More stories

  • in

    First molecular examination of Vietnamese mudflat snails in the genus Naranjia Golding, Ponder & Byrne, 2007 (Gastropoda: Amphibolidae)

    1.
    IUCN. A Study on Aid to the Environment Sector in Vietnam. (Ministry of Planning and Investment and UNDP, 1999).
    2.
    Myers, N. et al. Biodiversity hotspots for conservation priorities. Nature 403, 853–858 (2000).
    ADS  CAS  Article  Google Scholar 

    3.
    Sterling, E. J. & Hurley, M. M. Conserving biodiversity in Vietnam: applying biogeography to conservation research. Proc. Calif. Acad. Sci. 56, Supplement I, No.9, 98–118 (2005).

    4.
    Thuoc, P. & Long, N. Overview of the coastal fisheries of Vietnam. In Status and Management of Tropical Coastal Fisheries in Asia. (eds. Silvestre, G. & Pauly, D.) 96–106 (ICLARM, 1997).

    5.
    Ng, P. K. L. & Tan, K. S. The state of marine biodiversity in the South China Sea. Raffles Bull. Zool. (Supplement No. 8), 3–7 (2000).

    6.
    Burke, L., Selig, E. & Spalding, M. Reefs at Risk in Southeast Asia. (Resource for the Future, 2002).

    7.
    Benthem, W., van Lavieren, L. P. & Verheugt, W. J. M. Mangrove rehabilitation in the costal Mekong Delta, Vietnam. In An International Perspective on Wetland Rehabilitation. (ed. Streever, W.) 29–36 (Springer, 1999).

    8.
    McNally, R., McEwin, A. & Holland, T. The Potential for Mangrove Carbon Projects in Vietnam. (Netherlands Development Organization [SNV], 2011).

    9.
    Veettil, B. K. et al. Mangroves of Vietnam: historical development, current state of research and future threats. Estuar. Coast. Shelf Sci. 218, 212–236 (2019).
    ADS  Article  Google Scholar 

    10.
    Duke, N. C. Mangroves of the Kien Giang Biosphere Reserve Viet Nam. (Deutsche Gesellschaft fur International Zusammenarbeit GmbH, 2012).

    11.
    Cuong, C. V., Russell, M., Brown, S. & Dart, P. Using shoreline video assessment for coastal planning and restoration in the context of climate change in Kien Giang, Vietnam. Ocean Sci. J. 50, 413–432 (2015).
    ADS  Article  Google Scholar 

    12.
    Nguyen, T. P., Luom, T. T. & Parnell, K. E. Mangrove allocation for coastal protection and livelihood improvement in Kien Giang Province, Vietnam: constraints and recommendations. Land Use Policy 63, 401–407 (2017).
    Article  Google Scholar 

    13.
    Tue, N. T. et al. Food sources of macro-invertebrates in an important mangrove ecosystem of Vietnam determined by dual stable isotope signatures. J. Sea Res. 72, 14–21 (2012).
    ADS  Article  Google Scholar 

    14.
    Thanh, N. V. et al. The Zoobenthos of the Can Gio Mangrove Ecosystem (Publishing House for Science and Technology, 2013).

    15.
    Zvonareva, S. & Kantor, Y. Checklist of gastropod molluscs in mangroves of Khanh Hoa province, Vietnam. Zootaxa 4162, 401–437 (2016).
    Article  Google Scholar 

    16.
    Thach, N. N. Shells of Vietnam (Conchbooks, 2005).

    17.
    Thach, N. N. Recently Collected Shells of Vietnam (L’Informatore Piceno, 2007).

    18.
    Thach, N. N. New Shells of Southeast Asia. Sea Shells & Land Snails (48HrBooks Company, 2017).

    19.
    Raven, H. & Vermeulen, J. J. Notes on molluscs from NW Borneo and Singapore. 2. A synopsis of the Ellobiidae (Gastropoda, Pulmonata). Vita Malacol. 4, 29–62 (2007).
    Google Scholar 

    20.
    Lutaenko, K. A., Prozorova, L. A., Ngo, X. Q. & Bogatov, V. V. First reliable record of Mytilopsis sallei (Récluz, 1849) (Bivalvia: Dreissenidae) in Vietnam. Korean J. Malacol. 35, 355–360 (2019).
    Google Scholar 

    21.
    Prozorova, L. A. et al. Mangrove mollusk fauna of the Kien Giang Province in the Mekong River delta (South Vietnam). In The 1st International Conference on North East Asia Biodiversity, Vol. 1, 67 (2018a).

    22.
    Prozorova, L. A. et al. New for the Mekong Delta and Vietnam fauna mollusk families. In The 1st International Conference on North East Asia Biodiversity Vol. 1, 65–66 (2018b).

