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    Host selection pattern and flavivirus screening of mosquitoes in a disturbed Colombian rainforest

    1.Figueiredo, M. Human urban arboviruses can infect wild animals and jump to sylvatic maintenance cycles in South America. Front Cell Infect. Microbiol. 9, 1–6. https://doi.org/10.3389/fcimb.2019.00259 (2019).Article 

    Google Scholar 
    2.Reeves, L. E. et al. Interactions between the invasive Burmese python, Python bivittatus Kuhl, and the local mosquito community in Florida. PLoS ONE 13, 1–15. https://doi.org/10.1371/journal.pone.0190633 (2018).CAS 
    Article 

    Google Scholar 
    3.Reeves, L. E., Gillett-Kaufman, J. L., Kawahara, A. Y. & Kaufman, E. Barcoding blood meals : New vertebrate- specific primer sets for assigning taxonomic identities to host DNA from mosquito blood meal. PLoS Negl. Trop. Dis. 12, 1–18. https://doi.org/10.1371/journal.pntd.0006767 (2018).CAS 
    Article 

    Google Scholar 
    4.Makanga, B. et al. “Show me which parasites you carry and I will tell you what you eat”, or how to infer the trophic behavior of hematophagous arthropods feeding on wildlife. Ecol. Evol. 7, 7578–7584. https://doi.org/10.1002/ece3.2769 (2017).Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    5.Burkett-Cadena, N. D., Bingham, A. M., Porterfield, C. & Unnasch, T. R. Innate preference or opportunism : Mosquitoes feeding on birds of prey at the Southeastern raptor center. Vector Ecol. 39, 21–31. https://doi.org/10.1111/j.1948-7134.2014.12066.x (2014).Article 

    Google Scholar 
    6.Mendenhall, I. H., Tello, S. A., Neira, L. A., Castillo, L. F. & Ocampo, C. B. Host preference of the Arbovirus vector Culex erraticus (Diptera: host preference of the arbovirus vector Culex erraticus ( Diptera : Culicidae ) at Sonso Lake, Cauca valley department, Colombia. J. Med. Entomol. 49, 1092–1102. https://doi.org/10.1603/me11260 (2012).CAS 
    Article 
    PubMed 

    Google Scholar 
    7.Harrington, L. C. et al. Why do female Aedes aegypti (Diptera: Culicidae ) feed preferentially and frequently on human blood?. J. Med. Entomol. 38, 411–422. https://doi.org/10.1603/0022-2585-38.3.411 (2001).CAS 
    Article 
    PubMed 

    Google Scholar 
    8.Catenacci, L. S. et al. Surveillance of Arboviruses in Primates and Sloths in the Atlantic Forest, Surveillance of Arboviruses in Primates and Sloths in the Atlantic Forest, Bahia, Brazil. EcoHealth 15, 777–791. https://doi.org/10.1007/s10393-018-1361-2 (2018).CAS 
    Article 
    PubMed 

    Google Scholar 
    9.Dos Santos, T. et al. Potential of Aedes albopictus as a bridge vector for enzootic pathogens at the urban-forest interface in Brazil. Emerging Infect. Dis. 28, 191. https://doi.org/10.1038/s41426-018-0194-y (2018).Article 

    Google Scholar 
    10.Borremans, B. et al. Cross-species pathogen spillover across ecosystem boundaries: mechanisms and theory. Phil. Trans. R. Soc. B. 374, 20180344. https://doi.org/10.1098/rstb.2018.0344 (2019).Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    11.Weaver, S. C. Urbanization and geographic expansion of zoonotic arboviral diseases: mechanisms and potential strategies for prevention. Trends in Microbiol. 21, 360–363. https://doi.org/10.1016/j.tim.2013.03.003 (2013).CAS 
    Article 

    Google Scholar 
    12.Caron, A., Cappelle, J., Cumming, G. S., Garine-wichatitsky, M. D. & Gaidet, N. Bridge hosts, a missing link for disease ecology in multi-host systems. Vet. Res. 46, 1–11. https://doi.org/10.1186/s13567-015-0217-9 (2015).CAS 
    Article 

    Google Scholar 
    13.Komar, N. & Clark, G. G. West Nile virus activity in Latin America and the Caribbean. Rev. Panam. Salud Publica. 19, 112–117. https://doi.org/10.1590/S1020-49892006000200006 (2006).Article 
    PubMed 

    Google Scholar 
    14.Huba, Z. & Weissenbo, H. Zoonotic mosquito-borne flaviviruses: Worldwide presence of agents with proven pathogenicity and potential candidates of future emerging diseases. Vet. Microbiol. 140, 271–280. https://doi.org/10.1016/j.vetmic.2009.08.025 (2010).Article 

    Google Scholar 
    15.Barrera, R., Navarro, J. & Liria, J. Contrasting sylvatic foci of Venezuelan equine encephalitis virus in Northern South America. Am. J. Trop. Med. Hyg. 67, 324–34. https://doi.org/10.4269/ajtmh.2002.67.324 (2002).Article 
    PubMed 

    Google Scholar 
    16.Hoyos-López, R., Soto, S. U., Rúa-Uribe, G. & Gallego-Gómez, J. C. Molecular identification of Saint Louis encephalitis virus genotype IV in Colombia. Mem. Inst. Oswaldo Cruz. 110, 719–725. https://doi.org/10.1590/0074-02760280040110 (2015).Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    17.Guzmán, C., Calderón, A., Martinez, C., Oviedo, M. & Mattar, S. Eco-epidemiology of the Venezuelan equine encephalitis virus in bats of Córdoba and Sucre, Colombia. Acta Trop. 191, 178–184. https://doi.org/10.1016/j.actatropica.2018.12.016 (2019).Article 
    PubMed 

    Google Scholar 
    18.Torres-Gutierrez, C. et al. Mitochondrial COI gene as a tool in the taxonomy of mosquitoes Culex subgenus melanoconion. Acta Trop. 164, 137–149. https://doi.org/10.1016/j.actatropica.2016.09.007 (2016).Article 
    PubMed 

    Google Scholar 
    19.Torres-Gutierrez, C. & Sallum, M. A. M. Catalog of the subgenus melanoconion of Culex (Diptera: Culicidae) for South America. Zootaxa. 4028, 1–50. https://doi.org/10.11646/zootaxa.4028.1.1 (2015).Article 
    PubMed 

    Google Scholar 
    20.Torres-Gutierrez, C., Oliveira, T. M. P., Bergo, E. S. & Sallum, M. A. M. Molecular phylogeny of Culex subgenus Melanoconion (Diptera: Culicidae) based on nuclear and mitochondrial protein-coding genes. R Soc Open Sci. 5, 171900. https://doi.org/10.1098/rsos.171900 (2018).ADS 
    CAS 
    Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    21.Beebe, N. W. DNA barcoding mosquitoes: advice for potential prospectors. Parasitol. 145, 622–633. https://doi.org/10.1017/S0031182018000343 (2018).CAS 
    Article 

    Google Scholar 
    22.Laurito, M., De Oliveira, T. M. P., Almirón, W. R., Anice, M. & Sallum, M. COI barcode versus morphological identification of Culex (Culex) (Diptera: Culicidae) species: a case study using samples from Argentina and Brazil. Mem. Inst. Oswaldo Cruz. 108, 110–122. https://doi.org/10.1590/0074-0276130457 (2013).Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    23.IUCN 2020. The IUCN Red List of Threatened Species. https://www.iucnredlist.org (2020).24.Roque, A. L. R. & Jansen, A. M. Wild and synanthropic reservoirs of Leishmania species in the Americas. Int J Parasitol Parasites Wildl. 3, 251–262 (2014).Article 

    Google Scholar 
    25.Palermo, P. M. et al. Identification of blood meals from potential Arbovirus mosquito vectors in the peruvian amazon basin. Am. J. Trop. Med. Hyg. 95, 1026–1030. https://doi.org/10.4269/ajtmh.16-0167 (2016).Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    26.Silva, S., Alencar, J., Costa, J. M., Seixas-lorosa, E. & Guimarães, A. É. Feeding patterns of mosquitoes (Diptera: Culicidae) in six Brazilian environmental preservation areas. J. Vector Ecol. 37, 342–350. https://doi.org/10.1111/j.1948-7134.2012.00237 (2012).Article 

    Google Scholar 
    27.Edman, J. D. Host-feeding patterns of florida mosquitoes I. Aedes, anopheles, coquillettidia, Mansonia and Psorophora. J. Med. Entomol. 30, 687–695. https://doi.org/10.1093/jmedent/8.6.687 (1971).Article 

    Google Scholar 
    28.Gabriel, Z. et al. Culex nigripalpus Theobald (Diptera, Culicidae) feeding habit at the Parque Ecológico. Rev. Bras. Entomol. 52, 4. https://doi.org/10.1590/S0085-56262008000400019 (2008).Article 

    Google Scholar 
    29.Zimmerman, R. H., Galardo, A. K., Lounibos, L, P., Arruda, M. & Wirtz, R. Bloodmeal Hosts of Anopheles Species (Diptera: Culicidae) in a Malaria-Endemic Area of the Brazilian Amazon. J. Med. Entomol. 43, 947–56. https://doi.org/10.1093/jmedent/43.5.947 (2006).Article 
    PubMed 

    Google Scholar 
    30.Mitchell, C. J. et al. Hostfeeding patterns of Argentine mosquitoes (Diptera: Culicidae) collected during and after an epizootic of western equine encephalitis. J. Med. Entomol. 24, 260–267. https://doi.org/10.1093/jmedent/24.2.260 (1987).CAS 
    Article 
    PubMed 

    Google Scholar 
    31.Stein, M., Zalazar, L., Willener, J. A., Almeida, F. L. & Almirón, W. R. Culicidae (Diptera ) selection of humans, chickens and rabbits in three different environments in the province of Chaco, Argentina. Mem. Inst. Oswaldo Cruz. 108, 563–571. https://doi.org/10.1590/0074-0276108052013005 (2013).Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    32.Burkett-Cadena, N. D. et al. Blood. Feeding patterns of potential arbovirus vectors of the genus. Am. J. Trop. Med. Hyg. 79, 809–815 (2008).Article 

    Google Scholar 
    33.Takken, W. & Verhulst, N. O. Host preferences of blood-feeding mosquitoes. Annu. Rev. Entomol. 13, 433–453. https://doi.org/10.1146/annurev-ento-120811-153618 (2013).CAS 
    Article 

    Google Scholar 
    34.Besansky, N. J., Hill, C. A. & Costantini, C. No accounting for taste: host preference in malaria vectors. Trends Parasitol. 20, 249–251. https://doi.org/10.1016/j.pt.2004.03.007 (2004).Article 
    PubMed 

    Google Scholar 
    35.Burkett-Cadena, N. D. & Hayes, L. E. Hosts or habitats: What drives the spatial distribution of mosquitoes? Hosts or habitats. Ecosphere 4, 30. https://doi.org/10.1890/ES13-00009.1 (2013).Article 

    Google Scholar 
    36.Borkent, A. & Belton, P. Attraction of female Uranotaenia lowii (Diptera: Culicidae) to frog calls in Costa Rica. Cambridge Univ. 94, 91–94. https://doi.org/10.4039/n04-113 (2006).Article 

    Google Scholar 
    37.Scott, T. W. & Takken, W. Feeding strategies of anthropophilic mosquitoes result in increased risk of pathogen transmission. Trends Parasitol. 28, 114–121. https://doi.org/10.1016/j.pt.2012.01.001 (2012).Article 
    PubMed 

    Google Scholar 
    38.Dizney, L. J. & Ruedas, L. A. Increased host species diversity and decreased prevalence of Sin nombre virus. Emerg. Infect. Dis. 15, 1012–1018. https://doi.org/10.3201/eid1507.081083 (2009).Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    39.Krasnov, B. R. et al. Phylogenetic signal in module composition and species connectivity in compartmentalized host-parasite networks. Am. Nat. 179, 501–511. https://doi.org/10.1086/664612 (2012).Article 
    PubMed 

    Google Scholar 
    40.Rodrigues, B. N. & Boscolo, D. Do bipartite binary antagonistic and mutualistic networks have different responses to the taxonomic resolution of nodes?. Ecol. Entomol. 45, 709–717. https://doi.org/10.1111/een.12844 (2020).Article 

    Google Scholar 
    41.Segar, S. et al. The role of evolutionary processes in shaping ecological networks. Trends Ecol. Evol. 35, 4454–4466 (2020).
    Google Scholar 
    42.Svensson-Coelho, A. M. et al. Reciprocal specialization in multihost malaria parasite communities of birds: A temperate-tropical comparison. Am. Nat. 184, 624–635. https://doi.org/10.1086/678126 (2014).Article 
    PubMed 

    Google Scholar 
    43.Ghalmane, Z., El Hassouni, M., Cherifi, C. & Cherifi, H. Centrality in modular networks. EPJ. Data. Sci. 8, 15. https://doi.org/10.1140/epjds/s13688-019-0195-7 (2019).Article 

    Google Scholar 
    44.Rushmore, J., Bisanzio, D. & Gillespie, T. R. Making new connections: insights from primateparasite networks. Trends Parasitol. 33, 547–560 (2017).Article 

    Google Scholar 
    45.de Carneiro, I.O. et al. Knowledge, practice and perception of human-marsupial interactions in health promotion. J. Infect. Dev. Ctries. 13, 342–347. https://doi.org/10.3855/jidc.10177 (2019).Article 
    PubMed 

    Google Scholar 
    46.Root, J. J. et al. Serologic evidence of exposure of wild mammals to flaviviruses in the central and eastern United States. Am. J. Trop. Med. Hyg. 72, 622–630 (2005).Article 

    Google Scholar 
    47.Cardoso, J. et al. Yellow Fever Virus in Haemagogus leucocelaenus and Aedes serratus. Emerg. Infect. Dis. 16, 1918–1924. https://doi.org/10.3201/eid1612.100608 (2010).Article 
    PubMed Central 

    Google Scholar 
    48.Muñoz, M. & Navarro, J. C. Virus Mayaro: un arbovirus reemergente en Venezuela y Latinoamérica. Biomedica 32(286–302), 2012. https://doi.org/10.7705/biomedica.v32i2.64786-302 (2012).Article 

    Google Scholar 
    49.Turell, M. J. et al. Susceptibility of peruvian mosquitoes to eastern equine encephalitis virus. J. Med. Entomol. 45, 720–725. https://doi.org/10.1603/0022-2585 (2008).CAS 
    Article 
    PubMed 

    Google Scholar 
    50.Ferro, C. et al. Natural enzootic vectors of venezuelan equine encephalitis virus. Emerg. Infect. Dis. 9, 49–54. https://doi.org/10.3201/eid0901.020136 (2003).Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    51.Marsh, C., Link, A., King-Balley, G. & Donati, G. Effects of fragment and vegetation structure on the population abundance of Ateles hybridus, Alouatta seniculus and Cebus albifrons in Magdalena Valley, Colombia. Folia. Primatol. 87, 17–30. https://doi.org/10.1159/000443929 (2016).Article 

    Google Scholar 
    52.Link, A., De Luna, A. G., Alfonso, F., Giraldo-Beltran, P. & Ramirez, F. Initial effects of fragmentation on the density of three neotropical primate species in two lowland forests of Colombia. Endanger. Species Res. 13, 41–50. https://doi.org/10.3354/esr00312 (2010).Article 

    Google Scholar 
    53.Galindo, P., Blanton, S. & Peyton, E. L. A revision of the Uranotaenia of Panama with notes on other American species of the genus (Diptera, Culicidae). Ann. Entomol. Soc. Am. 47, 107–177. https://doi.org/10.1093/aesa/47.1.107 (1954).Article 

    Google Scholar 
    54.Forattini, O.P. Culicidologia médica Identificacao, biologia e epidemiologia. 884. (EDUSP, Sao Paulo, 2002).55.Folmer, Black, M., Hoeh, W. & Lutz, R. 1994 DNA primers for amplification of mitochondrial Cytochrome C oxidase subunit I from diverse metazoan invertebrates DNA primers for amplification of mitochondrial cytochrome c oxidase subunit I from diverse metazoan invertebrates. Mol. Mar. Biol. Biotechnol. 3, 294–299 (1994).CAS 
    PubMed 

    Google Scholar 
    56.Puillandre, N., Lambert, A., Brouillet, S. & Achaz, G. ABGD. Automatic barcode gap discovery for primary species delimitation. Mol. Ecol. 21, 1864–1877 (2012).CAS 
    Article 

    Google Scholar 
    57.Guindon, S. et al. New Algorithms and methods to estimate maximum-likelihood phylogenies: Assessing the performance of PhyML 3.0. Syst. Biol. 59, 307–321. https://doi.org/10.1093/sysbio/syq010 (2010).CAS 
    Article 
    PubMed 

    Google Scholar 
    58.Molaei, G., Andreadis, T. G., Armstrong, P. M., Anderson, J. F. & Vossbrinck, C. R. Host feeding patterns of Culex Mosquitoes and West Nile virus transmission, Northeastern United States. Emerg. Infect. Dis. 12, 468–474. https://doi.org/10.3201/eid1203.051004 (2006).Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    59.Ferro, C. et al. Phlebotomine vector ecology in the domestic transmission of American cutaneous leishmaniasis in chaparral, Colombia. Am. J. Trop. Med. Hyg. 85, 847–856. https://doi.org/10.4269/ajtmh.2011.10-0560 (2011).Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    60.Moureau, G. et al. A real-time RT-PCR method for the universal detection and identification of flaviviruses. Vector Borne Zoonotic Dis. 7, 467–477. https://doi.org/10.1089/vbz.2007.0206 (2007).CAS 
    Article 
    PubMed 

    Google Scholar 
    61.Lanciotti, R. S. et al. Genetic and serologic properties of zika virus associated with an epidemic, Yap State. Emerg. Infec. Dis. 14, 1232–1239. https://doi.org/10.3201/eid1408.080287 (2008).CAS 
    Article 

    Google Scholar 
    62.Bastian, M., Heymann, S. & Jacomy, M. Gephi: an open source software for exploring and manipulating networks. Icwsm. 8, 361–362 (2009).
    Google Scholar 
    63.Beckett, S. J. Improved community detection in weighted bipartite networks. R. Soc. Open Sci. https://doi.org/10.1098/rsos.140536 (2016).MathSciNet 
    Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    64.Dormann, C. F. & Strauss, R. A method for detecting modules in quantitative bipartite networks. Methods Ecol. Evol. 5, 90–98. https://doi.org/10.1111/2041-210X.12139 (2014).Article 

    Google Scholar 
    65.Almeida-Neto, M., Guimara, P., Guimara, P. R. & Ulrich, W. A consistent metric for nestedness analysis in ecological systems: reconciling concept and measurement. Oikos 117(1227–1239), 39. https://doi.org/10.1111/j.2008.0030-1299.16644.x (2008).Article 

