CAS  PubMed  Article  Google Scholar 

    39.
    Stevenson, A. et al. Multiplication of microbes below 0.690 water activity: implications for terrestrial and extraterrestrial life. Environ. Microbiol. 17, 257–277 (2015).
    PubMed  Article  Google Scholar 

    40.
    Schimel, J. P. Life in dry soils: Effects of drought on soil microbial ommunities and processes. Annu. Rev. Ecol. Evol. Syst. 49, 409–432 (2018).
    Article  Google Scholar 

    41.
    Lebre, P. H., De Maayer, P. & Cowan, D. A. Xerotolerant bacteria: Surviving through a dry spell. Nat. Rev. Microbiol. 15, 285–296 (2017).
    CAS  PubMed  Article  PubMed Central  Google Scholar 

    42.
    Joergensen, R. G. & Wichern, F. Alive and kicking: Why dormant soil microorganisms matter. Soil Biol. Biochem. 116, 419–430 (2018).
    CAS  Article  Google Scholar 

    43.
    Aslam, S. N. et al. Soil compartment is a major determinant of the impact of simulated rainfall on desert microbiota. Environ. Microbiol. 18, 5048–5062 (2016).
    CAS  PubMed  Article  Google Scholar 

    44.
    Armstrong, A. et al. Temporal dynamics of hot desert microbial communities reveal structural and functional responses to water input. Sci. Rep. 6, 1–8 (2016).
    Article  CAS  Google Scholar 

    45.
    Knief, C. et al. Tracing elevational changes in microbial life and organic carbon sources in soils of the Atacama Desert. Glob. Planet. Change 184, 103078 (2020).
    Article  Google Scholar 

    46.
    Kielak, A. M., Barreto, C. C., Kowalchuk, G. A., van Veen, J. A. & Kuramae, E. E. The ecology of Acidobacteria: moving beyond genes and genomes. Front. Microbiol. 7, 1–16 (2016).
    Google Scholar 

    47.
    Chernov, T. I. et al. Comparative analysis of the structure of buried and surface soils by analysis of microbial DNA. Microbiology 87, 833–841 (2018).
    CAS  Article  Google Scholar 

    48.
    Knief, C., Ramette, A., Frances, L., Alonso-Blanco, C. & Vorholt, J. A. Site and plant species are important determinants of the Methylobacterium community composition in the plant phyllosphere. ISME J. 4, 719–728 (2010).
    CAS  PubMed  Article  Google Scholar 

    49.
    Fierer, N., Colman, B. P., Schimel, J. P. & Jackson, R. B. Predicting the temperature dependence of microbial respiration in soil: A continental-scale analysis. Glob. Biogeochem. Cycles 20, GB3026 (2006).
    ADS  Article  CAS  Google Scholar 

    50.
    Baldani, J. I. et al. The family Oxalobacteraceae. In The Prokaryotes: Alphaproteobacteria and Betaproteobacteria (eds Rosenberg, E. et al.) 919–974 (Springer, Berlin, 2014).
    Google Scholar 

    51.
    Li, J. et al. Phytomonospora endophytica gen. nov., sp. nov., isolated from the roots of Artemisia annua L. Int. J. Syst. Evol. Microbiol. 61, 2967–2973 (2011).
    CAS  PubMed  Article  PubMed Central  Google Scholar 

    52.
    Eyice, Ö et al. SIP metagenomics identifies uncultivated Methylophilaceae as dimethylsulphide degrading bacteria in soil and lake sediment. ISME J. 9, 2336–2348 (2015).
    CAS  PubMed  PubMed Central  Article  Google Scholar 

    53.
    Liu, D., Yang, Y., An, S., Wang, H. & Wang, Y. The biogeographical distribution of soil bacterial communities in the Loess Plateau as revealed by high-throughput sequencing. Front. Microbiol. 9, 2456 (2018).
    PubMed  PubMed Central  Article  Google Scholar 

    54.
    Trujillo, M. E. et al. Pseudonocardia nigra sp. nov., isolated from Atacama desert rock. Int. J. Syst. Evol. Microbiol. 67, 2980–2985 (2017).
    CAS  PubMed  Article  PubMed Central  Google Scholar 

    55.
    Mohammadipanah, F. & Wink, J. Actinobacteria from arid and desert habitats: diversity and biological activity. Front. Microbiol. 6, 1541 (2016).
    PubMed  PubMed Central  Article  Google Scholar 