    23.
    Prendergast, J. R. et al. Rare species, the coincidence of diversity hotspots and conservation strategies. Nature 365, 335–337 (1993).
    ADS  Article  Google Scholar 

    24.
    Raphael, M. G. & Molina, R. Conservation of Rare or Little-known Species: Biological, Social, and Economic Considerations (Island Press, Washington, D.C., 2013).
    Google Scholar 

    25.
    Mouillot, D. et al. Rare species support vulnerable functions in high-diversity ecosystems. PLoS Biol. 11, e1001569 (2013).
    CAS  Article  Google Scholar 

    26.
    Golding, R. E. Molecular phylogenetic analysis of mudflat snails (Gastropoda: Euthyneura: Amphiboloidea) supports an Australasian centre of origin. Mol. Phylogenet. Evol. 63, 72–81 (2012).
    Article  Google Scholar 

    27.
    Thach, N. N. Vietnamese New Mollusks. Seashells-Land Snails-Cephalopods, with 59 New Species. (Thach, N. N., 2016).

    28.
    Golding, R. E., Ponder, W. F. & Byrne, M. Taxonomy and anatomy of Amphiboloidea (Gastropoda: Heterobranchia: Archaeopulmonata). Zootaxa 1476, 1–50 (2007).
    Article  Google Scholar 

    29.
    Golding, R. E., Byrne, M. & Ponder, W. F. Novel copulatory structures and reproductive functions in Amphiboloidea (Gastropoda, Heterobranchia, Pulmonata). Invertebr. Biol. 127, 168–180 (2008).
    Article  Google Scholar 

    30.
    Davis, G. M. Mollusks as indicators of the effects of herbicides on mangroves in South Vietnam. In The Effects of Herbicides in South Vietnam: Part B, Working papers. (ed. National Academy of Sciences, National Research Council) 1–29 (National Academy of Sciences, National Research Council, 1974).

    31.
    Academy of Natural Sciences. MAL. Occurrence dataset. GBIF. https://doi.org/10.15468/xp1dhx (2019a).

    32.
    Academy of Natural Sciences. ANSP Malacology Collection. The Academy of Natural Sciences, Philadelphia. https://clade.ansp.org/malacology/collections/index.html (2019b).

    33.
    Akiba, M. & Sasaki, T. Mollusca specimens of Ryukyu University Museum (Fujukan). Version 1.1. National Museum of Nature and Science, Japan. GBIF. https://doi.org/10.15468/qgmdhb (2019).

    34.
    Creuwels, J. Naturalis Biodiversity Center (NL) – Mollusca. Naturalis Biodiversity Center. GBIF. https://doi.org/10.15468/yefvnk (2019).

    35.
    Edgar, R. C. MUSCLE: multiple sequence alignment with high accuracy and high throughput. Nucleic Acids Res. 32, 1792–1797 (2004).
    CAS  Article  Google Scholar 

    36.
    Capella-Gutiérrez, S., Silla-Martínez, J. M. & Gabaldón, T. trimAl: a tool for automated alignment trimming in large-scale phylogenetic analyses. Bioinformatics 25, 1972–1973 (2009).
    Article  Google Scholar 

    37.
    Lanfear, R. et al. PartitionFinder 2: new methods for selecting partitioned models of evolution for molecular and morphological phylogenetic analyses. Mol. Biol. Evol. 34, 772–773 (2017).
    CAS  PubMed  Google Scholar 

    38.
    Tanabe, A. S. Phylogears version 2.2.2012.02.13. 2012. Life is fifthdimension. https://www.fifthdimension.jp/ (2012).

    39.
    Ronquist, F. & Huelsenbeck, J. P. MRBAYES 3: Bayesian phylogenetic inference under mixed models. Bioinformatics 19, 1572–1574 (2003).
    CAS  Article  Google Scholar 

    40.
    Nguyen, L. T., Schmidt, H. A., von Haeseler, A. & Minh, B. Q. IQ-TREE: a fast and effective stochastic algorithm for estimating maximum-likelihood phylogenies. Mol. Biol. Evol. 32, 268–274 (2015).
    CAS  Article  Google Scholar 

    41.
    Rambaut, A., Drummond, A. J. & Suchard, M. Tracer v1.6. Molecular evolution, phylogenetics and epidemiology. https://tree.bio.ed.ac.uk/software/tracer/ (2013).

    42.
    Minh, B. Q., Nguyen, M. A. T. & von Haeseler, A. Ultrafast approximation for phylogenetic bootstrap. Mol. Biol. Evol. 30, 1188–1195 (2013).
    CAS  Article  Google Scholar 

    43.
    Tamura, K., Stecher, G., Peterson, D., Filipski, A. & Kumar, S. MEGA6: molecular evolutionary genetics analysis version 6.0. Mol. Biol. Evol. 30, 2725–2729 (2013).
    CAS  Article  Google Scholar 

    44.
    Kuroda, T. Two families new to the Molluscan fauna of Japan. Venus 1, 10–15 (1928).
    Google Scholar 

    45.
    GADM. GADM database, version 3.4. GDAM maps and data. https://gadm.org/index.html (2018).

    46.
    QGIS development team. QGIS geographic information system version 2.18, open source geospatial foundation project. QGIS. https://qgis.osgeo.org (2018). More

  • in

    Helminth eggs from early cretaceous faeces

    1.
    Araújo, A. et al. Invited review: Paleoparasitology—Perspectives with new techniques. Rev. Inst. Med. Trop. S. P. 40(6), 371–376 (1998).
    Article  Google Scholar 
    2.
    De Baets, K., Dentzien-Dias, P., Harrison, G. W. M., Littlewood, D. T. J. & Parry, L. A. 2020) Identification and macroevolution of parasites (topics in geobiology. In The Evolution and Fossil Record of Parasitism (eds De Baets, K. & Huntley, J.) (Springer, New York, 2020).
    Google Scholar 