    Google Scholar 
    66.Almeida-Neto, M. & Ulrich, W. Environmental Modelling & Software A straightforward computational approach for measuring nestedness using quantitative matrices. Environ. Model. Softw. 26, 173–178. https://doi.org/10.1016/j.envsoft.2010.08.003 (2011).Article 

    Google Scholar 
    67.Bascompte, J., Olesen, J. M., Jordano, P. & Melia, C. J. The nested assembly of plant–animal mutualistic networks. PNAS 100, 9383–9387. https://doi.org/10.1073/pnas.1633576100100 (2003).ADS 
    CAS 
    Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    68.Blüthgen, N., Menzel, F. & Blüthgen, N. Measuring specialization in species interaction networks. BMC Ecol. https://doi.org/10.1186/1472-6785-6-9 (2006).Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    69.Romain, J., Joanne, C., Vincent, D., Frederic, J. & Denis, C. Spatial segregation of specialists and generalists in bird communities. Ecol. Lett. 9, 1237–1244. https://doi.org/10.1111/j.1461-0248.2006.00977.x (2006).Article 

    Google Scholar 
    70.Bascompte, J., Jordano, P. & Olesen, J. M. Facilitate biodiversity maintenance. Science 312, 431–433. https://doi.org/10.1126/science.1123412 (2006).ADS 
    CAS 
    Article 
    PubMed 

    Google Scholar  More

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    Confirmation of Oryctes rhinoceros nudivirus infections in G-haplotype coconut rhinoceros beetles (Oryctes rhinoceros) from Palauan PCR-positive populations

    Insects and virusOryctes rhinoceros was collected from Amami, Kagoshima, Japan in 2017 and Ishigaki, Okinawa, Japan in 2018. The insects were brought back to the lab in Tokyo and maintained in a moisture mushroom mat substrate (Mushroom Mat, Tsukiyono Kinokoen, Japan) which was also served as food for larvae. The temperature was held at 25–30 °C with a 16-h light / 8-h dark photoperiod. To collect eggs, 2 or 3 female adults were put in a plastic case containing a moisture mushroom mat substrate with a male adult beetle. The insect jelly (Dorcus Jelly, Fujikon, Japan) was provided ad libitum as food for adults. After 2 weeks, we collected eggs, and about 10 eggs were placed in a plastic cup with a moisture mushroom mat substrate until hatched larvae developed to the second instar. This strain was used in all bioassays in this study. All Japanese O. rhinoceros were confirmed as CRB-G.The OrNV-X2B isolate used in this study was originally isolated from Philippine CRB and obtained from AgResearch in New Zealand.Cell culturesFRI-AnCu-35 (AnCu35) cells were obtained from Genebank of NARO (Tsukuba, Japan)27. This continuous cell line was developed from embryos of the cupreous chafer, Anomala cuprea (Coleptera: Scarabaeidae). The cells were maintained as adherent cultures in 25 cm2 tissue culture flasks (Falcon, Corning, USA) at 25 °C in 5 ml of 10% Fetal Bovine Serum (Gibco, Thermo Fisher Scientific, USA) supplemented Grace’s insect medium (Gibco). Cells were passaged in the above culture medium until the cell monolayer reached 70% confluence.DNA extraction and identification of haplotypes in Palauan populationCRB specimens were collected in Palau using pheromone traps containing ethyl 4-methyloctanoate (ChemTica Internacional, Costa Rica). Adults were dissected to collect midgut and gut tissues to avoid cross contamination between dissection of individuals, which were immediately soaked into 0.1 μg/ml gentamicin solution to prevent bacterial contamination during transportation at room temperature. Specimens were stored at − 30 °C after arrival to Tokyo. The tissues were homogenized in cell lysis solution (10 mM Tris–HCl, 100 mM EDTA, 1% SDS, pH 8.0) using pestles in 1.5 ml microcentrifuge tubes. Homogenates were centrifuged at 12,000× g for 5 min at 4 °C. Proteinase K (200 µg/ml final concentration) (Nippon Gene Co. Ltd., Japan) was added to the supernatant and incubated at 50 °C for 5 h. To remove contaminating RNA, RNase A solution (100 µg/ml final concentration) (Nippon Gene Co. Ltd.) was added. After a 30 min incubation at 37 °C, the mixture was placed on ice and supplemented with 200 μl of Protein Precipitation Solution (Qiagen, Germany), and then centrifuged at 17,000× g for 15 min at 4 °C. The supernatant was isopropanol-precipitated, pelleted by centrifugation, and washed with 70% ethanol. Finally, precipitated DNA was dissolved in distilled MilliQ water. The concentrations of each DNA solution were measured by using NanoVue Plus (GE Healthcare, Buckinghamshire, England, UK). The sample DNA was diluted to 10 ng/μl and used for PCR. The following primer pair was used to amplify a 523 bp fragment of the COI gene: C1-J-1718Oryctes (5′-GGAGGTTTCGGAAATTGACTTGTTCC-3′) and C1-N-2191Oryctes (5′-CCAGGTAGAATTAAAATRTATACCTC-3′)9. Each 10 μl PCR reaction contained: 5 μl Emerald Amp (Takara, Japan), 0.3 μl forward primer (10 μM), 0.3 μl reverse primer (10 μM), 3.4 μl Milli-Q water (Merck Millipore, USA), and 1 µl template DNA. PCR amplifications were performed in a Life ECO thermocycler (Bioer Technology, China) with a cycling profile of 35 cycles of 94 °C denaturation (30 s), 50 °C annealing (45 s), 72 °C extension (1 min) with an initial denaturation of 3 min at 94 °C and a final extension of 5 min at 72 °C. A 5 μl aliquot of each PCR amplicon was checked by agarose gel electrophoresis (1.5%, 1 × TBE), stained with Midori green (Nippon Genetics, Japan) and fluorescence visualized over UV light. Photographs were recorded using an E-BOX-VX2 /20 M (E & M, Japan).For direct sequencing, the PCR products were purified using a QIAquick PCR Purification Kit (Qiagen). The purified DNA was sequenced using BigDye Terminator Kit ver. 3.1 (Applied Biosystems, USA) and performed by the 3700 DNA analyzer (Applied Biosystems). The obtained sequences were analyzed using MEGA X software28 and the G haplotype was identified by the presence of the (A→G) point mutation in the COI region as previously described9.Virus detection in Palauan populationUsing the same samples as above, virus detection was carried out by PCR. The following primer pair was used to amplify a 944 bp fragment of the OrNV-gp054 gene (GrBNV-gp83-like protein): OrNV15a (5′-ATTACGTCGTAGAGGCAATC-3′) and OrNV15b (5′-ATGATCGATTCGTCTATGG-3′)29. PCR amplifications were performed as above.Transmission electron microscopy (TEM) was also used for detection of OrNV within a subset of PCR positive CRB tissue samples. After washing in phosphate-buffered saline (PBS), midgut and fat body samples of Palauan CRB adults from Melekeok and Aimeliik (respectively; two each), were subjected to following resin fixation as described previously30: tissues were fixed in 5% glutaraldehyde for 1 h, rinsed 4 times with Millonig’s phosphate buffer (0.18% NaH2PO4 × H2O, 2.33% Na2HPO4 × 7H2O, 0.5% NaCl, pH 7.4), post-fixed and stained in 1% OsO4 for 2 h and dehydrated in an ethanol series. Following the final dehydration step, the ethanol was replaced by QY-1 (Nisshin EM, Tokyo), and the tissues were embedded in epoxy resin comprising 47% TAAB EPON812, 19% DDSA, 32% MNA and 2% DMP30 (Nisshin EM, Tokyo). Then, they were cut into 70 nm thick sections with a diamond knife on an Ultracut N ultramicrotome (Leica, Vienna, Austria), attached to grids and observed using TEM (JEM-1400Plus, JEOL, Japan).Isolation of OrNV from Palauan samples and infectivity to Japanese CRB larvaeVirus isolation was carried out using a modification of a method previously described23. The frozen tissues of two virus positive CRB-G from Melekeok were washed with PBS twice, and after grounding with 1 ml PBS by pestles, centrifuged at 6,000 g × 5 min at 4 °C. The supernatant was filtered by 0.45 µm pore sized filter (Merck, USA) and transferred to a 1.5 ml ultracentrifuge tube in a clean bench. Virus was pelleted by centrifugation at 4 °C, 98,600 g for 30 min using a TLA55 rotor. After separation, the supernatant was discarded and the pellet was suspended in 500 μl of PBS and designated as “virus solution”. A portion of this solution (30 µl/larva) was intrahemocoelically injected into 82nd instar CRB to evaluate its infectivity. This experiment had no biological replicates due to the very small amount of inoculum available. Intrahemocoelically injected larvae were reared in the insect rearing mat at 25 °C for two weeks. Following death, larval cadavers were immediately dissected to collect midgut for following RNA extraction to detect expression of a viral gene, and electron microscopy observation. Total RNA was extracted from larval tissue samples using ISOGEN (Nippon Gene Co. Ltd., Tokyo, Japan), as described in the manufactural protocol. The total RNA samples were treated with RNAse-free recombinant DNAse I (TaKaRa, Japan) to remove the contaminating DNAs. The DNAse I treated total RNA samples (approximately 100 ng/µl) were used as templates for cDNA synthesis using a TaKaRa RNA PCR Kit (AMV) ver. 3.0 (TaKaRa, Japan). PCR reactions were conducted as above using OrNV15a and b primers (detects gene GrBNV-gp83-like gene). This experiment was conducted in triplicate.Inoculum preparation using FRI-AnCu-35 cellsOrNV isolates were propagated using the FRI-AnCu-35 (AnCu35) cell line for further analyses following methods previously described for the DSIR-Ha-1179 cell line system9,12. AnCu35 was a Coleopteran cell line readily available in Japan, and was inoculated with the Palau OrNV solution prepared above and the OrNV-X2B isolate which was provided by AgResearch, New Zealand. When the cell culture reached 25% confluency, a 100 µl aliquot of virus solution was inoculated and incubated at 25 °C. The virus-treated cells were observed by optical microscope.Quantification of viral copy number using qPCR was conducted as follows. To measure the amount of OrNV virus produced by the AnCu35 cell line, DNA was extracted as described above for tissue samples from 1.5 ml of the virus treated cell’s suspension at 10 dpi (3 suspensions per each virus isolate). The extracted DNA was subjected to quantitative PCR (qPCR) following previously described methods31. The primer pair for qPCR was designed from the genome sequence of the P74 homolog of OrNV, a viral structural protein that is conserved widely among nudiviruses, polydnaviruses and baculoviruses32, to amplify a region of 82 bp of OrNV-X2B-gp120 (OrNV-p74_f2026: 5′-ATCGCCGGTGTGTTTATGG-3′, OrNV-p74_r2107: 5′-AGAGGGCTAACGCTACGAC-3′). The qPCR reaction was performed by using Step One Plus Real-Time PCR System (Life Technologies, USA). The reaction mixture contained 10 ng of template DNA, 5 µl of FastStart Universal SYBR Green Master Mix (ROX) (Roche, Switzerland), 0.3 µl forward primer (10 µM), 0.3 µl reverse primer (10 µM), and 3.4 µl Milli-Q water. The qPCR cycle condition was as follows: 95 °C 10 min; 40 cycle of 95 °C 15 s, 60 °C 1 min. At the end of the cycles, a dissociation curve analysis of the amplified product was performed as follows: 95 °C 15 s, 60 °C 1 min, 95 °C 15 s. The Ct value of each sample DNA was measured twice using two wells as technical replicates. The quantity of the viral genome (ng) in each sample was calculated from a standard curve generated from 29.7 to 29.7 × 10–5 ng of purified PCR amplicon from the OrNV P74 gene. The viral copies in 1 ng of sample DNA was estimated from the molecular weight of qPCR target region (p74). The virus titer was determined from average copy numbers of three virus suspensions as follows. The p74 qPCR amplicon was 83 bp, and the molecular weight of the amplicon was calculated as the length of dsDNA (83 bp) × 330 daltons × 2 nt/bp = 54,780 daltons (g/mol). DNA weight of 1 copy of virus genome was calculated as 54,780 g/mol/Avogadro constant (6.023 × 1023 molecules/mol) = 9.095 × 10–20 g/ molecule. Amplicons of the above region was purified by QIA quick PCR purification kit (Qiagen) and 29.7 ng/ul of DNA was obtained for use as a quantification standard. This is equivalent to 3.266 × 1011 copies of p74 gene (because the amplicon is 9.095 × 10–20 g/copy). Based on qPCR using the serial dilutions (× 10 – 105) of the standard DNA prepared above, Ct values were examined by each concentration of viral DNA. Ct-value = − 3.3112x – 1.4219 (x: diluton factor of 10x). Accordingly, copy number of p74 = 3.266 × 1011+x. Viral copy number (copy number of p74 genes) was calculated from Ct-value from the above formula.Viral replication in CRB larvae by time course and killing speedField collected CRB-G larvae from Japan were inoculated with the OrNV-Palau1 and -X2B isolates to examine establishment of infection over time using qPCR. The inoculum was prepared from supernatant collected from OrNV infected AnCu35 cell cultures at 10 dpi, passed through a 0.45 µm filter, and preserved at 4 °C until use.Second instar CRB was inoculated intrahemocoelically with 30 μl of the virus solution prepared from cell-culture per larva using a microinjector (Kiya Kogyo Seisakusho, Japan) fitted with a micro-syringe (Ito Seisakusho, Japan). The virus doses of OrNV-Palau1 and -X2B strains used for inoculation were confirmed to be comparable by absolute quantification using the above qPCR method (Palau1: 3.1 × 105 copies/ng, X2B: 3.3 × 105copies/ng; the mean titer of 3 DNA templates, respectively). As a mock treatment, CRB was injected with 30 µl PBS. The inoculated larvae were kept individually in plastic containers with a rearing mat in a 25 °C incubator. The samples were collected at 3, 6, and 9 dpi (25–30 larvae per time point) into 15 ml tubes and stored at − 30 °C until the DNA was extracted as above. Total DNA was extracted from whole, individual larvae which were dissected to remove midgut contents to prevent interference to Taq polymerase, and subjected to qPCR as above. Changes in viral copy number within the same virus strain over time were analyzed by one-way, nonparametric Steel–Dwass tests using JMP@ 9.0.0 software (SAS Institute, Cary, NC). Differences in virus copy number between strains were analyzed in the same way, but to correct for errors in the test values due to multiple comparisons, Bonferroni’s correction was used to set the α-value for the test at 0.008333. Ten larvae were inoculated and examined per each treatment-time point with three replications.To estimate killing speed, CRB-G larvae from Japan were inoculated with the OrNV-Palau1 and -X2B isolates as described previously. Intrahemocoelically inoculated larvae were reared individually in plastic containers with a rearing mat in a 25 °C incubator. Mortality of inoculated larvae were observed every day. Forty larvae were examined in a replicate with three replications carried out for virus treatments (total 120 larvae). The mock PBS inoculation treatment was done only once (total 37 larvae).Genome sequencingGenome sequencing of the OrNV-Palau1 isolate and X2B isolate was conducted. For obtaining high quality DNA, virus particles were purified, from 3 mL of AnCu35 culture supernatant collected six days after inoculation with OrNV. Virus containing supernatant was transferred to Ultra-Clear polyallomer tubes (Beckman Coulter, USA) with a 20–50% (w/w) sucrose density gradient and subjected to ultracentrifugation at 72,100 g, 4 °C, for 1 h. After ultracentrifugation, the white virus band was collected in a 1.5 ml tube. The solution was then subjected to ultracentrifugation at 110,000 g, 4 °C for 1 h to precipitate the viral particles33. Then, DNA was extracted from purified OrNV virions as described above. For the sequencing analysis, DNA libraries were prepared using the Nextera XT DNA Library Prep Kit (Illumina, USA). Amplified libraries were sequenced on Illumina HiSeq 2500 instrument using paired-end 2 × 150 bp chemistry which was performed by Novogene (Beijing, China). Contigs of each strain from NGS reads were generated by assembly using Unicycler (version 0.4.8)34. The gaps between contigs were further closed with Sanger sequences obtained by PCR direct sequencing using appropriate specific primers, and the sequence was aligned by minimap2 (version 2.17)35. The assembly and sequences of contigs were also confirmed by mapping to the OrNV isolate Solomon Islands genome sequence (GenBank accession no. MN623374.1) with NGS reads and Sanger sequences using minimap2. The mapped reads (SAM files) were converted to BAM format using SAMtools (version 1.10)36. After the sorting and indexing of BAM files, the consensus sequences were generated using bcftools (version 1.10.2)37.ORFs of at least 50 codons in size that possessed significant amino acid sequence similarity with ORFs from OrNV-Ma07 were identified with Lasergene GeneQuest (DNAStar, v. 17) and BLASTp. ORFs with no significant matches to other sequences also were selected for annotation if (a) they did not overlap a larger ORF by  > 75 bp, and (b) they were predicted to be protein-encoding by both the fgenesV0 (http://www.softberry.com/berry.phtml?topic=index&group=programs&subgroup=gfindv) and Vgas38 programs.OrNV genome sequences were compared by pairwise alignment using the Martinez/Needleman-Wunsch method as implemented in Lasergene MegAlign 15. Pairwise sequence identities were determined from these alignments as previously described39. Differences in ORF content and distribution of selected OrNV genomic regions were visualized with Mauve version 2015022640.Phylogenetic inferenceTo infer the relationships among OrNV isolates on the basis of nucleotide sequence alignments, the DNA polymerase ORFs of completely sequenced isolates (Table 2), OrNV-PV50516, and a set of nine isolates from Indonesia17 were aligned by MUSCLE as implemented in Lasergene MegAlign Pro v. 17 (DNAStar). Phylogeny was inferred by maximum likelihood using MEGA X28 with the Tamura-Nei (TN93) model41, with ambiguous data eliminated prior to analysis. Tree reliability was evaluated by bootstrap with 500 replicates. More

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    Insights into the origin of the invasive populations of Trioza erytreae in Europe using microsatellite markers and mtDNA barcoding approaches