    56.
    Goodfellow, M., Nouioui, I., Sanderson, R., Xie, F. & Bull, A. T. Rare taxa and dark microbial matter: novel bioactive actinobacteria abound in Atacama Desert soils. Antonie van Leeuwenhoek 111, 1315–1332 (2018).
    CAS  PubMed  Article  PubMed Central  Google Scholar 

    57.
    Bull, A. T. et al. High altitude, hyper-arid soils of the Central-Andes harbor mega-diverse communities of actinobacteria. Extremophiles 22, 47–57 (2018).
    PubMed  Article  PubMed Central  Google Scholar 

    58.
    Polymenakou, P. N., Mandalakis, M., Stephanou, E. G. & Tselepides, A. Particle size distribution of airborne microorganisms and pathogens during an intense African dust event in the eastern Mediterranean. Environ. Health Perspect. 116, 292–296 (2008).
    PubMed  Article  PubMed Central  Google Scholar 

    59.
    Wang, X. et al. Grain-size distribution of Pleistocene loess deposits in northern Iran and its palaeoclimatic implications. Quat. Int. 429, 41–51 (2017).
    Article  Google Scholar 

    60.
    Spring, S., Kämpfer, P. & Schleifer, K. H. Limnobacter thiooxidans gen. nov., sp. nov., a novel thiosulfate-oxidizing bacterium isolated from freshwater lake sediment. Int. J. Syst. Evol. Microbiol. 51, 1463–1470 (2001).
    CAS  PubMed  Article  PubMed Central  Google Scholar 

    61.
    Makhdoumi, A. Bacterial diversity in south coast of Caspian Sea: culture-dependent and culture-independent survey. Casp. J. Environ. Sci. 16, 259–269 (2018).
    Google Scholar 

    62.
    Lindh, M. V. et al. Transplant experiments uncover Baltic Sea basin-specific responses in bacterioplankton community composition and metabolic activities. Front. Microbiol. 6, 1–18 (2015).
    Article  Google Scholar 

    63.
    Shifteh Some’e, B., Ezani, A. & Tabari, H. Spatiotemporal trends and change point of precipitation in Iran. Atmos. Res. 113, 1–12 (2012).
    Article  Google Scholar 

    64.
    Mansouri Daneshvar, M. R., Ebrahimi, M. & Nejadsoleymani, H. An overview of climate change in Iran: facts and statistics. Environ. Syst. Res. 8, 7 (2019).
    Article  Google Scholar 

    65.
    Nercessian, O., Noyes, E., Kalyuzhnaya, M. G., Lidstrom, M. E. & Chistoserdova, L. Bacterial populations active in metabolism of C1 in the sediment of Lake Washington, a freshwater lake. Appl. Environ. Microbiol. 71, 6885–6899 (2005).
    CAS  PubMed  PubMed Central  Article  Google Scholar 

    66.
    Caporaso, J. G. et al. Ultra-high-throughput microbial community analysis on the Illumina HiSeq and MiSeq platforms. ISME J 6, 1621–1624 (2012).
    CAS  PubMed  PubMed Central  Article  Google Scholar 

    67.
    Takai, K. & Horikoshi, K. Rapid detection and quantification of members of the archaeal community by quantitative PCR using fluorogenic probes. Appl. Environ. Microbiol. 66, 5066–5072 (2000).
    CAS  PubMed  PubMed Central  Article  Google Scholar 

    68.
    Edgar, R. C. & Flyvbjerg, H. Sequence analysis error filtering, pair assembly and error correction for next-generation sequencing reads. Bioinformatics 31, 3476–3482 (2015).
    CAS  PubMed  Article  PubMed Central  Google Scholar 

    69.
    Maarastawi, S. A., Frindte, K., Linnartz, M. & Knief, C. Crop rotation and straw application impact microbial communities in Italian and Philippine soils and the rhizosphere of Zea mays. Front. Microbiol. 9, 1295 (2018).
    PubMed  PubMed Central  Article  Google Scholar 

    70.
    R Core Team. R: A language and environment for statistical computing version 3.2.5. Vienna: R Foundation for Statistical Computing. (2016).

    71.
    Oksanen, J. et al. Vegan: community ecology package. R package version 2.0-10 (2013). More