    3.
    Dentzien-Dias, P. C. et al. Tapeworm eggs in a 270 million-year-old shark coprolite. PLoS ONE 8(1), e55007. https://doi.org/10.1371/journal.pone.0055007 (2013).
    ADS  CAS  Article  PubMed  PubMed Central  Google Scholar 

    4.
    Hugot, J. P. et al. Discovery of a 240 million-year-old nematode parasite egg in a cynodont coprolite sheds light on the early origin of pinworms in vertebrates. Parasite Vector 7(1), 486. https://doi.org/10.1186/s13071-014-0486-6 (2014).
    Article  Google Scholar 

    5.
    Da Silva, P. A. et al. A new ascarid species in cynodont coprolite dated of 240 million years. An. Acad. Bras. Cienc. 86(1), 265–296 (2014).
    Article  PubMed  Google Scholar 

    6.
    Cardia, D. F. F., Bertini, R. J., Camossi, L. G. & Letizio, L. A. The first record of ascaridoidea eggs discovered in crocodyliformes hosts from the upper Cretaceous of Brazil. Rev. Bras. Paleontol. 21(3), 238–244 (2018).
    Article  Google Scholar 

    7.
    Poinar, G. Jr. & Boucot, A. J. Evidence of intestinal parasites of dinosaurs. Parasitology 133(2), 245–249 (2006).
    Article  PubMed  Google Scholar 

    8.
    Beltrame, M. O., Fugassa, M. H., Barberena, R., Udrizar-Sauthier, D. E. & Sardella, N. H. New record of anoplocephalid eggs (Cestoda: Anoplocephalidae) collected from the rodent coprolites from archaeological and paleontological sites of Patagonia, Argentina. Parasitol. Int. 62, 431–434 (2013).
    Article  PubMed  Google Scholar 

    9.
    Beltrame, M. O., Tietze, E., Pérez, A. E., Bellusci, A. & Sardella, N. H. Ancient parasites from endemic deer from “Cueva Parque Diana” archeological site, Patagonia, Argentina. Parasitol. Res. 116(2), 1523–1531 (2017).
    Article  PubMed  Google Scholar 

    10.
    Fugassa, M. H., Petrigh, R. S., Fernández, P. M., Carballido Calatayud, M. & Belleli, C. Fox parasites in pre-Columbian times: Evidence from the past to understand the current helminth assemblages. Acta Trop. 185, 380–384 (2018).
    CAS  Article  PubMed  PubMed Central  Google Scholar 

    11.
    Sianto, L. et al. Helminths in feline coprolites up to 9000 years in the Brazilian Northeast. Parasitol. Int. 63, 851–857 (2014).
    Article  PubMed  PubMed Central  Google Scholar 

    12.
    Barrios-de Pedro, S. Integrative Study of the Coprolites from Las Hoyas (upper Barremian; La Huérguina Formation, Cuenca, Spain). Unpublished PhD thesis. Universidad Autónoma de Madrid (Spain) (2019).

    13.
    Poyato-Ariza, F. J. & Buscalioni, A. D. Las Hoyas: A Cretaceous Wetland (Dr. Friedrich Pfeil Verlag, München, 2016).
    Google Scholar 

    14.
    Martin, T. et al. A Cretaceous eutricondont and integument evolution in early mammals. Nature 526, 380–384 (2015).
    ADS  CAS  Article  PubMed  PubMed Central  Google Scholar 

    15.
    Iniesto, M. et al. A.I. Involvement of microbial mats in early fossilization by decay delay and formation of impressions and replicas of vertebrates and invertebrates. Sci. Rep. 6, 1–12. https://doi.org/10.1038/srep25716 (2016).
    CAS  Article  Google Scholar 

    16.
    Iniesto, M. et al. Plant tissue decay in long-term experiments with microbial mats. Geosci. J. 8(11), 387. https://doi.org/10.3390/geosciences8110387 (2018).
    ADS  CAS  Article  Google Scholar 

    17.
    Poyato-Ariza, F. J., Talbot, M. R., Fregenal-Martínez, M. A., Meléndez, N. & Wenz, S. First isotopic and multidisciplinary evidence for nonmarine coelacanths and pycnodontiform fishes: Palaeoenvironmental implications. Palaeogeogr. Palaeoclimatol. Palaeoecol. 144, 64–84 (1998).
    Article  Google Scholar 

    18.
    Buscalioni, A. D. & Fregenal-Martínez, M. A. A holistic approach to the palaeoecology of Las Hoyas Konservat-Lagerstätte (La Huérguina Formation, Lower Cretaceous, Iberian ranges, Spain). J. Iber. Geol. 36(2), 297–326 (2010).
    Article  Google Scholar 