    Genome-wide characterisation of SSRsWe identified and mapped a total of 428,342 microsatellites across the 47,828 scaffolds of the unpublished genome sequence draft of T. erytreae using the GMATA software35. The SSRs frequency was estimated at 765.6 SSRs/Mb, which means 1 SSR for every 1.09 Kb. In silico identified SSRs were distributed among ten types of in tandem repeated motifs (from di- to deca-nucleotides). Analysis of SSR distribution revealed that the di-nucleotide motifs (340,227) were the most abundant SSRs, with a frequency of 79.4%. Both tetra- (20,902) and tri- (61,839) nucleotide repeats comprised about 5–15% (Fig. 1A; Supplementary Data 1). The remaining motifs, from hepta- to deca-nucleotides, comprised less than 1.5% of total SSRs identified in this study (Fig. 1A). Considering the unknown orientation of DNA strands in the Tery6 draft genome sequence of T. erytreae, a further SSRs characterization was carried out grouping the repeat motifs into pairs of complementary sequences. According to this, GA/TC (36.6%) and CT/AG (31.9%) are the most frequent motif pairs, with a total frequency of 68.5% (Fig. 1B). Grouped motif pairs GC/GC (0.05%) and CG/CG (~ 0.02%) were the least abundant di-nucleotide motifs. In decrease order, the most abundant tri-nucleotide motif pairs were ATT/AAT, ATA/TAT, ACA/TGT, TAA/TTA, AAC/GTT, TTG/CAA, and AAG/CTT, which encompassed 9.8% of all identified grouped motif pairs. Occurrence frequency of the remaining grouped motifs, including the rest of tri- and those from tetra- to deca-nucleotides (552 all together), was less than 11% of all motif pairs (Fig. 1B). Our data analysis reveals that SSR markers of 10 bp were most frequent, accounting for about 10% all SSR markers identified in this study. The overall trend of SSR length distribution in the T. erytreae genome is that the frequency of occurrence of SSRs gradually decreases as their length increases (Fig. 1C).Figure 1Frequency distribution of different classes of SSR repeat units in the Trioza erytreae genome. (A) Frequency of motif types by unit length (K-mers). (B) Frequency of grouped repeated motifs by nucleotide composition. (C) Length distribution of SSRs (total number of each type of SSR length is shown in the top of the bars).Full size imageSSR markers development for T. erytreae
    Fifteen SSRs chosen from those repeated motifs identified in silico in this study (Table 1) were used as potential markers to investigate the genetic diversity, structure and phylogeography of T. erytreae individuals from populations in mainland Europe and the archipelagos of the Macaronesia. Scaffolds Tery6_s00034 (274,710 bp), Tery6_s02825 (48,689 bp) and Tery6_s07841 (26739 bp) were randomly selected based on their sequence length (long, medium, and short scaffolds, respectively). SSRs were selected on the base of their type of repeat motif (di, tri-, tetra- and penta-nucleotides), nucleotide composition and length (number of in tandem repeated motifs) (Table 1; Supplementary Data 2). For the scaffold Tery6_s00034, 11 SSR loci were chosen from the total of 106 SSRs identified in silico, three for Tery6_s07841 and one for Tery6_s02825. Selected scaffolds were further investigated to know whether SSR loci mapped into coding or non-coding regions (inter-genic or intron sequences). Although gene annotation of the T. erytreae genome draft is not yet completed, it was possible to get this information for most of the selected SSR loci (data not shown). The scaffolds Tery6_s00034, Tery06, − 07, − 13 and − 14 were found in inter-genic regions, while Tery08, − 12 and − 15 were mapped into introns. For Tery05, − 9, − 10 and − 11 was not possible to establish whether they targeted coding or non-coding regions. SSR loci Tery01, − 02 and − 03 were found in intron regions in the scaffold Tery6_s07841, and SSR locus Tery04 in an inter-genic region in the sequence corresponding to the scaffold Tery6_s02825. For amplification of SSR loci, specific PCR primers were designed on the sequence flanking the in tandem repeated motifs. Blast of the different amplicons against the T. erytreae draft genome sequence showed that PCR primers would result in the specific amplification of their specific SSR locus. Experimental validation of PCR primers was carried out on a testing panel of individuals collected in different locations in the Canary Islands and South Africa. Primers pairs for SSR loci Tery04, − 05, − 06, − 08, − 09, − 10, − 11, − 12, − 13 and − 15 yielded DNA fragments of the expected size and were chosen for carry on further population genetic analysis. These loci contain eight di-nucleotides (AC, AG, GA, CA, GT, TC, TA and TG), one tri-nucleotide (TGA), and three tetra-nucleotides (CATA, CTAC and TACC), which arranged in microsatellites of different length (from 5 to 30 in tandem repeated motifs) (Table 1). Five SSR loci (Tery01, − 02, − 03, − 07 and − 14) were not amplified efficiently and the corresponding primer pairs were discarded for further analysis.Table 1 SSR loci developed in Trioza erytreae.Full size tableThe individuals of T. erytreae collected in different geographical locations in the west coast of mainland Spain and Portugal, the Canary Islands and Madeira, as well as in South Africa and Kenya (Table 2), were analysed using the 10 selected SSR markers designed in this study. The scored allelic data for each SSR marker is summarised in the Table 3. The analysis showed that all SSR markers were polymorphic. Seventy alleles were detected over the ten selected SSR loci, and the average number of alleles per locus (Na) was seven. SSR markers Tery08 and Tery11 had the highest number of alleles (12 and 20 alleles respectively), whereas Tery13 had the lowest (only two alleles). The expected (He) and observed (Ho) heterozygosity per locus in the entire population ranged from 0.20 to 0.77 and from 0.03 to 0.84, respectively. SSRs Tery11 and Tery08 displayed the highest diversity (He of 0.77 and 0.72, respectively), and Tery09 and Tery13 (He of 0.20 and 0.22, respectively) were the least informative markers. Most of the SSR markers used in this work showed He values higher than 0.5, apart from Tery05, − 09 and − 13 (with values of 0.39, 0.20 and 0.22, respectively). With the only exception of Tery04 and Tery15, for most of the analysed SSRs He was higher than Ho. It can be also observed that the whole population displayed a deficit of average Ho (0.31) compared with the He value (0.51) under Hardy–Weinberg equilibrium. This observation agrees with the positive value of the Wright’s fixation index (Fw) estimated for all analysed SSR markers over the whole population (Fw = 0.41). The SSR markers Tery12 and Tery13 showed Fw values close to 1.0 (0.81 and 0.85, respectively), suggesting that their alleles were considerably fixed in the population.Table 2 Collection data of T. erytreae populations.Full size tableTable 3 Statistical summary of the diversity of T. erytreae SSR markers.Full size tablePopulation structure based on T. erytreae SSR dataTo assess the differentiation and genetic diversity among the local populations of T. erytreae sampled in newly invaded areas from Spain and Portugal, including Madeira and the Canary Islands, and those from the previous invaded areas in Africa (South Africa and Kenya), we used a Bayesian clustering method to analyse the SSR multi-locus genotyping data. The STRUCTURE analysis according to the method of ΔK36 showed that the overall genetic profile of all the individuals sampled could be described with two or three different hypothetically original populations corresponding to the highest ΔK values (Fig. 2). It means that the most likely values of genetic clusters (K) are 2 or 3. Nevertheless, Pritchard’s method37 showed a posterior probability of data at K = 7 (Fig. 2). The estimated likelihood distribution increased from K = 1 to K = 7, and then started to decrease. This implied that seven was the smallest value of K, which was the most likely number of inferred populations in our data set. Interestingly, the value of K at which the likelihood distribution reached its maximum coincided with a further peak value of the ΔK statistic at K = 7, suggesting a more complex hierarchical structure of the T. erytreae populations (Fig. 2). In consequence, we plotted the clustering results for K = 2, K = 3 and K = 7 (Fig. 3). Furthermore, we considered an initial structure of two populations (K = 2) as was suggested by the method of ΔK36 whereby most of the analysed individuals were classified with high probability (Q  > 0.90) in two clusters (Fig. 3). Cluster 1 (in green) was exclusively formed by individuals from newly invaded areas in Spain and Portugal, including those from the archipelagos of Madeira and the Canary Islands. On the other hand, Cluster 2 (in beige) was mainly comprised of individuals from Africa, but also included individuals from Camacha (Madeira). The exception to this pattern involved three locations in Madeira (Quebradas, Camacha and Moreno), Pretoria (South Africa), and Homa Bay (Kenya), where almost all individuals consistently had significant membership in both clusters. Looking at K = 3 plot, the Bayesian clustering analysis resolved Cluster 1 into two by reassigning some individuals to Cluster 3 (in purple). Almost of all individuals from Moreno, Poiso, and Farrobo (in Madeira and Porto Santo, respectively) were entirely reassigned to Cluster 3 along with several individuals from the Canary Islands and Galicia (Spain). In addition, individuals from Vairão (Porto) and São Vicente de Pereira Jusã (Aveiro) (both in the northwest coast of Portugal) were also assigned to Cluster 3, while those individuals sampled from southern locations up to Sobreda (Setúbal) were assigned to Cluster 1. The exceptions to this pattern were the individuals from Ribamar (Ericeira), which were assigned to Cluster 3. Most notably, samples from Kenya were genetically different from those of South Africa and grouped in Cluster 1. At K = 7 the population structure scenario was more hierarchical, but 73% of all individuals (108 out from 147) could be assigned to one of the seven clusters with more than 90% probability (Q  > 0.9). The assignment of half of the remaining individuals (21 out of 39) could be done with more than 70% probability (Q  > 0.7). Among the different groups, Cluster 1 (in green) and 2 (in beige) are restricted to the populations of South Africa and Kenya, respectively, with almost no presence of individuals from any of the newly invaded areas. Clusters 3 (in purple) and 4 (in pink) are mostly exclusive to the individuals from Madeira and Portugal mainland, although with some membership in the Canary Islands and Galicia. Cluster 5 (in light blue) and Cluster 6 (in orange) are represented by individuals from Madeira, the Canary Islands and Galicia, while the individuals from Camacha (Madeira) –the only ones that were collected from Casimiroa edulis La Llave & Lex. (Rutacea: Toddalioideae)—form exclusively Cluster 7 (in dark blue). Remarkably, Q fractions corresponding to Cluster 7 are present in the individuals from Nelspruit, Tzaneen, and some in Pretoria.Figure 2Inference of the number of unique genetic clusters (K) from structure simulations derived from ten SSR markers. Diagrams of posterior probability of SSR data were obtained according to the methods of Evanno et al36 and Pritchard et al37. The likelihood of data given K (ln Pr(X|K), in open circles) and ΔK (the standardised second order rate of change of the likelihood function with respect to K, in bold circles) are plotted as functions of K. Error bars of the ln Pr(X|K) indicate standard deviations, but they are too small to be seen in the plot.Full size imageFigure 3Bayesian clustering analysis of individuals genotyped with ten SSR markers in 23 populations of T. erytreae sampled in Africa, Spain, and Portugal. The assignment of individuals to genetic clusters inferred from STRUCTURE37 simulations are based on average membership coefficient (Q). Estimated membership fractions for each individual and population are shown for K = 2, 3 and 7. Selection of the number of clusters was based both on the K value at which the likelihood distribution began to decrease and the peak values of ΔK. Each individual is represented by a single vertical bar, with the colouring of each bar represents the stacked proportion of assignment probabilities to each genetic cluster. For K = 7, clusters 1, 2, 3, 4, 5, 6 and 7 are shown in green, beige, purple, pink, light blue, orange, and dark blue, respectively. Black vertical lines separate sample sites. Labels identify T. erytreae populations from old invaded areas in Africa, and newly invaded areas in the Iberian Peninsula and the Macaronesia.Full size imageGenetic diversity analysis using T. erytreae SSR allelic dataThe genetic diversity of T. erytreae populations was also assessed by means of a distance-based clustering method. The scored SSR allelic data obtained from the ten SSR loci developed in this study were used to calculate a genetic dissimilarity matrix and to compute a Neighbor Joining (NJ) tree. A preliminary dendogram constructed using only the African populations of T. erytreae showed that the individuals from South Africa grouped together into a single cluster clearly separated from the Kenyan population. The robustness of the tree clustering was supported by the high bootstrap values obtained for nearly all branches (Fig. 4). To confirm the results obtained from the structure analysis a NJ tree under topological constraints was inferred using as initial tree the population structure of individuals from all the sampled areas with Q  > 0.7. The remaining individuals were positioned (constraint) on that previous topology. Inspection of the constrained tree topology revealed seven clusters that were in congruence with the structural population at K = 7 suggested by the STRUCTURE analysis (Fig. 5). It is noteworthy that Cluster 7 emerged as a paraphyletic group in the base of African Cluster 2. The cluster assignments of individuals with low membership coefficients (Q  0.7 according to STRUCTURE37 was used as initial tree, and the remaining individuals were positioned (constraint) on this previous topology. Spain: Aldán (A), Areeiro (AR), Gran Canaria (GC), Los Rodeos (LR), Oratava (O), Portonovo (PN), Tacoronte (T). Portugal: Areeiro-Lisbon (AR-Lis), Barreiralva (B), Camacha (C), Farrobo (F), Moreno (M), Paião (P), Poiso (PO), Quebradas (Q), Ribamar (R), Sobreda (S), São Vicente de Pereira Jusã (SV), Vairão (V). South Africa: Nelspruit (N), Pretoria (PR), Tzaneen (TZ). Kenya: Homa Bay (HB). Genetic clusters for K = 7 are indicated. Admixed individuals with Q  More

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    Body and wing size, but not wing shape, vary along a large-scale latitudinal gradient in a damselfly

    1.Brown, J. H., Stevens, G. C. & Kaufman, D. M. The geographic range: Size, shape, boundaries, and internal structure. Annu. Rev. Ecol. Syst. 27, 597–623 (1996).Article 

    Google Scholar 
    2.Gaston, K. J. Geographic range limits: Achieving synthesis. Proc. R. Soc. B 276, 1395–1406 (2009).PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    3.Parmesan, C. Ecological and evolutionary responses to recent climate change. Annu. Rev. Ecol. Evol. Syst. 37, 637–669 (2006).Article 

    Google Scholar 
    4.Sexton, J. P., McIntyre, P. J., Angert, A. L. & Rice, K. J. Evolution and ecology of species range limits. Annu. Rev. Ecol. Evol. Syst. 40, 415–436 (2009).Article 

    Google Scholar 
    5.Cahill, A. E. et al. Causes of warm-edge range limits: Systematic review, proximate factors and implications for climate change. J. Biogeogr. 41, 429–442 (2014).Article 

    Google Scholar 
    6.Bridle, J. R. & Vines, T. H. Limits to evolution at range margins: When and why does adaptation fail?. Trends Ecol. Evol. 22, 140–147 (2007).PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    7.Chuang, A. & Peterson, C. R. Expanding population edges: Theories, traits, and trade-offs. Glob. Change Biol. 22, 494–512 (2016).ADS 
    Article 

    Google Scholar 
    8.Kubisch, A., Holt, R. D., Poethke, H. J. & Fronhofer, E. A. Where am I and why? Synthesizing range biology and the eco-evolutionary dynamics of dispersal. Oikos 123, 5–22 (2014).Article 

    Google Scholar 
    9.Shine, R., Brown, G. P. & Phillips, B. L. An evolutionary process that assembles phenotypes through space rather than through time. PNAS 108, 5708–5711 (2011).ADS 
    CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    10.Wolz, M. et al. Dispersal and life-history traits in a spider with rapid range expansion. Mov. Ecol. 8, 1–11 (2020).Article 

    Google Scholar 
    11.Hill, J. K., Griffiths, H. M. & Thomas, C. D. Climate change and evolutionary adaptations at species’ range margins. Annu. Rev. Entomol. 56(56), 143–159 (2011).CAS 
    PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    12.Kaluthota, C., Brinkman, B. E., Dos Santos, E. B. & Rendall, D. Transcontinental latitudinal variation in song performance and complexity in house Wrens (Troglodytes aedon). Proc. R. Soc. B 283, 1–8 (2016).Article 
    CAS 

    Google Scholar 
    13.Golab, M. J., Johansson, F. & Sniegula, S. Let’s mate here and now—seasonal constraints increase mating efficiency. Ecol. Entomol. 44, 623–629 (2019).Article 

    Google Scholar 
    14.Monteiro, N. et al. Parabolic variation in sexual selection intensity across the range of a cold-water pipefish: Implications for susceptibility to climate change. Glob. Change Biol. 23, 3600–3609 (2017).ADS 
    Article 

    Google Scholar 
    15.Hughes, C. L., Hill, J. K. & Dytham, C. Evolutionary trade-offs between reproduction and dispersal in populations at expanding range boundaries. Proc. R. Soc. B 270, S147–S150 (2003).PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    16.Dudaniec, R. Y. et al. Latitudinal clines in sexual selection, sexual size dimorphism, and sex‐specific genetic dispersal during a poleward range expansion. J. Anim. Ecol. https://doi.org/10.1111/1365-2656.13488 (2021).Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    17.De Lisle, S. P., Goedert, D., Reedy, A. M. & Svensson, E. I. Climatic factors and species range position predict sexually antagonistic selection across taxa. Philos. Trans. R. Soc. B 373, 20170415 (2018).Article 

    Google Scholar 
    18.Holt, R. D. & Keitt, T. H. Species’ borders: A unifying theme in ecology. Oikos 108, 3–6 (2005).Article 

    Google Scholar 
    19.Parmesan, C. & Yohe, G. A globally coherent fingerprint of climate change impacts across natural systems. Nature 421, 37–42 (2003).ADS 
    CAS 
    PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    20.Norberg, U. M. & Rayner, J. M. V. Ecological morphology and flight in bats (Mammalia; Chiroptera): Wing adaptations, flight performance, foraging strategy and echolocation. Philos. Trans. R. Soc. Lond. B 316, 335–427 (1987).ADS 
    Article 

    Google Scholar 
    21.Bowlin, M. S. & Wikelski, M. Pointed wings, low wingloading and calm air reduce migratory flight costs in songbirds. PLoS One 3, e2154 (2008).ADS 
    PubMed 
    PubMed Central 
    Article 
    CAS 

    Google Scholar 
    22.DeVries, P. J., Penz, C. M. & Hill, R. I. Vertical distribution, flight behaviour and evolution of wing morphology in Morpho butterflies. J. Anim. Ecol. 79, 1077–1085 (2010).CAS 
    PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    23.Le Roy, C., Debat, V. & Llaurens, V. Adaptive evolution of butterfly wing shape: From morphology to behaviour. Biol. Rev. 94, 1261–1281 (2019).PubMed 
    PubMed Central 

    Google Scholar 
    24.Cassel-Lundhagen, A., Tammaru, T., Windig, J. J., Ryrholm, N. & Nylin, S. Are peripheral populations special? Congruent patterns in two butterfly species. Ecography 32, 591–600 (2009).Article 

    Google Scholar 
    25.Taylor-Cox, E. D. et al. Wing morphological responses to latitude and colonisation in a range expanding butterfly. PeerJ 8, e10352 (2020).PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    26.Hassall, C., Thompson, D. J. & Harvey, I. F. Variation in morphology between core and marginal populations of three British damselflies. Aquat. Insect. 31, 187–197 (2009).Article 