    19.
    Fregenal-Martínez, M. A., Meléndez, N., Muñoz-García, M. B., Elez, J. & de la Horra, R. The stratigraphic record of the late Jurassic-early Cretaceous rifting in the Alto Tajo-Serranía de Cuenca region (Iberian Ranges, Spain): Genetic and structural evidences for a revision and a new lithostratigraphic proposal. Rev. Soc. Geol. Esp. 30(1), 113–142 (2017).
    Google Scholar 

    20.
    Buscalioni, A. D. et al. The wetlands of Las Hoyas. In Las Hoyas: A Cretaceous Wetland (eds Poyato-Ariza, F. J. & Buscalioni, A. D.) 238–253 (Dr. Friedrich Pfeil Verlag, München, 2016).
    Google Scholar 

    21.
    Timm, T., Vinn, O. & Buscalioni, A. D. Soft-bodied annelids (Oligochaeta) from the lower Cretaceous (La Huerguina formation) of the Las Hoyas Konservat-Lagerstätte, Spain. Neues. Jahrb. Geol. P.-A. 280(3), 315–324 (2016).
    Article  Google Scholar 

    22.
    Buatois, L. A., Fregenal-Martínez, M. A. & de Gibert, J. M. Short-term colonization trace-fossil assemblages in a carbonate lacustrine Konservat-Lagerstätte (Las Hoyas fossil site, Lower Cretaceous, Cuenca, centra Spain). Facies 43, 145–156 (2000).
    Article  Google Scholar 

    23.
    de Gibert, J. M., Moratalla, J. J., Mángano, M. G. & Buatois, L. A. Ichnoassemblage (trace fossils). In Las Hoyas: A Cretaceous Wetland (eds Poyato-Ariza, F. J. & Buscalioni, A. D.) 195–201 (Dr. Friedrich Pfeil Verlag, München, 2016).
    Google Scholar 

    24.
    Barrios-de-Pedro, S., Poyato-Ariza, F. J., Moratalla, J. J. & Buscalioni, A. D. Exceptional coprolite association from the early Cretaceous continental Lagerstätte of Las Hoyas, Cuenca, Spain. PLoS ONE 13(5), E0196982. https://doi.org/10.1371/journal.pone.0196982 (2018).
    CAS  Article  PubMed  PubMed Central  Google Scholar 

    25.
    Barrios-de Pedro, S., Chin, K. & Buscalioni, A. D. The late Barremian ecosystem of Las Hoyas sustained by fishes and shrimps as inferred from coprofabrics. Cretac. Res. 110, 104409. https://doi.org/10.1016/j.cretres.2020.104409 (2020).
    Article  Google Scholar 

    26.
    Poyato-Ariza, F. J. & Martín-Abad, H. Osteichthyan fishes. In Las Hoyas: A Cretaceous Wetland (eds Poyato-Ariza, F. J. & Buscalioni, A. D.) 114–132 (Dr. Friedrich Pfeil Verlag, München, 2016).
    Google Scholar 

    27.
    de Gibert, J. M. et al. The fish trace fossil Undichnafrom the Cretaceous of Spain. Paleontol. 42, 409–427 (2003).
    Article  Google Scholar 

    28.
    Sprent, J. F. A. Ascaridoid nematodes of amphibians and reptiles: Dujardinascaris. Supplementary review article. J. Helminthol. 51, 251–285 (1977).
    Google Scholar 

    29.
    Foreyt, W. J. Veterinary Parasitology. Reference Manual (Blackwell Publishing Profesional, Iowa, 2001).
    Google Scholar 

    30.
    Sullivan, T. A Color Atlas of Parasitology (University of San Francisco, San Francisco, 2004).
    Google Scholar 

    31.
    Rajesh, N. V., Kalpana Devi, R., Jayathangaraj, M. G., Raman, M. & Sridhar, R. Intestinal parasites in captive mugger crocodiles (Crocodylus palustris) in south India. J. Trop. Med. Parasit. 37(2), 69–73 (2014).
    Google Scholar 

    32.
    King, S. & Scholz, T. Trematodes of the family Opisthorchiidae: A minireview, Korean. J. Parasitol. 39(3), 209–221 (2001).
    CAS  Google Scholar 

    33.
    Olsen, O. W. Animal Parasites: Their Life Cycles and Ecology 3rd edn. (University Park Press, Baltimore, London, 1974).
    Google Scholar 

    34.
    Chen, T. C. General Parasitology 2nd edn. (Academic Press Inc., Florida, 1986).
    Google Scholar 

    35.
    Gegenbaur, C. Gundriss der Vergleichenden Anatomie (Wilhelm Engelmann, Leipzig, 1859).
    Google Scholar 

    36.
    Rudolphi, C. A. Entozoorum Sive Vermium Intesstinalium (Historia Naturalis, Amsterdam, 1808).
    Google Scholar 

    37.
    Yamaguti, S. The Digenetic-Trematodes of Vertebrates Volume I (Parts 1 and 2) (Interscience Publisjers Inc., New York, 1958).
    Google Scholar 

    38.
    Ditrich, O., Giboda, M., Scholz, T. & Beer, S. A. Comparative morphology of eggs of the Haplorchiinae (Trematoda: Heterophyidae) and some other medically important heterophyid and opisthorchiid flukes. Folia. Parasit. 39, 123–132 (1992).
    CAS  Google Scholar 