    Google Scholar 
    27.Therry, L., Zawal, A., Bonte, D. & Stoks, R. What factors shape female phenotypes of a poleward-moving damselfly at the edge of its range?. Biol. J. Linn. Soc. 112, 556–568 (2014).Article 

    Google Scholar 
    28.Johansson, F. Latitudinal shifts in body size of Enallagma cyathigerum (Odonata). J. Biogeogr. 30, 29–34 (2003).Article 

    Google Scholar 
    29.Swaegers, J. et al. Ecological and evolutionary drivers of range size in Coenagrion damselflies. J. Evol. Biol. 27, 2386–2395 (2014).CAS 
    PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    30.Hickling, R., Roy, D. B., Hill, J. K. & Thomas, C. D. A northward shift of range margins in British Odonata. Glob. Change Biol. 11, 502–506 (2005).ADS 
    Article 

    Google Scholar 
    31.Termaat, T. et al. Distribution trends of European dragonflies under climate change. Divers. Distrib. 25, 936–950 (2019).Article 

    Google Scholar 
    32.Outomuro, D. et al. Antagonistic natural and sexual selection on wing shape in a scrambling damselfly. Evolution 70, 1582–1595 (2016).PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    33.Arambourou, H., Sanmartín-Villar, I. & Stoks, R. Wing shape-mediated carry-over effects of a heat wave during the larval stage on post-metamorphic locomotor ability. Oecologia 184, 279–291 (2017).ADS 
    PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    34.Therry, L., Nilsson-Örtman, V., Bonte, D. & Stoks, R. Rapid evolution of larval life history, adult immune function and flight muscles in a poleward-moving damselfly. J. Evol. Biol. 27, 141–152 (2014).CAS 
    PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    35.Dijkstra, K.-D.B. & Schröter, A. Field Guide to the Dragonflies of Britain and Europe 2nd edn. (Bloomsbury Wildlife, 2020).
    Google Scholar 
    36.Corbet, P. S., Suhling, F. & Soendgerath, D. Voltinism of odonata: A review. Int. J. Odonatol. 9, 1–44 (2006).Article 

    Google Scholar 
    37.Sniegula, S., Golab, M. J. & Johansson, F. A large-scale latitudinal pattern of life-history traits in a strictly univoltine damselfly. Ecol. Entomol. 41, 459–472 (2016).Article 

    Google Scholar 
    38.Stoks, R. Components of lifetime mating success and body size in males of a scrambling damselfly. Anim. Behav. 59, 339–348 (2000).CAS 
    PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    39.Sniegula, S., Prus, M. A., Golab, M. J. & Outomuro, D. Do males with higher mating success invest more in armaments? An across-populations study in damselflies. Ecol. Entomol. 42, 526–530 (2017).Article 

    Google Scholar 
    40.Jenkins, D. G. et al. Does size matter for dispersal distance?. Glob. Ecol. Biogeogr. 16, 415–425 (2007).Article 

    Google Scholar 
    41.Fairbairn, D. J., Blanckenhorn, W. U. & Székely, T. Sex, Size & Gender Roles (Oxford University Press, 2007).Book 

    Google Scholar 
    42.Sekar, S. A meta-analysis of the traits affecting dispersal ability in butterflies: Can wingspan be used as a proxy?. J. Anim. Ecol. 81, 174–184 (2012).PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    43.Malmqvist, B. How does wing length relate to distribution patterns of stoneflies (Plecoptera) and mayflies (Ephemeroptera)?. Biol. Conserv. 93, 271–276 (2000).Article 

    Google Scholar 
    44.Lancaster, J. & Downes, B. J. Dispersal traits may reflect dispersal distances, but dispersers may not connect populations demographically. Oecologia 184, 171–182 (2017).ADS 
    PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    45.Rundle, S. D., Bilton, D. T. & Foggo, A. By wind, wings or water: Body size, dispersal and range size in aquatic invertebrates. In Body Size: The Structure and Function of Aquatic Ecosystems (eds. Hildrew, A. G., Raffaelli, D. G. & Edmonds-Brown, R.) 186–209 (Cambridge University Press, 2007).46.Wootton, R. J. The functional morphology of the wings of Odonata. Adv. Odonatol. 5, 153–169 (1991).
    Google Scholar 
    47.Dudley, R. The Biomechanics of Insect Flight. Form, Function, Evolution (Princeton University Press, 2000).Book 

    Google Scholar 
    48.Roff, D. Optimizing development time in a seasonal environment—The ups and downs of clinal variation. Oecologia 45, 202–208 (1980).ADS 
    PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    49.Dmitriew, C. M. The evolution of growth trajectories: What limits growth rate?. Biol. Rev. 86, 97–116 (2011).PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    50.Utzeri, C., Carchini, G., Falchetti, E. & Belfiore, C. Philopatry, homing and dispersal in Lestes barbarus (Fabricius) (Zygoptera: Lestidae). Odonatologica 13, 573–584 (1984).
    Google Scholar 
    51.Wang, X. & Clarke, J. A. The evolution of avian wing shape and previously unrecognized trends in covert feathering. Proc. R. Soc. B 282, 20151935 (2015).PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    52.Johansson, F., Söderquist, M. & Bokma, F. Insect wing shape evolution: Independent effects of migratory and mate guarding flight on dragonfly wings. Biol. J. Linn. Soc. 97, 362–372 (2009).Article 

    Google Scholar 
    53.Outomuro, D., Adams, D. C. & Johansson, F. Wing shape allometry and aerodynamics in calopterygid damselflies: A comparative approach. BMC Evol. Biol. 13, 118 (2013).PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    54.Berwaerts, K., Van Dyck, H. & Aerts, P. Does flight morphology relate to flight performance? An experimental test with the butterfly Pararge aegeria. Funct. Ecol. 16, 484–491 (2002).Article 

    Google Scholar 
    55.Jantzen, B. & Eisner, T. Hindwings are unnecessary for flight but essential for execution of normal evasive flight in Lepidoptera. PNAS 105, 16636–16640 (2008).ADS 
    CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    56.Kalkman, V. J. Lestes sponsa. The IUCN Red List of Threatened Species 2014: e.T165475A19165578. https://doi.org/10.2305/IUCN.UK.2014-1.RLTS.T165475A19165578.en. Downloaded on 20 April 2021. (2014).57.Corbet, P. S. Dragonflies. Behaviour and Ecology of ODONATA (Cornell University Press, 1999).
    Google Scholar 
    58.Córdoba-Aguilar, A., López-Valenzuela, A. & Brunel, O. Allometry in damselfly ornamental and genital traits: Solving some pitfalls of allometry and sexual selection. Genetica 138, 1141–1146 (2010).PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    59.Śniegula, S., Drobniak, S. M., GołaB, M. J. & Johansson, F. Photoperiod and variation in life history traits in core and peripheral populations in the damselfly Lestes sponsa. Ecol. Entomol. 39, 137–148 (2014).Article 

    Google Scholar 
    60.Rohlf, F. J. tpsDig2 version 2.19. (Accessed 1 September 2021); https://sbmorphometrics.org (2015).61.Rohlf, F. J. & Slice, D. Extension of the Procrustes method for the optimal superimposition of landmarks. Syst. Zool. 39, 40–59 (1990).Article 

    Google Scholar 
    62.Rohlf, F. J. tpsRelw. Relative warps version 1.49. (Accessed 1 September 2021); https://sbmorphometrics.org (2010).63.Adams, D. C., Collyer, M. L., Kaliontzopoulou, A. & Balken, E. Geomorph: Software for geometric morphometric analyses. R package version 3.3.2. (Accessed 1 September 2021); https://sbmorphometrics.org (2021).64.Bartoń, K. MuMIn: Multi-Model Inference. R package version 1.43.17 (Accessed 1 September 2021); https://sbmorphometrics.org (2020).65.Collyer, M. L., Sekora, D. J. & Adams, D. C. A method for analysis of phenotypic change for phenotypes described by high-dimensional data. Heredity 115, 357–365 (2015).CAS 
    PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    66.Adams, D. C. & Collyer, M. L. On the comparison of the strength of morphological integration across morphometric datasets. Evolution 70, 2623–2631 (2016).PubMed 
    Article 
    PubMed Central 

    Google Scholar  More

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    A Tunguska sized airburst destroyed Tall el-Hammam a Middle Bronze Age city in the Jordan Valley near the Dead Sea

    Melted quartz grainsCrystalline quartz melts between 1670 °C (tridymite) and 1713 °C (cristobalite), and because quartz is pervasive and easily identified, melted grains serve as an important temperature indicator. At TeH, we observed that unmelted potsherds displayed no melted quartz grains, indicating exposure to low temperatures. On the other hand, most quartz grains on the surfaces of pottery, mudbricks, and roofing clay exhibited some degree of melting, and unmelted quartz grains were rare. Nearly all quartz grains found on broken, unmelted surfaces of potsherds were also unmelted. On melted pottery and mudbricks, melted quartz has an estimated density of 1 grain per 5 mm2.Melted quartz grains at TeH exhibit a wide range of morphologies. Some show evidence of partial melting that only melted grain edges and not the rest of the grain (Figs. 22, 23). Others displayed nearly complete melting with diffusion into the melted Ca–Al–Si matrix of pottery or mudbrick (Fig. 22). Melted quartz grains commonly exhibit vesiculation caused by outgassing (Figs. 22, 23), suggesting that those grains rose above quartz’s melting point of ~ 1713 °C.Figure 22SEM images of melted quartz grains on melted potsherd from the palace. (a) Highly melted quartz grain from the upper surface of melted pottery; shows flow lines of molten quartz in darker ‘neck’ at upper right; (b) manually constructed EDS-based phase map showing 100% quartz grain (green) embedded in Ca–Al–Si matrix of melted pottery (red); blue marks mixing zone between SiO2 and matrix at approximately  > 1713 °C, the melting point of quartz. Yellow arrow points to area depleted in oxygen, indicating high-temperature transformation to elemental Si mixed with melted SiO2. (c) Highly melted quartz grain; (d) manually constructed EDS-based phase map showing diffusion/mixing zone in blue with arrow pointing to bubble, indicating outgassing as grain reached temperatures above its melting point. (e) Quartz grain that has almost completely melted; (f) manually constructed EDS-based phase map showing the small remnant of a melted quartz grain (green) with a wide mixing zone (blue).Full size imageFigure 23SEM images of melted quartz grains on melted mudbrick from the palace. (a) Highly melted quartz grain; (b) manually constructed EDS-based phase map indicates center is pure SiO2 surrounded by melted mudbrick. Arrow points to vesicles indicating outgassing as grain temperature rose above ~ 1713 °C, the melting point of quartz. (c) The surface of a flattened quartz grain showing flow marks toward the upper right. High temperatures are required to lower the viscosity sufficiently for quartz to flow. (d) Manually constructed EDS-based phase map with an arrow pointing to vesicles indicating outgassing at high temperatures. (e) Close up of grain in panel ‘c’ showing flow marks (schlieren) at arrows. (f) Shattered, melted quartz splattered onto mudbrick meltglass; (g) manually constructed EDS-based phase map indicating that the blue area is SiO2; the yellow area is a shattered, thermally altered Fe-oxide grain.Full size imageAn SEM–EDS elemental map of one melted grain showed that the quartz had begun to dissociate into elemental Si (Fig. 22b). Another grain (Fig. 23c–e) displayed flow marks consistent with exposure to temperatures above 1713 °C where the viscosity of quartz falls low enough for it to flow easily. Another SEM–EDS analysis confirmed that one agglutinated mass of material is 100 wt.% SiO2 (Fig. 23f, g), suggesting that this polycrystalline quartz grain shattered, melted, and partially fused again.Discussion of melted quartzMoore et al.17 reported that during heating experiments, many quartz grains  50-µm-wide remained visually unaltered up to ~ 1700 °C. By 1850 °C, all quartz grains fully melted. These experiments establish a particle-size dependency and confirm confirmed the melting point for  > 50-µm-wide TeH quartz grains between ~ 1700–1850 °C. Melted  > 50-µm-wide quartz grains on the surfaces of melted pottery and mudbrick from the TeH destruction layer indicate exposure to these unusually high temperatures  > 1700 °C.Previously, Thy et al.70 proposed that glass at Abu Hureyra did not form during a cosmic impact, but rather, formed in biomass slag that resulted from thatched hut fires. However, Thy et al. did not determine whether or not high-temperature grains existed in the biomass slag. To test that claim, Moore et al.17 analyzed biomass slag from Africa and found only low-temperature melted grains with melting points of ~ 1200 °C, consistent with a temperature range for biomass slag of 1155–1290 °C, as reported by Thy et al.71. Upon testing the purported impact glass from Abu Hureyra, Moore et al.17 discovered high-temperature mineral grains that melt in the range of 1713° to  > 2000 °C, as are also found in TeH glass. These test results suggest that the melted glass from Abu Hureyra must have been exposed to higher temperatures than those associated with fires in thatched huts. Because of the presence of high-temperature minerals at TeH, we conclude that, as at Abu Hureyra, the meltglass could not have formed simply by burning thatched huts or wood-roofed, mudbrick buildings.Melted Fe- and Si-rich spherulesThe presence of melted spherulitic objects (“spherules”) has commonly been used to help identify and investigate high-temperature airburst/impact events in the sedimentary record. Although these objects are referred to here as “spherules,” they display a wide range of other impact-related morphologies that include rounded, sub-rounded, ovate, oblate, elongated, teardrop, dumbbell, and/or broken forms17,72,73,74,75,76,77,78,79,80,81,82. Optical microscopy and SEM–EDS are commonly used to identify and analyze spherules and the processes by which they are formed. Care is needed to conclusively distinguish high-temperature spherules produced by cosmic impacts from other superficially similar forms. Other such objects that frequently occur in sediments include anthropogenic spherules (typically from modern coal-fired power plants), authigenic framboids (Supporting Information, Fig. S7), rounded detrital magnetite, and volcanic spherules.Spherules in TeH sediment were investigated from stratigraphic sequences that include the MB II destruction layer at four locations: palace, temple, ring road, and wadi (Fig. 24). For the palace (Field UA, Square 7GG), the sequence spanned 28 cm with 5 contiguous samples of sediment ranging from 3-cm thick for the MB II destruction layer to 13-cm thick for some outlying samples. In the palace, 310 spherules/kg (Fig. 24d) were observed in the destruction layer with none found in samples above and below this layer. For the temple (Field LS, Square 42J), 5 continuous samples spanned 43 cm and ranged in thickness from 6 to 16 cm; the MB II layer contained ~ 2345 Fe- and Si-rich spherules/kg with 782/kg in the sample immediately below and none at other levels (Fig. 24c). Six contiguous samples from the ring road (Field LA, Square 28 M) spanned 30 cm with all 5 cm thick; the MB II destruction layer at this location contains 2150 spherules/kg with none detected in younger or older samples (Fig. 24b). Five discontinuous samples from the wadi spanned 170 cm, ranging from 10-cm thick for the destruction layer up to 20-cm thick for other samples; the MB II destruction layer at this location contained 2780 spherules/kg with none in samples from other levels (Fig. 24a, Supporting Information, Table S3). Notably, when melted mudbrick from the ring road was being mounted for SEM analysis, numerous loose spherules were observed within vesicles of the sample, confirming a close association between the spherules and meltglass. At all four locations, the peaks in high-temperature spherule abundances occur in the MB II destruction layer dating to ~ 1650 BCE.Figure 24Spherule abundances. (a)–(d). Number per kg for Fe- and S-rich spherules from 4 locations. Depths are in cm above or below the bottom of the destruction layer.Full size imageSEM images of spherules are shown in Figs. 25, 26, 27 and 28, and compositions are listed in Supporting Information, Table S4. The average spherule diameter was 40.5 µm with a range of 7 to 72 µm. The dominant minerals were Fe oxides averaging 40.2 wt.%, with a range of up to 84.1 wt.%; elemental Fe with a range of up to 80.3 wt.%; SiO2 averaging 20.9 wt.%, ranging from 1.0 to 45.2 wt.%; Al2O3 averaging 7.8 wt.% with a range of up to 15.6 wt.%; and TiO2 averaging 7.1 wt.% with a range of up to 53.1 wt.%. Fourteen spherules had compositions  > 48 wt.% of oxidized Fe, elemental Fe, and TiO2; five spherules contained  75 wt.% Fe with no Ti. Eight of 23 spherules analyzed contained detectable levels of Ti at up to 53.1 wt.%.Figure 25SEM images of mostly silica-rich spherules from TeH. (a)–(d) Representative spherules from the ring road on the lower tall. SEM images of iron-rich spherules. (e)–(f) Fe-rich spherules from the temple complex. (g) temple spherule containing ~ 3.7 wt.% Cr. (h) Broken, vesicular spherule from temple containing 1.4 wt.% Ni and 3.7 wt.% Cr. SEM images of titanium-rich spherules. Ti content of these ranges from 18.9 to 1.2 wt.%, averaging 10.7 wt.%. (i)–(k) Spherules from the ring road. (l) Spherule from the wadi site.Full size imageFigure 26SEM image of rare-earth (REE) spherule. (a) REE-rich 72-µm-wide spherule from the palace, dominantly composed of Fe, La, Ce, and O. (b) Close up of REE blebs found on the spherule. (c)–(f) SEM–EDS elemental maps showing composition. La = 15.6 wt.% and Ce = 21.0 wt.%. Ce is enriched over Fe and La in the middle part of the spherule, as seen in panels ‘d’ through ‘f’.Full size imageFigure 27SEM images of a spherule mainly composed of Fe and Si. (a) Fe–Ti-rich 54-µm-wide spherule from the palace. Spherule displays a protrusion to the left, suggesting aerodynamic shaping when molten, after which the tail detached. (b) A focused ion beam (FIB) was used to section the spherule, revealing inclusions of wassonite or titanium sulfide (TiS; yellow arrows) that are lighter-colored than the matrix. (c)–(f) Color-coded SEM–EDS elemental maps, showing the distribution of Ti, S, Si, and Fe and the location of the TiS grains. The spherule is dominantly composed of Fe and Si with minor amounts of Ti and S found in TiS inclusions.Full size imageFigure 28Fe-rich spherules embedded in meltglass. (a) Optical photomicrograph of a 167-µm-wide piece of meltglass with embedded Fe-rich spherules. (b) SEM image of same grain as in panel ‘a’. Melted quartz grain (Qtz) is embedded in Ca–Al–Si-rich matrix, which has the same composition as melted mudbrick. (c) SEM close-up image of the boxed area and panel ‘b’, showing splattered Fe-rich spherule.Full size imageTwo unusual spherules from the palace contain anomalously high percentages of rare-earth elements (REEs) at  > 37 wt.% of combined lanthanum (La), and cerium (Ce) (Fig. 26), as determined by preliminary measurements using SEM–EDS. Minor oxides account for the rest of the spherules’ bulk composition (Table S1).One 54-µm-wide sectioned spherule contains titanium sulfide (TiS) with a melting point of ~ 1780° C. TiS, known as wassonite, was first identified in meteorites (Fig. 27) and has been reported in impact-related material17,81,83. However, TiS sometimes occurs as an exsolution product forming fine networks in magnetite and ilmenite and can be of terrestrial origin.One unusual piece of 167-µm-wide Ca–Al–Si meltglass contains nearly two dozen iron oxide spherules on its surface (Fig. 28). The meltglass contains a completely melted quartz grain as part of the matrix (Fig. 28b). Most of the spherules appear to have been flattened or crushed by collision with the meltglass while they were still partially molten (Fig. 28c).Discussion of spherules and meltglassMelted materials from non-impact-related combustion have been reported in multiple studies. Consequently, we investigated whether Ca-, Fe-, and Si-rich spherules and meltglass (mudbrick, pottery, plaster, and roofing clay) may have formed normally, rather than from a cosmic impact event. For example, (i) glassy spherules and meltglass are known to form when carbon-rich biomass smolders below ground at ~ 1000° to 1300 °C, such as in midden mounds71. They also form in buried peat deposits84, underground coal seams85, burned haystacks86, and in large bonfires, such as at the Native American site at Cahokia, Illinois, in the USA87. (ii) Also, ancient fortifications (hillforts) in Scotland and Sweden, dating from ~ 1000 BCE to 1400 AD, have artificially vitrified walls that melted at temperatures of ~ 850° to 1000 °C88. (iii) Partially vitrified pottery and meltglass derived from the melting of wattle and daub (thatch and clay) with estimated temperatures of ~ 1000 °C have been reported in burned houses of the Trypillia culture in Ukraine89,90. (iv) Vitrified mudbricks and pottery that melted at 17 investigated biomass glass from midden mounds in Africa and found no high-temperature minerals. For this contribution, we used SEM–EDS to examine aluminosilicate meltglass from an underground peat fire in South Carolina, USA; meltglass in coal-fired fly ash from New Jersey, USA; and mining slag from a copper mine in Arizona, USA. All these meltglass examples display unmelted quartz and contain no other high-temperature melted grains, consistent with low-temperature melting at  97% wt.% FeO, as are found at TeH. Nor can these low temperatures produce meltglass and spherules embedded with melted zircon (melting point = 1687 °C), chromite (2190 °C), quartz (1713 °C), platinum (1768 °C), and iridium (2466 °C). Moore et al.17 confirmed that the melting of these high-temperature minerals requires minimum temperatures of ~ 1500° to 2500 °C.This evidence demonstrates that although the matrix of the spherules and meltglass at TeH likely experienced incipient melting at temperatures lower than ~ 1300 °C, this value represents only the minimum temperature of exposure, because the high-temperature minerals embedded in them do not melt at such low temperatures. Instead, the spherules and meltglass at TeH must have reached temperatures greater than ~ 1300 °C, most likely involving brief exposure to ambient temperatures of ~ 2500 °C, the melting point of iridium. These temperatures far exceed those characteristic of city fires and other types of biomass burning. In summary, all of this evidence is consistent with very high temperatures known during cosmic impacts but inconsistent with other known natural causes.Calcium carbonate spherules and plasterIn sediments of the destruction layer, we observed amber-to-off-white-colored spherules (Fig. 29) at high concentrations of ~ 240,000/kg in the palace, ~ 420/kg in the temple, ~ 60/kg on the ring road, and ~ 910/kg in the wadi (Supporting information, Table S2). In all four profiles, the spherules peak in the destruction layer with few to none above or below. Peak abundances of calcium carbonate spherules are closely associated with peak abundances of plaster fragments, which are the same color. By far the most spherules (~ 250× more) occurred in the destruction layer of the palace, where excavations showed that nearly every room and ceiling was surfaced with off-white lime-based plaster. Excavators uncovered high-quality lime plaster fragments still adhering to mudbricks inside the MB II palace complex, and in one palace room, we uncovered fragments of melted plaster (Fig. 29e). In contrast, lime plaster was very rarely used in buildings on the lower tall, including those near the temple.Figure 29Images of calcium carbonate spherules and melted plaster from TeH. (a) Photomicrographs of translucent, amber-colored CaCO3 spherules from the destruction layer in the palace. (b) SEM image of 83-µm carbonate spherule with impact or outgassing crater at arrow. (c) Photomicrograph of ~ 2-mm-wide piece of partially melted palace plaster from oxygen/propylene torch test, showing incipient melting at 1500 °C. Arrows point to hemispheric droplets emerging as spherules. (d) 142-µm cluster of 8 carbonate spherules with apparent impact or outgassing crater at arrow. (e) 64 × 30 mm piece of melted plaster that broke off the palace wall and became melted. It is composed only of calcium, carbon, and oxygen.Full size imageTo explore a potential connection between plaster and spherules, we performed SEM–EDS on samples of the palace plaster. Comparison of SEM–EDS analyses shows that the plaster composition has a  > 96% similarity to the spherule composition: CaCO3 = 71.4 wt.% in plaster versus 68.7 wt.% in the spherules; elemental C = 23.6 versus 26.3 wt.%; SiO2 = 2.4 versus 1.8 wt.%; MgO = 1.7 versus 2.0 wt.%; and SO3 = 0.94 versus 1.2 wt.%. The high carbon percentage and low sulfur content indicate that the plaster was made from calcium carbonate and not gypsum (CaSO4·2H2O). SEM imaging revealed that the plaster contains small plant parts, commonly used in plaster as a binder, and is likely the source of the high abundance of elemental C in the plaster. Inspection showed no evidence of microfossils, such as coccoliths, brachiopods, and foraminifera. The morphology of the spherules indicates that they are not authigenic or biological in origin.Discussion of carbonate plaster and spherulesOne of the earliest known uses of CaCO3-based plaster was in ~ 6750 BCE at Ayn Ghazal, ~ 35 km from TeH in modern-day Amman, Jordan97. At that site, multi-purpose lime plaster was used to make statues and figurines and to coat the interior walls of buildings. Because the production of lime-based plaster occurred at least 3000 years before TeH was destroyed, the inhabitants of TeH undoubtedly were familiar with the process. Typically, lime powder was produced in ancient times by stacking wood/combustibles interspersed with limestone rocks and then setting the stack on fire. Temperatures of ~ 800–1100 °C were required to transform the rocks into crumbly chalk, which was then mixed with water to make hydrated lime and plastered onto mudbrick walls97.At TeH, fragments of CaCO3-based plaster are intermixed in covarying abundances with CaCO3-based spherules with both compositions matching to within 96%. This similarity suggests that the carbonate spherules are derived from the plaster. We infer that the high-temperature blast wave from the impact event stripped some plaster from the interior walls of the palace and melted some into spherules. However, it is difficult to directly melt CaCO3, which gives off CO2 at high temperatures and decomposes into lime powder. We investigated this cycle in a heating experiment with an oxygen/propylene torch and found that we could decompose the plaster at ~ 1500 °C, the upper limit of the heating test, and begin incipient melting of the plaster. The heated plaster produced emergent droplets at that temperature but did not transform into free spherules (Supporting Information, Text S2).Similar spherules have been reported from Meteor Crater, where spherules up to ~ 200 μm in diameter are composed entirely of CaCO3 formed from a cosmic impact into limestone98,99. One of several possible hypotheses for TeH is that during the impact event, the limestone plaster converted to CaO with an equilibrium melting point of 2572 °C. However, it is highly likely that airborne contaminants, such as sodium and water vapor, reacted with the CaO and significantly lowered the melting point, allowing spherule formation at ≥ 1500 °C.The proposed chemical sequence of events of plaster formation and the later impact are as follows:

    1.

    Limestone was heated to ~ 800–1100 °C, decomposing to quicklime:

    $${text{CaCO}}_{{3}} to {text{ CaO }} + {text{ CO}}_{{2}}$$

    2.

    Quicklime was mixed with water to make a wet plaster:$${text{CaO }} + {text{ H}}_{{2}} {text{O }} to {text{ Ca}}left( {{text{OH}}} right)_{{2}}$$

    3.

    The plaster hardened and slowly absorbed CO2 to revert to CaCO3:$${text{Ca}}left( {{text{OH}}} right)_{{2}} + {text{ CO}}_{{2}} to {text{ H}}_{{2}} {text{O }} + {text{ CaCO}}_{{3}}$$

    4.

    The high-temperature impact event melted some plaster into spherules:$${text{CaCO}}_{{3}} to {text{ CaO }}left( {{text{spherules}}} right) , + {text{ CO}}_{{2}} left( { > {15}00^circ {text{C}}} right)$$

    5.

    CaO spherules slowly absorbed CO2 to revert to CaCO3:$${text{Ca }} + {text{ CO}}_{{2}} to {text{ CaCO}}_{{3}} left( {text{as spherules}} right)$$

    General discussion of all spherulesAccording to the previous investigations17,72,81,82, Fe-rich spherules such as those found at TeH typically melt at  > 1538 °C, the melting point of iron (Table 1). Because of the presence of magnetite (Fe3O4) in the REE spherule, its melting point is inferred to be  > 1590 °C (Table 1). The Si-rich spherules are similar in composition to TeH sediment and mudbrick, and thus, we propose that they were derived from the melting of these materials at  > 1250 °C. The carbonate-rich spherules likely formed at  > 1500 °C.Several studies describe a mechanism by which spherules could form during a low-altitude cosmic airburst100,101. When a bolide enters Earth’s atmosphere, it is subjected to immense aerodynamic drag and ablation, causing most of the object to fragment into a high-temperature fireball, after which its remaining mass is converted into a high-temperature vapor jet that continues at hypervelocity down to the Earth’s surface. Depending on the altitude of the bolide’s disruption, this jet is capable of excavating unconsolidated surficial sediments, melting them, and ejecting the molten material into the air as Si- and Fe-rich spherules and meltglass. This melted material typically contains a very low percentage (17.Melted zircons in pottery and mudbricks were observed (Fig. 30) at an estimated density of 1 grain per 20 mm2. On highly melted surfaces, nearly all zircons showed some degree of melting. In contrast, nearly all zircons found on broken interior surfaces were unmelted (Fig. 30d), except those within ~ 1 mm of melted surfaces. This implies that the temperature of the surrounding atmosphere was higher than the internal temperatures of the melting objects. Unmelted potsherds displayed only unmelted minerals.Figure 30SEM images of melted zircon grains. (a) Melted TeH zircon grain with bubbles at yellow arrow due to high-temperature dissociation and/or entrapped porosity. (b) Melted TeH zircon grain decorated with bubbles along the fracture line at upper arrow; arrows labeled “Bd” point to bright granular baddeleyite, ZrO2, formed during the high-temperature dissociation of zircon. (c) Almost fully melted TeH zircon grain mixing into the Ca–Al–Si matrix. (d) A typical unmelted zircon grain from TeH with straight, euhedral edges. Grain shows cracks on the top surface from possible thermal or mechanical damage. (e) For comparison, from cosmic airburst/impact at Dakhleh Oasis in Egypt: melted zircon decorated with lines of bubbles (arrow).Full size imageThe melted zircons in TeH materials exhibit a wide range of morphologies. Most showed evidence of sufficient melting to alter or destroy the original distinctive, euhedral shape of the grains. Also, the grains were often decorated with vesicles that were associated with fractures (Fig. 30a, c).Stoichiometric zircon contains 67.2 wt.% and 32.8 wt.% ZrO2 and SiO2 respectively, but in several TeH samples, we observed a reduction in the SiO2 concentration due to a loss of volatile SiO from the dissociation of SiO2. This alteration has been found to occur at 1676 °C, slightly below zircon’s melting point of 1687 °C103. This zircon dissociation leads to varying ZrO2:SiO2 ratios and to the formation of distinctive granular textures of pure ZrO2, also known as baddeleyite104 (Figs. 30, 31, 32). With increasing time at temperature, zircon will eventually convert partially or completely to ZrO2. Nearly all zircons observed on the surfaces of melted materials were either melted or showed some conversion to baddeleyite. We observed one zircon grain (Fig. 32d–e) displaying granular ZrO2 associated with three phases that span a wide range of SiO2 concentrations, likely formed at temperatures above 1687 °C. This extreme temperature and competing loss of SiO over an inferred duration of only several seconds led to complex microstructures, where grains melted, outgassed, and diffused into the surrounding matrix.Figure 31SEM images of other melted zircon grains in palace potsherd. (a) Two melted zircon grains adjacent to a previously discussed melted quartz grain; (b) close-up of same zircon grains; (c) manually constructed EDS-based phase map showing baddeleyite grains in green. The blue area represents melted zircon, while the red background represents the Ca–Al–Si matrix of the melted pottery. (d) Manually constructed EDS-based phase map of zircon grain showing small baddeleyite grains in green at the top.Full size imageFigure 32SEM images of melted zircon grains in mudbrick meltglass from the palace. (a) Thermally distorted zircon grain with a “hook” that resulted from the flow of molten material at  > 1687 °C; the darker area represents unrelated debris on top of zircon. (b) Manually constructed EDS-based phase map showing baddeleyite grains (Bd = ZrO2) in green, zircon in blue, and melted mudbrick in red. (c) Zircon grain showing limited thermal alteration, yet sufficient to cause dissociation into bright baddeleyite grains at ~ 1676 °C. (d) Zircon grain exhibiting three phases of thermal alteration, as shown in detail in (e), where a manually constructed EDS-based phase map demonstrates that high temperatures caused bubbling in the center band of zircon (purple = Hi) producing sub-micron-sized grains of baddeleyite (e.g., at arrow). Medium temperatures caused zircon to melt and flow (blue = Lo), and lower temperatures at the left end of grain produced thermal cracks (medium blue = Med). The green area marks the high-Si diffusion zone resulting from the dissociation of zircon. (f) Zircon grain from TeH has been fully converted to granular baddeleyite.Full size imageDiscussion of melted zirconZircon grains have a theoretical, equilibrium melting point of ~ 1687 °C. Under laboratory heating17, zircon grains showed no detectable alteration in shape at ~ 1300 °C but displayed incipient melting of grain edges and dissociation to baddeleyite beginning at ~ 1400 °C with increasing dissociation to 1500 °C17. Most zircon grains  120 µm were still recognizable but displayed considerable melting17. These experiments establish a lower melting range for TeH zircon grains of ~ 1400° to 1500 °C.Patterson105 showed that zircon dissociation becomes favorable above 1538 °C and particles between 1 and 100 µm in size melted and dissociated when passing through a plasma, forming spherules with various amounts of SiO2 glass containing ZrO2 crystallites ranging in size from 5 nm to 1 µm. The majority of zircon crystals were monoclinic, but tetragonal ZrO2 was observed for the smaller crystallite sizes. Residence times were in the order of 100 ms, and the specific ZrO2 to SiO2 ratio within each spherule depended on the particle’s time at temperature106.Bohor et al.104 presented images of impact-shocked zircons from the K-Pg impact event at 66 Ma that are morphologically indistinguishable from those at TeH. Decorated zircon grains are uncommon in nature but commonly associated with cosmic impact events, as evidenced by two partially melted zircons from the known airburst/impact at Dakhleh Oasis, Egypt (Fig. 30e). The presence of bubbles indicates that temperatures reached at least 1676 °C, where the zircon began to dissociate and outgas. Similar dissociated zircon grains also have been found in tektite glass and distal fallback ejecta (deposited from hot vapor clouds). Granular baddeleyite-zircon has been found in the ~ 150-km-wide K-Pg impact crater107 and the 28-km-wide Mistatin Lake crater in Canada107. The dissociation of zircon requires high temperatures of ~ 1676 °C104, implying that TeH was exposed to similar extreme conditions.Melted chromite grainsExamples of melted chromite, another mineral that melts at high temperatures, were also observed. Thermally-altered chromite grains were observed in melted pottery, melted mudbricks, and melted roofing clay from the palace. Their estimated density was 1 grain per 100 mm2, making them rarer than melted zircon grains. The morphologies of chromite grains range from thermally altered (Fig. 33a) to fully melted (Fig. 33b, d). One chromite grain from the palace displays unusual octahedral cleavage or shock-induced planar fractures (Fig. 33b). The typical chemical composition for chromite is 25.0 wt.% Fe, 28.6 wt.% O, and 46.5 wt.% Cr, although the Cr content can vary from low values to ~ 68 wt.%. SEM images reveal that, as chromite grains melted, some Cr-rich molten material migrated into and mixed with the host melt, causing an increase in Cr and Fe, and corresponding depletion of Si. The ratio of Cr to Fe in chromite affects its equilibrium melting point, which varies from ~ 1590 °C for a negligible amount of Cr up to ~ 2265 °C for ~ 46.5 wt.% Cr as in chromite or chromian magnetite ((Fe)Cr2O4), placing the melting point of TeH chromite at close to 2265 °C.Figure 33SEM images of melted chromite grains found on a melted potsherd from the palace. (a) Shattered, polycrystalline chromite grain that appears to have become agglutinated while molten. (b) Melted chromite grain, displaying cleavage (lamellae) suggestive of thermal and/or mechanical shock metamorphism at ~ 12 GPa; (c) close-up image showing angles between three sets of crystalline cleavage; (d) manually constructed EDS-based phase map showing chromite (purple) embedded in Ca–Al–Si matrix. The lines mark three sets of cleavage extending across the entire grain. A melt tail merging with the matrix is observed to trail off to the upper right of the grain at arrow.Full size imageDiscussion of melted chromiteChromite grains theoretically melt at ~ 2190 °C. Moore et al.17 reported the results of heating experiments in which chromite grains in bulk sediment showed almost no thermal alteration up to ~ 1500 °C (Supporting Information, Fig. S8). At temperatures of ~ 1600 °C and ~ 1700 °C, the shapes of chromite grains were intact but exhibited limited melting of grain edges. These results establish a range of ~ 1600° to 1700 °C for melting chromite grains.Because chromite typically does not exhibit cleavage, the grain exhibiting this feature is highly unusual. Its origin is unclear but there are several possibilities. The cleavage may have resulted from exsolution while cooling in the source magma. Alternately, the lamellae may have resulted from mechanical shock during a cosmic impact, under the same conditions that produced the shocked quartz, as reported by Chen et al.108 for meteorites shocked at pressures of ~ 12 GPa. Or they may have been formed by thermal shock, i.e., rapid thermal loading followed by rapid quenching. This latter suggestion is supported by the observation that the outside glass coating on the potsherd does not exhibit any quench crystals, implying that the cooling progressed very rapidly from liquid state to solid state (glass). This is rare in terrestrial events except for some varieties of obsidian, but common in melted material produced by atomic detonations (trinitite), lightning strikes (fulgurites), and cosmic airburst/impacts (meltglass)81. More investigations are needed to determine the origin of the potentially shocked chromite.Nuggets of Ir, Pt, Ru, Ni, Ag, Au, Cr, and Cu in meltglassUsing SEM–EDS, we investigated abundances and potential origins (terrestrial versus extraterrestrial) of platinum-group elements (PGEs) embedded in TeH meltglass, in addition to Ni, Au, and Ag. Samples studied include melted pottery (n = 3); melted mudbrick (n = 6); melted roofing clay (n = 1), and melted lime-based building plaster (n = 1). On the surfaces of all four types of meltglass, we observed melted metal-rich nuggets and irregularly shaped metallic splatter, some with high concentrations of PGEs (ruthenium (Ru), rhodium (Rh), palladium (Pd), osmium (Os), iridium (Ir), and platinum (Pt)) and some nuggets enriched in silver (Ag), gold (Au), chromium (Cr), copper (Cu), and nickel (Ni) with no PGEs (Figs. 34, 35). Importantly, these metal-rich nuggets were observed only on the top surfaces of meltglass and not inside vesicles or on broken interior surfaces.Figure 34SEM images of nuggets of melted metals in mudbrick meltglass from the palace. (a)–(c) Pt-dominant TeH nuggets enriched in ruthenium (Ru), rhodium (Rh), palladium (Pd), osmium (Os), iridium (Ir), and platinum (Pt). (d)–(f) Fe-dominant TeH splatter is also enriched in PGEs. (g)–(i) Nuggets enriched in varying percentages and combinations of nickel (Ni), chromium (Cr), copper (Cu), and silver (Ag).Full size imageFigure 35Average composition of selected metal-rich nuggets from the palace. (a-h) Silver (Ag), gold (Au), chromium (Cr), copper (Cu), iridium (Ir), nickel (Ni), platinum (Pt), and ruthenium (Ru), showing wt.% in selected nuggets from the destruction layer of the palace (7GG).Full size imageUsing SEM–EDS, we identified variable concentrations and assemblages of PGEs. The metallic particles appear to have melted at high temperatures based on the minimum melting points of the elements: iridium at 2466 °C; platinum = 1768 °C; and ruthenium = 2334 °C, indicating a temperature range of between approximately 1768° and 2466 °C. Our investigations also identified two PGE groups, one with nuggets in which Pt dominates Fe and the other with metallic splatter in which Fe dominates Pt.Pt-dominant nuggetsWe conducted 21 measurements on Pt-dominant TeH nuggets on meltglass (Fig. 34a–c). The nuggets average ~ 5 µm in length (range 1–12 µm) with an estimated concentration of 1 nugget per 10 mm2. For these nuggets, Fe concentrations average 1.0 wt.