    39.
    Cobb, N. A. The english word “nema”. J. A. M. A. 98, 75 (1932).
    Google Scholar 

    40.
    Skrjabin, K. I. & Karokhin, V. I. On the rearrangement of nematodes of the order Ascaridata Skrjabin, 1915. Dokl. Akad. Nauk. Soiuza. Sov. Sotsialisticheskikh. Resp. 48(4), 297–299 (1945).
    CAS  PubMed  PubMed Central  Google Scholar 

    41.
    Ubelaker, J. E. & Allison, V. F. Scanning electron microscopy of the eggs of Ascaris lumbricoides, A. suum, Toxocara canis, and T. mystax. J. Parasitol. 61(5), 802–807 (1975).
    CAS  Article  PubMed  Google Scholar 

    42.
    Dujardin, F. Histoire Naturelle des Helminthes ou Vers Intestinaux (Librairie Encyclopedique de Roret, Paris, 1845).
    Google Scholar 

    43.
    Cardoso, A. M. C., de Souza, A. J. S., Menezes, R. C., Pereira, W. L. A. & Tortelly, R. Gastric lesions in free-ranging black caimans (Melanosuchus niger) associated with Brevimulticaecum species. Vet. Pathol. 50(4), 582–584 (2013).
    CAS  Article  PubMed  Google Scholar 

    44.
    Tellez, M. & Nifong, J. Gastric nematode diversity between estuarine and inland freshwater populations of the American alligator (Alligator mississippienses, daudin 1802), and the prediction of intermediate hosts. Int. J. Parasitol.-Par. 3, 227–235 (2014).
    Article  Google Scholar 

    45.
    Villegas, A. & González-Solís, D. Gastrointestinal helminth parasites of the American crocodile (Crocodylus Acutus) in southern Quintana, Roo, Mexico. Herpetol. Conserv. Biol. 4(3), 346–351 (2009).
    Google Scholar 

    46.
    Cardia, D. F. F., Bertini, R. J., Camossi, L. G. & Letizio, L. A. First record of Acanthocephala parasites eggs in coprolites preliminary assigned to Crocodyliformes from the Adamantina Formation (Bauru Group, upper Cretaceous), Sao Paulo, Brazil. An. Acad. Bras. Cienc. 91(2), e20170848. https://doi.org/10.1590/0001-3765201920170848 (2019).
    Article  Google Scholar 

    47.
    Qvarnström, M., Niedźwiedzki, G. & Žigaitė, Ž. Vertebrate coprolites (fossil faeces): An underexplored Konservat-Lagerstätte. Earth Sci. Rev. 162, 44–57 (2016).
    ADS  Article  Google Scholar 

    48.
    Uddin, M. H., Bae, Y. M., Choi, M. H. & Hong, S. T. Production and deformation of Clonorchis sinensis eggs during in vitro maintenance. PLoS ONE 7(12), e52676. https://doi.org/10.1371/journal.pone.0052676 (2012).
    ADS  CAS  Article  PubMed  PubMed Central  Google Scholar 

    49.
    Grobbelaar, A., Van As, L. L., Butler, H. J. B. & Van As, J. G. Ecology of Diplostomid (Trematoda: Digenea) infection in freshwater fish in Southern Africa. Afr. Zool. 49(2), 222–232 (2014).
    Article  Google Scholar 

    50.
    Schell, S. C. How to Know the Trematodes (William C. Brown Company Publishers, Iowa, 1970).
    Google Scholar 

    51.
    McConnaughey, M. Life Cycle of Parasites. Reference Module in Biomedical Sciences (Elsevier, Amsterdam, 2014).
    Google Scholar 

    52.
    Tsubokawa, D. et al. Collection methods of trematode eggs using experimental animal models. Parasitol. Int. 65, 584–587 (2016).
    Article  PubMed  Google Scholar 

    53.
    Wolf, D. et al. Diagnosis of gastrointestinal parasites in reptiles: Comparison of two coprological methods. Acta. Vet. Scand. 56(1), 44. https://doi.org/10.1186/s13028-014-0044-4 (2014).
    Article  PubMed  PubMed Central  Google Scholar 

    54.
    Mehlhorn, H. Encyclopedia of Parasitology (Springer, Berlin, 2016).
    Google Scholar 

    55.
    Dai, W. et al. Phylogenomic perspective on the relationships and evolutionary history of the major otocephalan lineages. Sci. Rep. 8, 205. https://doi.org/10.1038/s41598-017-18432-5 (2018).
    ADS  CAS  Article  PubMed  PubMed Central  Google Scholar 

    56.
    Kappas, I., Vittas, S., Pantzartzi, C. N., Drosopoulou, E. & Scouras, Z. G. A time-calibrated mitogenome phylogeny of catfish (Teleostei: Siluriformes). PLoS ONE 11(12), E0166988. https://doi.org/10.1371/journal.pone.0166988 (2016).
    CAS  Article  PubMed  PubMed Central  Google Scholar 

    57.
    Anderson, R. C. Nematode Parasites of Vertebrates: Their Development and Transmission 2nd edn. (CABI Publishing, Wallingford, 2000).
    Google Scholar 