%, Ir = 6.0 wt.%, and Pt = 44.9 wt.% (Supporting Information, Tables S6, S7). The presence of PGEs was confirmed by two SEM–EDS instruments that verified the accurate identification of PGEs through analyses of several blanks that showed no PGE content. Some concentrations are low ( Pt or Pt  > Fe were found to be consistent between the two instruments.To determine the source of TeH nuggets and splatter, we constructed ternary diagrams. Terrestrial PGE nuggets are commonly found in ore bodies that when eroded, can become concentrated in riverine placer deposits, including those of the Jordan River floodplain. To compare Fe–Ir–Pt relationships among the TeH nuggets, we compiled data from nearby placer deposits in Greece109, Turkey110,111, and Iraq112, along with distant placers in Russia113,114,115, Canada116, and Alaska, USA117,118. The compilation of 109 Pt-dominant placer nuggets indicates that the average Fe concentration is 8.2 wt.%, Ir = 2.9 wt.%, and Pt = 80.3 wt.%. For the Ir-dominant placer nuggets (n = 104), Fe = 0.4 wt.%, Ir = 47.8 wt.%, and Pt = 5.3 wt.% (Supporting Information, Tables S6, S7). The ternary diagrams reveal that the values for Pt-dominant TeH nuggets overlap with Pt-dominant terrestrial placer nuggets but the Fe-dominant splatter is dissimilar (Fig. 36a).Figure 36Ternary diagrams for PGE-rich grains. Comparison of Fe–Ir–Pt ratios of PGE-rich nuggets fused into the surfaces of TeH meltglass. There are two populations of TeH nuggets (red diamonds): Pt-dominant at #1 (top) and Fe-dominant at #2 (bottom left). (a) TeH Pt-dominant nugget group #1 (red diamonds) overlaps Pt-dominant but not Ir-dominant nuggets (blue circles) from placers and ophiolite deposits in Greece, Turkey, Iraq, Russia, Canada, and the USA. The Fe-dominant TeH nugget group #2 is geochemically dissimilar to all known placer nuggets, suggesting that these nuggets are not placer-derived. (b) TeH nuggets (red diamonds) compared to nuggets in carbonaceous chondrites (light gray circles) and nuggets in cosmic spherules (dark gray circles). Pt-dominant TeH nuggets in group #1 are a poor match, but Fe-dominant TeH splatter is an excellent match with chondritic meteorites and cosmic spherules, suggesting that they may be extraterrestrial in origin and that the impactor may have been a chondrite. (c) TeH nuggets (red diamonds) are a poor match for most nuggets in iron meteorites (purple circles), but an excellent match for nuggets found in comets (green circles). These data suggest that Fe-dominant PGE nuggets at TeH may have originated from cometary material. (d) Semi-log comparison of PGEs ruthenium (Ru), rhodium (Rh), palladium (Pd), osmium (Os), iridium (Ir), and platinum (Pt), normalized to CI chondrites. TeH Fe-dominant splatter (red line) is an excellent match for PGE nuggets in carbonaceous chondrites (blue line), cosmic spherules (purple line), micrometeorites (dark blue line), and iron meteorites (gray line). In contrast, TeH PGE nuggets are a poor match for bulk material from CI-normalized CV-type chondrites (e.g., Allende; orange line) and CM-type chondrites (e.g., Murchison; brown line).Full size imageFe-dominant splatterWe made 8 measurements on TeH Fe-dominant PGE splatter (Fig. 34d–f). The metal-rich areas average ~ 318 µm in length (range 20–825 µm) with an estimated concentration of 1 PGE-rich bleb per mm2, 100× more common than the TeH nuggets. Average concentrations are Fe = 17.5 wt.%, Ir = 4.7 wt.%, and Pt = 1.5 wt.%.We explored a potential extraterrestrial origin by constructing ternary diagrams for comparison of TeH Fe-dominant splatter with known meteorites and comets (Fig. 36b, c). We compiled data for 164 nuggets extracted from carbonaceous chondritic meteorites (e.g., Allende, Murchison, Leoville, and Adelaide)119,120,121,122, seafloor cosmic spherules123,124, iron meteorites122,125, Comet Wild 2126, and cometary dust particles126. For average weight percentages, see Supporting Information, Tables S6, S7. The Fe-dominant TeH splatter (Fig. 36b) closely matches nuggets from carbonaceous chondrites and cosmic spherules but is a weak match for most iron meteorites (Fig. 36c). In addition, the TeH nuggets are similar to four cometary particles, two of which were collected during the Stardust flyby mission of Comet Wild 2 in 2004126. For average weight percentages, see Supporting Information, Tables S6, S7.To further explore an extraterrestrial connection for TeH Fe-dominant splatter, we compiled wt.% data for TeH PGEs (Rh, Ru, Pd, Os, Ir, and Pt) and normalized them to CI chondrites using values from Anders and Grevasse127. We compared those values to CI-normalized nuggets in carbonaceous chondrites, including CV-type chondrites (e.g., Allende) and CM types (e.g., Murchison)119,120,122,128,129,130,131, seafloor cosmic spherules124, micrometeorites123, and iron meteorites122,125. These results are shown in Fig. 36d.The TeH Fe-dominant splatter closely matches all types of extraterrestrial material with a similar pattern among all data sets: Pd has the lowest normalized values and Os and/or Ir have the highest, closely followed by Pt. The TeH splatter was also compared to the CI-normalized wt.% of bulk meteoritic material from CV- and CM-type chondrites (Fig. 36d). The composition of TeH splatter shows poor correlation with bulk chondritic materials, although the splatter is an excellent geochemical match with the PGE nuggets inside them. In summary, the CI normalization of PGEs suggests an extraterrestrial origin for the Fe-dominant TeH splatter, just as the ternary diagrams also suggest an extraterrestrial source. The correspondence of these two independent results suggests that the quantification of PGEs is sufficiently accurate in this study.Another unusually abundant element, Mo, is also associated with Fe-dominant splatter but not with Pt-dominant nuggets. Mo averages 0.3 wt.% with up to 1.1 wt.% detected in Fe-dominant splatter but with none detected in TeH Pt-dominant nuggets. Mo also is not reported in any terrestrial placer nuggets and occurs in low concentrations (less than ~ 0.02 wt.%) in iron meteorites. In contrast, Mo is reported at high concentrations in PGE nuggets from carbonaceous chondrites (~ 11.5 wt.%), cosmic spherules (0.6 wt.%), and cometary material (5.8 wt.%). Thus, the Mo content of TeH splatter appears dissimilar to terrestrial material but overlaps values of known cosmic material, suggesting an extraterrestrial origin.Based on the volume and weight of the meltglass, we estimate that the extraterrestrial-like metallic TeH Fe-dominant splatter represents  More

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    “Indirect development” increases reproductive plasticity and contributes to the success of scyphozoan jellyfish in the oceans

    1.Cartwright, P. et al. Exceptionally preserved jellyfishes from the middle Cambrian. PLoS One 2, e1121 (2007).ADS 
    Article 

    Google Scholar 
    2.Walcott, C. D. Cambrian Geology and Paleontology II: No. 3—Middle Cambrian Holothurians and Medusae Vol. 3 (Smithsonian Institution, 1911).
    Google Scholar 
    3.Willoughby, R. H. & Robison, R. A. Medusoids from the Middle Cambrian of Utah. J. Paleontol. 53, 494–500 (1979).
    Google Scholar 
    4.Rigby, S. & Milsom, C. V. Origins, evolution, and diversification of zooplankton. Annu. Rev. Ecol. Syst. 31, 293–313 (2000).Article 

    Google Scholar 
    5.Young, G. A. & Hagadorn, J. W. The fossil record of cnidarian medusae. Palaeoworld 19, 212–221 (2010).Article 

    Google Scholar 
    6.Technau, U. & Steele, R. E. Evolutionary crossroads in developmental biology: Cnidaria. Development 138, 1447 (2012).Article 

    Google Scholar 
    7.Hoegh-Guldberg, O., Poloczanska, E. S., Skirving, W. & Dove, S. Coral reef ecosystems under climate change and ocean acidification. Front. Mar. Sci. https://doi.org/10.3389/fmars.2017.00158 (2017).Article 

    Google Scholar 
    8.Hagadorn, J. W., Dott, R. H. & Damrow, D. Stranded on a Late Cambrian shoreline: Medusae from central Wisconsin. Geology 30, 147–150 (2002).ADS 
    Article 

    Google Scholar 
    9.Boero, F. Review of jellyfish blooms in the Mediterranean and Black Sea. Studies and Reviews. General Fisheries Commission for the Mediterranean, Vol. 92 (FAO, Rome, 2013).10.Brotz, L., Cheung, W., Kleisner, K., Pakhomov, E. & Pauly, D. Increasing jellyfish populations: Trends in large marine ecosystems. Hydrobiologia 690, 3–20 (2012).Article 

    Google Scholar 
    11.Condon, R. H. et al. Recurrent jellyfish blooms are a consequence of global oscillations. Proc. Natl. Acad. Sci. 110, 1000–1005. https://doi.org/10.1073/pnas.1210920110 (2013).ADS 
    Article 
    PubMed 

    Google Scholar 
    12.Arai, M. Pelagic coelenterates and eutrophication: A review. Hydrobiologia 451, 69–87. https://doi.org/10.1023/A:1011840123140 (2001).Article 

    Google Scholar 
    13.Purcell, J. E., Malej, A. & Benović, A. in Ecosystems at the Land-Sea Margin: Drainage Basin to Coastal Seas Vol. 55 Coastal and Estuarine Studies Ch. 8, 241–263 (American Geophysical Union, 1999).14.Lynam, C. P. et al. Have jellyfish in the Irish Sea benefited from climate change and overfishing?. Glob. Change Biol. 17, 767–782. https://doi.org/10.1111/j.1365-2486.2010.02352.x (2011).ADS 
    Article 

    Google Scholar 
    15.Richardson, A. J., Bakun, A., Hays, G. C. & Gibbons, M. J. The jellyfish joyride: Causes, consequences and management responses to a more gelatinous future. Trends Ecol. Evol. 24, 312–322 (2009).Article 

    Google Scholar 
    16.Lucas, C. H., Graham, W. M. & Widmer, C. Jellyfish life histories: Role of polyps in forming and maintaining scyphomedusa populations. Adv. Mar. Biol. 63, 133–196 (2012).Article 

    Google Scholar 
    17.Helm, R. R. Evolution and development of scyphozoan jellyfish. Biol. Rev. 93, 1228–1250 (2018).Article 

    Google Scholar 
    18.Jarms, G. & Morandini, A. C. World Atlas of Jellyfish (Dölling und Galitz Verlag, Germany, 2019).
    Google Scholar 
    19.Piraino, S., Boero, F., Aeschbach, B. & Schmid, V. Reversing the life cycle: medusae transforming into polyps and cell transdifferentiation in Turritopsis nutricula (Cnidaria, Hydrozoa). Biol. Bull. 180, 302–312 (1996).Article 

    Google Scholar 
    20.De Vito, D., Piraino, S., Schmich, J., Bouillon, J. & Boero, F. Evidence of reverse development in Leptomedusae (Cnidaria, Hydrozoa): the case of Laodicea undulata (Forbes and Goodsir 1851). Mar. Biol. 149, 339–346 (2006).Article 

    Google Scholar 
    21.He, J., Zheng, L., Zhang, W. & Lin, Y. Life cycle reversal in Aurelia sp.1 (Cnidaria, Scyphozoa). PLoS One 10, e0145314 (2015).Article 

    Google Scholar 
    22.Sandrini, L. R. & Avian, M. Biological cycle of Pelagia noctiluca: Morphological aspects of the development from planula to ephyra. Mar. Biol. 74, 169–174. https://doi.org/10.1007/BF00413920 (1983).Article 

    Google Scholar 
    23.Jarms, G., Båmstedt, U., Tiemann, H., Martinussen, M. B. & Fosså, J. H. The holopelagic life cycle of the deep-sea medusa Periphylla periphylla (Scyphozoa, Coronatae). Sarsia 84, 55–65 (1999).Article 

    Google Scholar 
    24.Dawson, M. N. & Hamner, W. M. A character-based analysis of the evolution of jellyfish blooms: Adaptation and exaptation. Hydrobiologia 616, 193–215. https://doi.org/10.1007/s10750-008-9591-x (2009).Article 

    Google Scholar 
    25.Ceh, J., Gonzalez, J., Pacheco, A. S. & Riascos, J. M. The elusive life cycle of scyphozoan jellyfish—Metagenesis revisited. Sci. Rep. 5, 12037. https://doi.org/10.1038/srep12037. http://www.nature.com/srep/2015/150708/srep12037/abs/srep12037.html#supplementary-information (2015).26.Campos, L., Gonzállez, K. & Ceh, J. First report of a precocious form of strobilation in a jellyfish, the South American Pacific sea nettle Chrysaora plocamia. Mar. Biodivers. 50, 85 (2020).Article 

    Google Scholar 
    27.Henroth, L. & Grondähl, F. On the biology of Aurelia aurita (L.) 1. Release and growth of Aurelia aurita (L.) ephyrae in the Gullmar Fjiord, western Sweden, 1982–83. Ophelia 22, 189–199 (1983).Article 

    Google Scholar 
    28.Hirai, E. On the developmental cycles of Aurelia aurita and Dactylometra pacifica. Bull. Mar. Biol. Stn Asamushi IX, 81 (1958).
    Google Scholar 
    29.Kakinuma, Y. An experimental study of the life cycle and organ differentiation of Aurelia aurita Lamarck. Bull. Mar. Biol. Stn. Asamushi XV, 101–113 (1975).
    Google Scholar 
    30.Yasuda, T. Ecological studies on the jelly-fish, Aurelia aurita, in Urazoko Bay, Fukui Prefecture-XI. An observation on ephyra formation. Publ. Seto Mar. Biol. Lab. XXII, 75–80 (1975).Article 

    Google Scholar 
    31.Suzuki, K. S. et al. Seasonal alternation of the ontogenetic development of the moon jellyfish Aurelia coerulea in Maizuru Bay, Japan. PLoS One 14, e0225513. https://doi.org/10.1371/journal.pone.0225513 (2019).CAS 
    Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    32.Avian, M. In Workshop on Jellyfish in the Mediterranean Sea Vol. 2 (eds Rottini Sandrini, L. & Avian, M.) 47–59 (Nova Thalassia, 1986).
    Google Scholar 
    33.Costello, J. et al. Project Meduza in the context of its historical time. Ann. Ser. Hist. Nat. 19, 1–18 (2009).
    Google Scholar 
    34.Margiotta, F. et al. Do plankton reflect the environmental quality status? The case of a post-industrial Mediterranean Bay. Mar. Environ. Res. 160, 104980 (2020).CAS 
    Article 

    Google Scholar 
    35.Schiariti, A. et al. Asexual reproduction strategies and blooming potential in Scyphozoa. Mar. Ecol. Prog. Ser. 510, 241–253 (2014).ADS 
    Article 

    Google Scholar 
    36.Yasuda, T. Ecological studies on the jelly-fish, Aurelia aurita, in Urazoko Bay, Fukui Prefecture-IV. Monthly change in the bell-length composition and breeding season. Bull. Jpn. Soc. Sci. Fish. 37, 364–370 (1971).Article 

    Google Scholar 
    37.Suryan, R. M. et al. Environmental forcing on life history strategies: Evidence for multi-trophic level responses at ocean basin scales. Prog. Oceanogr. 81, 214–222 (2009).ADS 
    Article 

    Google Scholar 
    38.Dawson, M. N. Macro-morphological variation among cryptic species of the moon jellyfish, Aurelia (Cnidaria: Scyphozoa). Mar. Biol. 143, 369–379 (2003).Article 

    Google Scholar 
    39.Benović, A. et al. Ecological characteristics of the Mljet Island seawater lakes (South Adriatic Sea) with special reference to their resident population of medusae. Sci. Mar. 64, 197–206 (2000).Article 

    Google Scholar 
    40.Prieto, L., Astorga, D., Navarro, G. & Ruiz, J. Environmental control of phase transition and polyp survival of a massive-outbreaker jellyfish. PLoS One 5, e13793. https://doi.org/10.1371/journal.pone.0013793 (2010).ADS 
    CAS 
    Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    41.Purcell, J. et al. Temperature effects on asexual reproduction rates of scyphozoan polyps from the NW Mediterranean Sea. Hydrobiologia 690, 169–180 (2012).CAS 
    Article 

    Google Scholar 
    42.Kikinger, R. Cotylorhiza tuberculata (Cnidaria: Scyphozoa)—Life history of a stationary population. PSZN Mar. Ecol. 13, 333–362 (1992).Article 

    Google Scholar 
    43.Djeghri, N., Pondaven, P., Stibor, H. & Dawson, M. N. Review of the diversity, traits, and ecology of zooxanthellate jellyfishes. Mar. Biol. 166, 147 (2019).Article 

    Google Scholar 
    44.Glynn, P. W. & Colgan, M. W. Sporadic disturbances in fluctuating coral reef environments: El Niño and coral reef development in the Eastern Pacific. Am. Zool. 32, 707–718. https://doi.org/10.1093/icb/32.6.707 (1999).Article 

    Google Scholar  More

  • in

    Study on hyperspectral estimation model of soil organic carbon content in the wheat field under different water treatments

    1.Aryal, D. R., De Jong, B. H., Ochoa-Gaona, S., Esparza-Olguin, L. & Mendoza-Vega, J. Carbon stocks and changes in tropical secondary forests of southern Mexico. Agr. Ecosyst. Environ. 195, 220–230 (2014).Article 

    Google Scholar 
    2.Aryal, D. R., De Jong, B. H., Ochoa-Gaona, S., Mendoza-Vega, J. & Esparza-Olguin, L. Successional and seasonal variation in litterfall and associated nutrient transfer in semi-evergreen tropical forests of SE Mexico. Nutr. Cycl. Agroecosys. 103(1), 45–60 (2015).CAS 
    Article 

    Google Scholar 
    3.Aryal, D. R. et al. Soil organic carbon depletion from forests to grasslands conversion in Mexico: A review. Trop. Agric. 8, 181 (2018).CAS 

    Google Scholar 
    4.Gao, W., Yang, J., Ren, S. R. & Liu, H. L. The trend of soil organic carbon, total nitrogen, and wheat and maize productivity under different long-term fertilizations in the upland fluvo-aquic soil of North China. Nutr. Cycl. Agroecosys. 103, 61–73 (2015).CAS 
    Article 

    Google Scholar 
    5.Qi, H., Paz-Kagan, T., Karnieli, A., Jin, X. & Li, S. Evaluating calibration methods for predicting soil available nutrients using hyperspectral VNIR data. Soil Till Res. 175, 267–275 (2018).Article 

    Google Scholar 
    6.Dong, X., Tian, J., Zhang, R., He, D. & Chen, Q. Study on the relationship between soil emissivity spectra and content of soil element. Spectrosc. Spect. Anal. 37(02), 557–564 (2017).CAS 

    Google Scholar 
    7.Kemper, T. & Sommer, S. Estimate of heavy metal contamination in soils after a mining accident using reflectance spectroscopy. Environ. Sci. Technol. 36(12), 2742–2747 (2002).ADS 
    CAS 
    PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    8.Panigrahi, N. & Das, B. S. Canopy spectral reflectance as a predictor of soil water potential in rice. Water Resour. Res. 54(4), 2544–2560 (2018).ADS 
    Article 

    Google Scholar 
    9.Peddle, D. R., White, H. P., Soffer, R. J., Miller, J. R. & LeDrew, E. F. Reflectance processing of remote sensing spectroradiometer data. Comput. Geoences. 27(2), 203–213 (2001).ADS 

    Google Scholar 
    10.Ben-Dor, E. et al. Using imaging spectroscopy to study soil properties. Remote Sens. Environ. 113, S38–S55 (2009).Article 

    Google Scholar 
    11.Rossel, R. A., Walvoort, D. J., Mcbratney, A. B., Janik, L. J. & Skjemstad, J. O. Visible, near infrared, mid infrared or combined diffuse reflectance spectroscopy for simultaneous assessment of various soil properties. Geoderma 131(1), 59–75 (2006).ADS 
    Article 
    CAS 

    Google Scholar 
    12.Cheng, H. et al. Estimating heavy metal concentrations in suburban soils with reflectance spectroscopy. Geoderma 336, 59–67 (2019).ADS 
    CAS 
    Article 

    Google Scholar 
    13.Ding, J., Yang, A., Wang, J., Sagan, V. & Yu, D. Machine-learning-based quantitative estimation of soil organic carbon content by VIS/NIR spectroscopy. PeerJ 6(3), e5714 (2018).PubMed 
    PubMed Central 
    Article 
    CAS 

    Google Scholar 
    14.Gobrecht, A., Bendoula, R., Roger, J.-M. & Bellon-Maurel, V. A new optical method coupling light polarization and vis–NIR spectroscopy to improve the measurement of soil carbon content. Soil Till Res. 155, 461–470 (2016).Article 

    Google Scholar 
    15.Gu, X., Wang, Y., Song, X. & Xu, X. The Inversion Model of Soil Organic Matter of Cultivated Land Based on Hyperspectral Technology. Remote Sensing for Agriculture, Ecosystems, and Hydrology XVII (International Society for Optics and Photonics, 2015).
    Google Scholar 
    16.Nawar, S., Buddenbaum, H., Hill, J., Kozak, J. & Mouazen, A. M. Estimating the soil clay content and organic matter by means of different calibration methods of vis-NIR diffuse reflectance spectroscopy. Soil Till Res. 155, 510–522 (2016).Article 

    Google Scholar 
    17.Yu, X., Liu, Q., Wang, Y., Liu, X. & Liu, X. Evaluation of MLSR and PLSR for estimating soil element contents using visible/near-infrared spectroscopy in apple orchards on the Jiaodong peninsula. CATENA 137, 340–349 (2016).CAS 
    Article 

    Google Scholar 
    18.Ji, W. J., Li, X., Li, C. X., Zhou, Y. & Shi, Z. Using different data mining algorithes to predict soil organic matter based on visible-near infrared spectroscopy. Spectrosc. Spect. Anal. 32(09), 2393–2397 (2012).CAS 

    Google Scholar 
    19.Douglas, R. K., Nawar, S., Alamar, M. C., Mouazen, A. M. & Coulon, F. Rapid prediction of total petroleum hydrocarbons concentration in contaminated soil using vis-NIR spectroscopy and regression techniques. Sci. Total Environ. 616, 147–155 (2018).ADS 
    PubMed 
    Article 
    CAS 
    PubMed Central 

    Google Scholar 
    20.Mouazen, A. M. & Al-Asadi, R. A. Influence of soil moisture content on assessment of bulk density with combined frequency domain reflectometry and visible and near infrared spectroscopy under semi field conditions. Soil Till Res. 176, 95–103 (2018).Article 

    Google Scholar 
    21.Rossel, R. A. & Behrens, T. Using data mining to model and interpret soil diffuse reflectance spectra. Geoderma 158(1), 46–54 (2010).ADS 
    CAS 
    Article 

    Google Scholar 
    22.Nawar, S. & Mouazen, A. M. Comparison between random forests, artificial neural networks and gradient boosted machines methods of on-line vis-NIR spectroscopy measurements of soil total nitrogen and total carbon. Sensors. 17(10), 2428 (2017).ADS 
    PubMed Central 
    Article 
    CAS 

    Google Scholar 
    23.Wang, J., Chen, Y., Chen, F., Shi, T. & Wu, G. Wavelet-based coupling of leaf and canopy reflectance spectra to improve the estimation accuracy of foliar nitrogen concentration. Agr. Forest Meteorol. 248, 306–315 (2018).ADS 
    Article 

    Google Scholar 
    24.Hong, Y. et al. Combining fractional order derivative and spectral variable selection for organic matter estimation of homogeneous soil samples by vis–NIR spectroscopy. Remote Sens. 10(3), 479 (2018).ADS 
    Article 

    Google Scholar 
    25.Sorenson, P. T. et al. Monitoring organic carbon, total nitrogen, and pH for reclaimed soils using field reflectance spectroscopy. Can J Soil Sci. 97(2), 241–248 (2017).CAS 
    Article 

    Google Scholar 
    26.Gomez, C., Rossel, R. A. V. & Mcbratney, A. B. Soil organic carbon prediction by hyperspectral remote sensing and field vis-NIR spectroscopy: An Australian case study. Geoderma 146(3–4), 403–411 (2008).ADS 
    CAS 
    Article 

    Google Scholar 
    27.Shi, T. Z. et al. Comparison of multivariate methods for estimating soil total nitrogen with visible/near-infrared spectroscopy. Plant Soil. 366(1–2), 363–375 (2013).CAS 
    Article 

    Google Scholar 
    28.Stenberg, B., Rossel, R. A. V., Mouazen, A. M. & Wetterlind, J. Chapter five-visible and near infrared spectroscopy in soil science. Adv. Agron. 107, 163–215 (2010).CAS 
    Article 

    Google Scholar 
    29.Uddin, M. P., Mamun, M. A. & Hossain, M. A. PCA-based feature reduction for hyperspectral remote sensing image classification. IETE Tech. Rev. 5, 1–21 (2020).
    Google Scholar 
    30.Cambule, A. H., Rossiter, D. G., Stoorvogel, J. J. & Smaling, E. M. A. Building a near infrared spectral library for soil organic carbon estimation in the Limpopo National Park Mozambique. Geoderma 183, 41–48 (2012).ADS 
    Article 
    CAS 

    Google Scholar 
    31.Kawamura, K. et al. Vis-NIR spectroscopy and PLS regression with waveband selection for estimating the total C and N of paddy soils in Madagascar. Remote Sens. 9(10), 1081 (2017).ADS 
    Article 

    Google Scholar 
    32.Leone, A. P., Viscarra-Rossel, R. A., Amenta, P. & Buondonno, A. Prediction of soil properties with PLSR and vis-NIR spectroscopy: Application to mediterranean soils from southern Italy. Curr. Anal. Chem. 8(2), 283–299 (2012).CAS 
    Article 

    Google Scholar 
    33.Wang, S., Chen, Y., Wang, M., Zhao, Y. & Li, J. SPA-based methods for the quantitative estimation of the soil salt content in saline-alkali land from field spectroscopy data: A case study from the Yellow River irrigation regions. Remote Sens. 11(8), 967 (2019).ADS 
    CAS 
    Article 

    Google Scholar 
    34.Barnes, E. M. et al. Remote- and ground-based sensor techniques to map soil properties. Photogramm. Eng Rem S. 69(6), 619–630 (2003).Article 

    Google Scholar 
    35.Priori, S. et al. Field-scale mapping of soil carbon stock with limited sampling by coupling gamma-ray and vis-NIR spectroscopy. Soil Sci Soc Am J. 80(4), 954–964 (2016).ADS 
    CAS 
    Article 

    Google Scholar 
    36.Amin, I., Fikrat, F., Mammadov, E. & Babayev, M. Soil organic carbon prediction by vis-NIR spectroscopy: Case study the Kur-Aras plain Azerbaijan. Commun. Soil Sci. Plan. 51(6), 726–734 (2020).CAS 
    Article 

    Google Scholar 
    37.Yu, L. et al. Hyperspectral estimation of soil organic matter content based on partial least squares regression. Trans. CSAE. 31(14), 103–109 (2015).
    Google Scholar 
    38.Liu, Y. F., Lu, Y. N., Guo, L., Xiao, F. T. & Chen, Y. Y. Construction of calibration set based on the land use types in visible and near-infrared (VIS-NIR)model for soil organic matter estimation. Acta Pedol. Sin. 53, 332–341 (2016).
    Google Scholar 
    39.Zhou, X. M. & Zhang, T. Analysis of the April 2019 atmospheric circulation and weather. Meteor. Mon. 45(7), 1028–1036 (2019).
    Google Scholar 
    40.Guan, L. & Zhang, T. Analysis of the May 2019 atmospheric circulation and weather. Meteor. Mon. 45(8), 1181–1188 (2019).
    Google Scholar 
    41.Li, X., He, Y. & Wu, C. Non-destructive discrimination of paddy seeds of different storage age based on Vis/NIR spectroscopy. J. Stored Prod. Res. 44(3), 264–268 (2008).Article 

    Google Scholar 
    42.Boško, M. & Bensa, A. Prediction of soil organic carbon using VIS-NIR spectroscopy: Application to Red Mediterranean soils from Croatia. Eurasian J. Soil Sci. 6(4), 365–373 (2017).
    Google Scholar 
    43.McCarty, G. W., Reeves, J. B. III., Reeves, V. B., Follett, R. F. & Kimble, J. M. Mid-infrared and near-infrared diffuse reflectance spectroscopy for soil carbon measurement. Soil Sci. Soc. Am. J. 66(2), 640–646 (2002).ADS 
    CAS 
    Article 

    Google Scholar 
    44.Gholizadeh, A. et al. Comparing different data preprocessing methods for monitoring soil heavy metals based on soil spectral features. Soil Water Res. 10(4), 218–227 (2015).CAS 
    Article 

    Google Scholar 
    45.Wang, X., Xue, L., He, X. W. & Liu, M. H. Vitamin C content estimation of chilies using Vis/NIR spectroscopy. Int. Conf. Electr. Inf. Control Eng. 2011, 1894–1897 (2011).
    Google Scholar 
    46.Lee, K. S. et al. Wavelength identification and diffuse reflectance estimation for surface and profile soil properties. Am. Soc. Agric. Biol. Eng. 52(3), 683–695 (2009).CAS 

    Google Scholar  More

  • in

    Genome-wide analysis reveals associations between climate and regional patterns of adaptive divergence and dispersal in American pikas

    Alexander DH, Novembre J, Lange K (2009) Fast model-based estimation of ancestry in unrelated individuals. Genome Res 19:1655–1664CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Alexander DH, Shringarpure SS, Novembre J, Lange K (2015) Admixture 1.3 software manual. UCLA Hum Genet Softw Distrib, Los Angeles
    Google Scholar 
    Angert AL, Bontrager MG, Ågren J (2020) What do we really know about adaptation at range edges? Annu Rev Ecol Evol Syst 51:341–361Article 

    Google Scholar 
    Araújo MB, Pearson RG, Thuiller W, Erhard M (2005) Validation of species–climate impact models under climate change. Glob Change Biol 11:1504–1513Article 

    Google Scholar 
    Astle W, Balding DJ (2009) Population structure and cryptic relatedness in genetic association studies. Stat Sci 24:451–471Article 

    Google Scholar 
    Attard CRM, Beheregaray LB, Möller LM (2018) Genotyping-by-sequencing for estimating relatedness in nonmodel organisms: Avoiding the trap of precise bias. Mol Ecol Resour 18:381–390CAS 
    PubMed 
    Article 

    Google Scholar 
    Baird NA, Etter PD, Atwood TS, Currey MC, Shiver AL, Lewis ZA et al. (2008) Rapid SNP discovery and genetic mapping using sequenced RAD markers. PLoS One 3:1–7Article 
    CAS 

    Google Scholar 
    Barbosa S, Mestre F, White TA, Paupério J, Alves PC, Searle JB (2018) Integrative approaches to guide conservation decisions: Using genomics to define conservation units and functional corridors. Mol Ecol 27:3452–3465PubMed 
    Article 

    Google Scholar 
    Beever EA, Brussard PF, Berger J (2003) Patterns of apparent extirpation among isolated populations of pikas (Ochotona princeps) in the Great Basin. J Mammal 84:37–54Article 

    Google Scholar 
    Beever EA, Ray C, Mote PW, Wilkening JL (2010) Testing alternative models of climate-mediated extirpations. Ecol Appl 20:164–178PubMed 
    Article 

    Google Scholar 
    Beever EA, Ray C, Wilkening JL, Brussard PF, Mote PW (2011) Contemporary climate change alters the pace and drivers of extinction. Glob Change Biol 17:2054–2070Article 

    Google Scholar 
    Beever EA, Perrine JD, Rickman T, Flores M, Clark JP, Waters C et al. (2016) Pika (Ochotona princeps) losses from two isolated regions reflect temperature and water balance, but reflect habitat area in a mainland region. J Mammal 97:1495–1511Article 

    Google Scholar 
    Bellard C, Bertelsmeier C, Leadley P, Thuiller W, Courchamp F (2012) Impacts of climate change on the future of biodiversity. Ecol Lett 15:365–377PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Blois JL, Williams JW, Fitzpatrick MC, Jackson ST, Ferrier S (2013) Space can substitute for time in predicting climate-change effects on biodiversity. Proc Natl Acad Sci USA 110:9374–9379CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Browning SR (2008) Missing data imputation and haplotype phase inference for genome-wide association studies. Hum Genet 124:439–450CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Calkins MT, Beever EA, Boykin KG, Frey JK, Andersen MC (2012) Not-so-splendid isolation: modeling climate-mediated range collapse of a montane mammal Ochotona princeps across numerous ecoregions. Ecography 35:780–791Article 

    Google Scholar 
    Carlson SM, Cunningham CJ, Westley PAH (2014) Evolutionary rescue in a changing world. Trends Ecol Evol 29:521–530PubMed 
    Article 

    Google Scholar 
    Castillo JA, Epps CW, Davis AR, Cushman SA (2014) Landscape effects on gene flow for a climate-sensitive montane species, the American pika. Mol Ecol 23:843–856PubMed 
    Article 

    Google Scholar 
    Castillo JA, Epps CW, Jeffress MR, Ray C, Rodhouse TJ, Schwalm D (2016) Replicated landscape genetic and network analyses reveal wide variation in functional connectivity for American pikas. Ecol Appl 26:1660–1676PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    Ceballos G, Ehrlich PR, Raven PH (2020) Vertebrates on the brink as indicators of biological annihilation and the sixth mass extinction. Proc Natl Acad Sci USA 117:13596–13602CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Ceballos G, Ehrlich PR, Barnosky AD, García A, Pringle RM, Palmer TM (2015) Accelerated modern human–induced species losses: entering the sixth mass extinction. Sci Adv 1:e1400253PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Chapman JA, Flux JE (1990) Rabbits, hares and pikas: status survey and conservation action plan. IUCN.Chypre M, Zaidi N, Smans K (2012) ATP-citrate lyase: a mini-review. Biochem Biophys Res Commun 422:1–4CAS 
    PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    Cornuet JM, Luikart G (1996) Description and power analysis of two tests for detecting recent population bottlenecks from allele frequency data. Genetics 144:2001–2014CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Danecek P, Auton A, Abecasis G, Albers CA, Banks E, DePristo MA et al. (2011) The variant call format and VCFtools. Bioinformatics 27:2156–2158CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Diaz HF, Grosjean M, Graumlich L (2003) Climate variability and change in high elevation regions: past, present and future. Clim Change 59:1–4Article 