    58.
    Valles-Vega, I., Molina-Fernández, D., Benítez, R., Hernández-Trujillo, S. & Adroher, F. J. Early development and life cycle of Contracaecum multipapillatum s.l. from a brown pelican Pelecanus occidentalis in the Gulf of California, Mexico. Dis. Aquat. Organ. 125, 167–178 (2017).
    CAS  Article  PubMed  PubMed Central  Google Scholar 

    59.
    Klimpel, S., Palm, H. W., Rückert, S. & Piatkowski, U. The life cycle of Anisakis simplex in the Norwegian Deep (northern North Sea). Parasitol. Res. 94(1), 1–9 (2004).
    Article  PubMed  PubMed Central  Google Scholar 

    60.
    Cardia, D. F. F., Bertini, R. J., Camossi, L. G. & Letizio, L. A. Two new species of ascaridoid nematodes in Brazilian Crocodylomorpha from the upper Cretaceous. Parasitol. Int. 72, 101947. https://doi.org/10.1016/j.parint.2019.101947 (2019).
    Article  PubMed  PubMed Central  Google Scholar 

    61.
    Buscalioni, A. D. & Chamero, B. Crocodylomorpha. In Las Hoyas: A Cretaceous Wetland (eds Poyato-Ariza, F. J. & Buscalioni, A. D.) 162–169 (Dr. Friedrich Pfeil Verlag, München, 2016).
    Google Scholar 

    62.
    Esch, G. W., Barger, M. A. & Fellis, K. J. The transmission of digenetic trematodes: Style, elegance, complexity. Integr. Comp. Biol. 42, 304–312 (2002).
    Article  PubMed  Google Scholar 

    63.
    Delvene, G. & Clive Munt, M. Mollusca. In Las Hoyas: A Cretaceous Wetland (eds Poyato-Ariza, F. J. & Buscalioni, A. D.) 57–63 (Dr. Friedrich Pfeil Verlag, München, 2016).
    Google Scholar 

    64.
    Delclós, X. & Soriano, C. Insecta. In Las Hoyas: A Cretaceous Wetland (eds Poyato-Ariza, F. J. & Buscalioni, A. D.) 70–88 (Dr. Friedrich Pfeil Verlag, München, 2016).
    Google Scholar 

    65.
    Garassino, A. Decapoda. In Las Hoyas: A Cretaceous Wetland (eds Poyato-Ariza, F. J. & Buscalioni, A. D.) 98–102 (Dr. Friedrich Pfeil Verlag, München, 2016).
    Google Scholar 

    66.
    Heimhofer, U. et al. Deciphering the depositional environment of the laminated Crato fossil beds (early Cretaceous, Araripe Basin, North-eastern Brazil. Sedimentology 57(2), 677–694 (2010).
    ADS  CAS  Article  Google Scholar 

    67.
    de Gibert, J. M., Fregenal-Martínez, M. A., Buatois, L. A. & Mángano, M. G. Trace fossils and their palaeoecological significance in lower Cretaceous lacustrine conservation deposits, El Montsec, Spain. Palaeogeogr. Palaeoclimatol. Palaeoecol. 156, 89–101 (2000).
    Article  Google Scholar 

    68.
    Ferreira, L. F., Reinhard, K. & Araújo, A. Fundamentos da Paleoparasitología 1st edn. (Editora Fiocruz, Rio de Janeiro, 2011).
    Google Scholar 

    69.
    Ritchie, L. S. An ether sedimentation technique for routine stool examination. Bull. U. S. Army. Med. Dep. 8, 326 (1948).
    CAS  PubMed  PubMed Central  Google Scholar 

    70.
    Rasband, W.S. ImageJ. (U.S. National Institutes of Health, Bethesda, 1997–2018). https://imagej.nih.gov/ij/. More

  • in

    Characterising the effect of crop species and fertilisation treatment on root fungal communities

    1.
    Ramankutty, N. et al. Trends in global agricultural land use: Implications for environmental health and food security. Annu. Rev. Plant Biol. 69, 789–815 (2018).
    CAS  PubMed  Article  PubMed Central  Google Scholar 
    2.
    Tilman, D., Balzer, C., Hill, J. & Befort, B. L. Global food demand and the sustainable intensification of agriculture. Proc. Natl. Acad. Sci. USA 108, 20260–20264 (2011).
    ADS  CAS  PubMed  Article  PubMed Central  Google Scholar 

    3.
    Bender, S. F., Wagg, C. & van der Heijden, M. G. A. An underground revolution: Biodiversity and soil ecological engineering for agricultural sustainability. Trends Ecol. Evol. 31, 440–452 (2016).
    PubMed  Article  PubMed Central  Google Scholar 

    4.
    Schröder, P. et al. Discussion paper: Sustainable increase of crop production through improved technical strategies, breeding and adapted management—A European perspective. Sci. Total Environ. 678, 146–161 (2019).
    ADS  PubMed  Article  CAS  PubMed Central  Google Scholar 

    5.
    Bardgett, R. D., Mommer, L. & De Vries, F. T. Going underground: Root traits as drivers of ecosystem processes. Trends Ecol. Evol. 29, 692–699 (2014).
    PubMed  Article  PubMed Central  Google Scholar 

    6.
    Berendsen, R. L., Pieterse, C. M. J. & Bakker, P. A. H. M. The rhizosphere microbiome and plant health. Trends Plant Sci. 17, 478–486 (2012).
    CAS  PubMed  Article  PubMed Central  Google Scholar 

    7.
    Wissuwa, M., Mazzola, M. & Picard, C. Novel approaches in plant breeding for rhizosphere-related traits. Plant Soil 321, 409–430 (2009).
    CAS  Article  Google Scholar 

    8.
    Backer, R. et al. Plant growth-promoting rhizobacteria: Context, mechanisms of action, and roadmap to commercialization of biostimulants for sustainable agriculture. Front. Plant Sci. 871, 1–17 (2018).
    Google Scholar 

    9.
    Bulgarelli, D. et al. Structure and function of the bacterial root microbiota in wild and domesticated barley. Cell Host Microbe 17, 392–403 (2015).
    CAS  PubMed  Article  PubMed Central  Google Scholar 

    10.
    Chaparro, J. M., Badri, D. V. & Vivanco, J. M. Rhizosphere microbiome assemblage is affected by plant development. ISME J. 8, 790–803 (2014).
    CAS  PubMed  Article  PubMed Central  Google Scholar 

    11.
    Edwards, J. et al. Structure, variation, and assembly of the root-associated microbiomes of rice. Proc. Natl. Acad. Sci. USA 112, E911–E920 (2015).
    CAS  PubMed  Article  PubMed Central  Google Scholar 

    12.
    Food and Agriculture Organization of United Nations. World Food and Agriculture Statistical Workbook 2018 https://www.fao.org/3/ca1796en/ca1796en.pdf (2018).

    13.
    International Potato Centre. Annual Report 2017 https://cipotato.org/annualreport2017/ (2017).

    14.
    Busby, P. E. et al. Research priorities for harnessing plant microbiomes in sustainable agriculture. PLoS Biol. 15, 1–14 (2017).
    Article  CAS  Google Scholar 

    15.
    Lareen, A., Burton, F. & Schäfer, P. Plant root-microbe communication in shaping root microbiomes. Plant Mol. Biol. 90, 575–587 (2016).
    CAS  PubMed  Article  PubMed Central  Google Scholar 

    16.
    Adair, K. L. & Douglas, A. E. Making a microbiome: The many determinants of host-associated microbial community composition. Curr. Opin. Microbiol. 35, 23–29 (2017).
    PubMed  Article  PubMed Central  Google Scholar 

    17.
    Donn, S., Kirkegaard, J. A., Perera, G., Richardson, A. E. & Watt, M. Evolution of bacterial communities in the wheat crop rhizosphere. Environ. Microbiol. 17, 610–621 (2015).
    PubMed  Article  PubMed Central  Google Scholar 

    18.
    Grayston, S. J., Wang, S., Campbell, C. D. & Edwards, A. C. Selective influence of plant species on microbial diversity in the rhizosphere. Soil Biol. Biochem. 30, 369–378 (1998).
    CAS  Article  Google Scholar 

    19.
    Esperschütz, J., Gattinger, A., Mäder, P., Schloter, M. & Fließbach, A. Response of soil microbial biomass and community structures to conventional and organic farming systems under identical crop rotations. FEMS Microbiol. Ecol. 61, 26–37 (2007).
    PubMed  Article  CAS  PubMed Central  Google Scholar 

    20.
    Francioli, D. et al. Mineral vs. organic amendments: Microbial community structure, activity and abundance of agriculturally relevant microbes are driven by long-term fertilization strategies. Front. Microbiol. 7, 1–16 (2016).
    Article  Google Scholar 

    21.
    Lupatini, M., Korthals, G. W., de Hollander, M., Janssens, T. K. S. & Kuramae, E. E. Soil microbiome is more heterogeneous in organic than in conventional farming system. Front. Microbiol. 7, 1–13 (2017).
    Article  Google Scholar 

    22.
    Kätterer, T., Börjesson, G. & Kirchmann, H. Changes in organic carbon in topsoil and subsoil and microbial community composition caused by repeated additions of organic amendments and N fertilisation in a long-term field experiment in Sweden. Agric. Ecosyst. Environ. 189, 110–118 (2014).
    Article  Google Scholar 

    23.
    Liu, B., Tu, C., Hu, S., Gumpertz, M. & Ristaino, J. B. Effect of organic, sustainable, and conventional management strategies in grower fields on soil physical, chemical, and biological factors and the incidence of Southern blight. Appl. Soil Ecol. 37, 202–214 (2007).
    Article  Google Scholar 

    24.
    Liu, Y. et al. Direct and indirect influences of 8 year of nitrogen and phosphorus fertilisation on glomeromycota in an alpine meadow ecosystem. New Phytol. 194, 523–535 (2012).
    CAS  PubMed  Article  PubMed Central  Google Scholar 

    25.
    Liu, W. et al. Arbuscular mycorrhizal fungi in soil and roots respond differently to phosphorus inputs in an intensively managed calcareous agricultural soil. Sci. Rep. 6, 1–11 (2016).
    Article  CAS  Google Scholar 