    Google Scholar 
    Eckert CG, Samis EK, Lougheed SC (2008) Genetic variation across species’ geographical ranges: the central-marginal hypothesis and beyond. Mol Ecol 17:1170–1188CAS 
    PubMed 
    Article 

    Google Scholar 
    Erb LP, Ray C, Guralnick R (2011) On the generality of a climate-mediated shift in the distribution of the American pika (Ochotona princeps). Ecology 92:1730–1735PubMed 
    Article 

    Google Scholar 
    Excoffier L, Foll M, Petit RJ (2009) Genetic consequences of range expansions. Annu Rev Ecol Evol Syst 40:481–501Article 

    Google Scholar 
    Flanagan SP, Forester BR, Latch EK, Aitken SN, Hoban S (2018) Guidelines for planning genomic assessment and monitoring of locally adaptive variation to inform species conservation. Evol Appl 11:1035–1052PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    Foll M, Gaggiotti O (2008) A genome-scan method to identify selected loci appropriate for both dominant and codominant markers: a Bayesian perspective. Genetics 180:977–993PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Franks SJ, Hoffmann AA (2012) Genetics of climate change adaptation. Annu Rev Genet 46:185–208CAS 
    PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    Frichot E, François O (2015) LEA: an R package for landscape and ecological association studies. Methods Ecol Evol 6:925–929Article 

    Google Scholar 
    Frichot E, Schoville SD, Bouchard G, François O (2013) Testing for associations between loci and environmental gradients using latent factor mixed models. Mol Biol Evol 30:1687–1699CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Funk WC, McKay JK, Hohenlohe PA, Allendorf FW (2012) Harnessing genomics for delineating conservation units. Trends Ecol Evol 27:489–496PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Galbreath KE, Hafner DJ, Zamudio KR (2009) When cold is better: climate-driven elevation shifts yield complex patterns of diversification and demography in an Alpine specialist (American Pika, Ochotona Princeps). Evolution 63:2848–2863CAS 
    PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    Gautier M (2015) Genome-wide scan for adaptive divergence and association with population-specific covariates. Genetics 201:1555–1579CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Götz S, García-Gómez JM, Terol J, Williams TD, Nagaraj SH, Nueda MJ et al. (2008) High-throughput functional annotation and data mining with the Blast2GO suite. Nucleic Acids Res 36:3420–3435PubMed 
    PubMed Central 
    Article 
    CAS 

    Google Scholar 
    Gradogna A, Gavazzo P, Boccaccio A, Pusch M (2017) Subunit‐dependent oxidative stress sensitivity of LRRC8 volume‐regulated anion channels. J Physiol 595:6719–6733CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Hafner DJ, Sullivan RM (1995) Historical and ecological biogeography of Nearctic pikas (Lagomorpha: Ochotonidae). J Mammal 76:302–321Article 

    Google Scholar 
    Hafner DJ, Smith AT (2010) Revision of the subspecies of the American pika, Ochotona princeps (Lagomorpha: Ochotonidae). J Mammal 91:401–417Article 

    Google Scholar 
    Hanson JO, Marques A, Veríssimo A, Camacho-Sanchez M, Velo-Antón G, Martínez-Solano Í et al. (2020) Conservation planning for adaptive and neutral evolutionary processes. J Appl Ecol 57:2159–2169Article 

    Google Scholar 
    Harrisson KA, Pavlova A, Telonis-Scott M, Sunnucks P (2014) Using genomics to characterize evolutionary potential for conservation of wild populations. Evol Appl 7:1008–1025PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Heled J, Drummond AJ (2008) Bayesian inference of population size history from multiple loci. BMC Evol Biol 8:289PubMed 
    PubMed Central 
    Article 
    CAS 

    Google Scholar 
    Henry P, Russello MA (2013) Adaptive divergence along environmental gradients in a climate-change-sensitive mammal. Ecol Evol 3:3906–3917CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Henry P, Sim Z, Russello MA (2012) Genetic evidence for restricted dispersal along continuous altitudinal gradients in a climate change-sensitive mammal: the American pika. PLoS One 7:e39077CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Hewitt G (2000) The genetic legacy of the Quaternary ice ages. Nature 405:907–913CAS 
    PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    Hewitt GM (1996) Some genetic consequences of ice ages, and their role in divergence and speciation. Biol J Linn Soc 58:247–276Article 

    Google Scholar 
    Hinzpeter A, Lipecka J, Brouillard F, Baudoin-Legros M, Dadlez M, Edelman A et al. (2006) Association between Hsp90 and the ClC-2 chloride channel upregulates channel function. Am J Physiol Cell Physiol 290:C45–56CAS 
    PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    Hoban S (2018) Integrative conservation genetics: Prioritizing populations using climate predictions, adaptive potential and habitat connectivity. Mol Ecol Resour 18:14–17PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    Hoffmann AA, Sgrò CM (2011) Climate change and evolutionary adaptation. Nature 470:479–485CAS 
    PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    Holbrook JD, DeYoung RW, Janecka JE, Tewes ME, Honeycutt RL, Young JH (2012) Genetic diversity, population structure, and movements of mountain lions (Puma concolor) in Texas. J Mammal 93:989–1000Article 

    Google Scholar 
    IPCC (2014) AR5 Synthesis Report: Climate Change 2014. IPCC.Jentsch TJ, Lutter D, Planells-Cases R, Ullrich F, Voss FK (2016) VRAC: molecular identification as LRRC8 heteromers with differential functions. Pflüg Arch Eur J Physiol 468:385–393CAS 
    Article 

    Google Scholar 
    Johnston AN, Bruggeman JE, Beers AT, Beever EA, Christophersen RG, Ransom JI (2019) Ecological consequences of anomalies in atmospheric moisture and snowpack. Ecology 100:e02638PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    Johnston KM, Freund KA, Schmitz OJ (2012) Projected range shifting by montane mammals under climate change: implications for Cascadia’s National Parks. Ecosphere 3:art97Article 

    Google Scholar 
    Kilham L (1958) Territorial behavior in pikas. J Mammal 39:307–307Article 

    Google Scholar 
    Kimura M (1971) Theoretical foundation of population genetics at the molecular level. Theor Popul Biol 2:174–208CAS 
    PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    Klingler KB, Jahner JP, Parchman TL, Ray C, Peacock MM (2021) Genomic variation in the American pika: signatures of geographic isolation and implications for conservation. BMC Ecol Evol 21:2CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Lambers JHR (2015) Extinction risks from climate change. Science 348:501–502CAS 
    PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    Latch EK, Dharmarajan G, Glaubitz JC, Rhodes OE (2006) Relative performance of Bayesian clustering software for inferring population substructure and individual assignment at low levels of population differentiation. Conserv Genet 7:295–302Article 

    Google Scholar 
    Latch EK, Scognamillo DG, Fike JA, Chamberlain MJ, Rhodes Jr OE (2008) Deciphering ecological barriers to North American River Otter (Lontra canadensis) gene flow in the Louisiana landscape. J Hered 99:265–274CAS 
    PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    Lee KM, Coop G (2017) Distinguishing among modes of convergent adaptation using population genomic data. Genetics 207:1591–1619PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Lee KM, Coop G (2019) Population genomics perspectives on convergent adaptation. Philos Trans R Soc B Biol Sci 374:20180236Article 

    Google Scholar 
    Lemay MA, Russello MA (2015) Genetic evidence for ecological divergence in kokanee salmon. Mol Ecol 24:798–811CAS 
    PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    Li H (2013) Aligning sequence reads, clone sequences and assembly contigs with BWA-MEM. ArXiv13033997 Q-Bio.MacArthur RA, Wang LCH (1974) Behavioral thermoregulation in the pika Ochotona princeps: a field study using radiotelemetry. Can J Zool 52:353–358CAS 
    PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    McCain CM (2019) Assessing the risks to United States and Canadian mammals caused by climate change using a trait-mediated model. J Mammal 100:1808–1817
    Google Scholar 
    Meirmans PG, Van Tienderen PH (2004) GENOTYPE and GENODIVE: two programs for the analysis of genetic diversity of asexual organisms. Mol Ecol Notes 4:792–794Article 

    Google Scholar 
    Morin PA, Luikart G, Wayne RK, the SNP Workshop Group (2004) SNPs in ecology, evolution and conservation. Trends Ecol Evol 19:208–216Article 

    Google Scholar 
    Moritz C, Agudo R (2013) The future of species under climate change: resilience or decline? Science 341:504–508CAS 
    PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    Moritz C, Patton JL, Conroy CJ, Parra JL, White GC, Beissinger SR (2008) Impact of a century of climate change on small-mammal communities in Yosemite National Park, USA. Science 322:261–264CAS 
    PubMed 
    Article 

    Google Scholar 
    Morrison SF, Hik DS (2007) Demographic analysis of a declining pika Ochotona collaris population: linking survival to broad-scale climate patterns via spring snowmelt patterns. J Anim Ecol 76:899–907PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    Moyer‐Horner L, Mathewson PD, Jones GM, Kearney MR, Porter WP (2015) Modeling behavioral thermoregulation in a climate change sentinel. Ecol Evol 5:5810–5822PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Mussmann SM, Douglas MR, Chafin TK, Douglas ME (2019) BA3-SNPs: contemporary migration reconfigured in BayesAss for next-generation sequence data. Methods Ecol Evol 10:1808–1813Article 

    Google Scholar 
    Parmesan C (2006) Ecological and evolutionary responses to recent climate change. Annu Rev Ecol Evol Syst 37:637–669Article 

    Google Scholar 
    Paz-Vinas I, Loot G, Hermoso V, Veyssière C, Poulet N, Grenouillet G et al. (2018) Systematic conservation planning for intraspecific genetic diversity. Proc R Soc B Biol Sci 285:20172746Article 

    Google Scholar 
    Peacock MM (1997) Determining natal dispersal patterns in a population of North American pikas (Ochotona princeps) using direct mark-resight and indirect genetic methods. Behav Ecol 8:340–350Article 

    Google Scholar 
    Peacock MM, Smith AT (1997) Nonrandom mating in pikas Ochotona princeps: evidence for inbreeding between individuals of intermediate relatedness. Mol Ecol 6:801–811CAS 
    PubMed 
    Article 

    Google Scholar 
    Pew J, Muir PH, Wang J, Frasier TR (2015) related: an R package for analysing pairwise relatedness from codominant molecular markers. Mol Ecol Resour 15:557–561PubMed 
    Article 

    Google Scholar 
    Pickett STA (1989) Space-for-time substitution as an alternative to long-term studies. In: Likens GE (ed) Long-term studies in ecology. Springer New York, New York, NY, p 110–135Chapter 

    Google Scholar 
    Piry S, Alapetite A, Cornuet J-M, Paetkau D, Baudouin L, Estoup A (2004) GENECLASS2: a software for genetic assignment and first-generation migrant detection. J Hered 95:536–539CAS 
    PubMed 
    Article 

    Google Scholar 
    R Core Team (2019) R: a language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria, http://www.R-project.org/Rankin AM, Galbreath KE, Teeter KC (2017) Signatures of adaptive molecular evolution in American pikas (Ochotona princeps). J Mammal 98:1156–1167Article 

    Google Scholar 
    Rannala B, Mountain JL (1997) Detecting immigration by using multilocus genotypes. Proc Natl Acad Sci USA 94:9197–9201CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Razgour O, Taggart JB, Manel S, Juste J, Ibáñez C, Rebelo H et al. (2018) An integrated framework to identify wildlife populations under threat from climate change. Mol Ecol Resour 18:18–31PubMed 
    Article 

    Google Scholar 
    Rellstab C, Gugerli F, Eckert AJ, Hancock AM, Holderegger R (2015) A practical guide to environmental association analysis in landscape genomics. Mol Ecol 24:4348–4370PubMed 
    Article 

    Google Scholar 
    Ritland K (1996) Estimators for pairwise relatedness and individual inbreeding coefficients. Genet Res 67:175–185Article 

    Google Scholar 
    Robson KM, Lamb CT, Russello MA (2016) Low genetic diversity, restricted dispersal, and elevation-specific patterns of population decline in American pikas in an atypical environment. J Mammal 97:464–472Article 

    Google Scholar 
    Rochette NC, Rivera‐Colón AG, Catchen JM (2019) Stacks 2: analytical methods for paired-end sequencing improve RADseq-based population genomics. Mol Ecol 28:4737–4754CAS 
    PubMed 
    Article 

    Google Scholar 
    Rousset F (2008) GENEPOP’007: a complete re-implementation of the GENEPOP software for Windows and Linux. Mol Ecol Resour 8:103–106PubMed 
    Article 

    Google Scholar 
    Rubidge EM, Patton JL, Lim M, Burton AC, Brashares JS, Moritz C (2012) Climate-induced range contraction drives genetic erosion in an alpine mammal. Nat Clim Change 2:285–288Article 

    Google Scholar 
    Russello MA, Waterhouse MD, Etter PD, Johnson EA (2015) From promise to practice: pairing non-invasive sampling with genomics in conservation. PeerJ 3:e1106PubMed 
    PubMed Central 
    Article 
    CAS 

    Google Scholar 
    Savolainen O, Lascoux M, Merilä J (2013) Ecological genomics of local adaptation. Nat Rev Genet 14:807–820CAS 
    PubMed 
    Article 

    Google Scholar 
    Sjodin BMF, Galbreath KE, Lanier HC, Russello MA (2021) Chromosome-level reference genome assembly for the American pika (Ochotona princeps). J Hered https://doi.org/10.1093/jhered/esab031Smith AT (1974b) The distribution and dispersal of pikas: influences of behavior and climate. Ecology 55:1368–1376Article 

    Google Scholar 
    Smith AT (1974a) The distribution and dispersal of pikas: consequences of insular population structure. Ecology 55:1112–1119Article 

    Google Scholar 
    Smith AT (2020) Conservation status of American pikas (Ochotona princeps). J Mammal 101:1466–1488Article 

    Google Scholar 
    Smith AT, Ivins BL (1983) Colonization in a pika population: dispersal vs philopatry. Behav Ecol Sociobiol 13:37–47Article 

    Google Scholar 
    Smith AT, Weston ML (1990) Ochotona princeps. Mamm Species 352:1–8.Article 

    Google Scholar 
    Smith AT, Millar CI (2018) American pika (Ochotona princeps) population survival in winters with low or no snowpack. West North Am Nat 78:126–132Article 

    Google Scholar 
    La Sorte FA, Jetz W (2010) Projected range contractions of montane biodiversity under global warming. Proc R Soc B Biol Sci 277:3401–3410Article 

    Google Scholar 
    Stern DL (2013) The genetic causes of convergent evolution. Nat Rev Genet 14:751–764CAS 
    PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    Stewart JAE, Wright DH, Heckman KA (2017) Apparent climate-mediated loss and fragmentation of core habitat of the American pika in the Northern Sierra Nevada, California, USA. PLoS One 12:e0181834PubMed 
    PubMed Central 
    Article 
    CAS 

    Google Scholar 
    Stewart JAE, Perrine JD, Nichols LB, Thorne JH, Millar CI, Goehring KE et al. (2015) Revisiting the past to foretell the future: summer temperature and habitat area predict pika extirpations in California. J Biogeogr 42:880–890Article 

    Google Scholar 
    Varner J, Dearing MD (2014) The importance of biologically relevant microclimates in habitat suitability assessments. PLoS One 9:e104648PubMed 
    PubMed Central 
    Article 
    CAS 

    Google Scholar 
    Waldvogel A-M, Feldmeyer B, Rolshausen G, Exposito‐Alonso M, Rellstab C, Kofler R et al. (2020) Evolutionary genomics can improve prediction of species’ responses to climate change. Evol Lett 4:4–18PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Wang T, Hamann A, Spittlehouse DL, Murdock TQ (2012) ClimateWNA—high-resolution spatial climate data for Western North America. J Appl Meteorol Climatol 51:16–29Article 

    Google Scholar 
    Waterhouse MD, Erb LP, Beever EA, Russello MA (2018) Adaptive population divergence and directional gene flow across steep elevational gradients in a climate-sensitive mammal. Mol Ecol 27:2512–2528PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    Weir BS, Cockerham CC (1984) Estimating F-statistics for the analysis of population structure. Evolution 38:1358–1370CAS 
    PubMed 

    Google Scholar 
    Wiens JJ (2016) Climate-related local extinctions are already widespread among plant and animal species. PLoS Biol 14:e2001104PubMed 
    PubMed Central 
    Article 
    CAS 

    Google Scholar 
    Wilkening JL, Ray C (2016) Characterizing predictors of survival in the American pika (Ochotona princeps). J Mammal 97:1366–1375Article 

    Google Scholar 
    Wilkening JL, Ray C, Beever EA, Brussard PF (2011) Modeling contemporary range retraction in Great Basin pikas (Ochotona princeps) using data on microclimate and microhabitat. Quat Int 235:77–88Article 

    Google Scholar 
    Wilson GA, Rannala B (2003) Bayesian inference of recent migration rates using multilocus genotypes. Genetics 163:1177–1191PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Wogan GOU, Wang IJ (2018) The value of space-for-time substitution for studying fine-scale microevolutionary processes. Ecography 41:1456–1468Article 

    Google Scholar 
    Yandow LH, Chalfoun AD, Doak DF (2015) Climate tolerances and habitat requirements jointly shape the elevational distribution of the American pika (Ochotona princeps), with implications for climate change effects. PLoS One 10:e0131082PubMed 
    PubMed Central 
    Article 
    CAS 

    Google Scholar 
    Zgurski JM, Hik DS (2012) Polygynandry and even-sexed dispersal in a population of collared pikas, Ochotona collaris. Anim Behav 83:1075–1082Article 

    Google Scholar 
    Zhang D, Pan J, Cao J, Cao Y, Zhou H (2020) Screening of drought-resistance related genes and analysis of promising regulatory pathway in camel renal medulla. Genomics 112:2633–2639CAS 
    PubMed 
    Article 

    Google Scholar 
    Zhang Z, Schwartz S, Wagner L, Miller W (2000) A greedy algorithm for aligning DNA sequences. J Comput Biol 7:203–214CAS 
    PubMed 
    Article 

    Google Scholar  More