    26.
    Beauregard, M. S. et al. Various forms of organic and inorganic P fertilizers did not negatively affect soil- and root-inhabiting AM fungi in a maize–soybean rotation system. Mycorrhiza 23, 143–154 (2013).
    CAS  PubMed  Article  PubMed Central  Google Scholar 

    27.
    Wemheuer, B., Thomas, T. & Wemheuer, F. Fungal endophyte communities of three agricultural important grass species differ in their response towards management regimes. Microorganisms 7, 37 (2019).
    CAS  Article  Google Scholar 

    28.
    Hartman, K. et al. Erratum: Correction to: Cropping practices manipulate abundance patterns of root and soil microbiome members paving the way to smart farming (Microbiome (2018) 6 1 (14)). Microbiome 6, 74 (2018).
    PubMed  Article  PubMed Central  Google Scholar 

    29.
    Estonian Weather Service. Meteorological Yearbook of Estonia 2017 https://www.ilmateenistus.ee/wp-content/uploads/2018/03/aastaraamat_2017.pdf (2018).

    30.
    De Leon, D. G. et al. Different wheat cultivars exhibit variable responses to inoculation with arbuscular mycorrhizal fungi from organic and conventional farms. PLoS ONE 15, 1–17 (2020).
    Google Scholar 

    31.
    Van Reeuwijk, L. P. Nitrogen in Procedures for soil analysis 6th edn (ed. Van Reeuwijk L. P.) (International Soil Reference and Information Centre, Wageningen, 2002).
    Google Scholar 

    32.
    Nikitin, B. A. Methods for soil humus determination. Agric.Chem. (Agrokhimya) 3, 156–158 (1999) in Russian
    Google Scholar 

    33.
    Egnér, H., Riehm, H. & Domingo, W. R. Untersuchungen über die chemische Bodenanalyse als Grundlage für die Beurteilung des Nährstoffzustandes der Böden. II. Chemische Extraktionsmethoden zur Phosphor- und Kaliumbestimmung 199–215 (The Annals of the Royal Agricultural College of Sweden, 1960) in German

    34.
    Tedersoo, L. et al. Global diversity and geography of soil fungi. Science 346, 1256688 (2014).
    PubMed  Article  CAS  PubMed Central  Google Scholar 

    35.
    Riit, T. et al. Oomycete-specific ITS primers for identification and metabarcoding. MycoKeys 14, 17–30 (2016).
    Article  Google Scholar 

    36.
    Anslan, S., Bahram, M., Hiiesalu, I. & Tedersoo, L. PipeCraft: Flexible open-source toolkit for bioinformatics analysis of custom high-throughput amplicon sequencing data. Mol. Ecol. Resour. https://doi.org/10.1111/1755-0998.12692 (2017).
    Article  PubMed  Google Scholar 

    37.
    Rognes, T., Flouri, T., Nichols, B., Quince, C. & Mahé, F. VSEARCH: A versatile open source tool for metagenomics. PeerJ 2016, 1–22 (2016).
    Google Scholar 

    38.
    Schloss, P. D. et al. Introducing mothur: Open-source, platform-independent, community-supported software for describing and comparing microbial communities. Appl. Environ. Microbiol. 75, 7537–7541 (2009).
    CAS  PubMed  Article  PubMed Central  Google Scholar 

    39.
    Abarenkov, K. et al. The UNITE database for molecular identification of fungi—Recent updates and future perspectives. New Phytol 186, 281–285 (2010).
    PubMed  Article  PubMed Central  Google Scholar 

    40.
    Bengtsson-Palme, J. et al. Improved software detection and extraction of ITS1 and ITS2 from ribosomal ITS sequences of fungi and other eukaryotes for analysis of environmental sequencing data. Methods Ecol. Evol. 4, 914–919 (2013).
    Google Scholar 

    41.
    Fu, L., Niu, B., Zhu, Z., Wu, S. & Li, W. CD-HIT: Accelerated for clustering the next-generation sequencing data. Bioinformatics 28, 3150–3152 (2012).
    CAS  PubMed  Article  PubMed Central  Google Scholar 

    42.
    Camacho, C. et al. BLAST+: Architecture and applications. BMC Bioinform. 10, 1–9 (2009).
    Article  CAS  Google Scholar 

    43.
    Nguyen, N. H. et al. FUNGuild: An open annotation tool for parsing fungal community datasets by ecological guild. Fungal Ecol. 20, 241–248 (2016).
    Article  Google Scholar 

    44.
    Agrios, G. N. In Plant Pathology 5th edn (ed. Agrios, G. N.) (Elsevier Academic Press, Amsterdam, 2005).

    45.
    Jensen, B., Lübeck, P. S. & Jørgensen, H. J. L. Clonostachys rosea reduces spot blotch in barley by inhibiting prepenetration growth and sporulation of Bipolaris sorokiniana without inducing resistance. Pest Manag. Sci. 72, 2231–2239 (2016).
    CAS  PubMed  Article  PubMed Central  Google Scholar 

    46.
    Knudsen, I. M. B., Hockehull, J. & Jensen, D. N. Biocontrol of seedling diseases of barley and wheat caused by Fusarium culmorum and Bipolaris sorokiniana: Effects of selected fungal antagonists on growth and yield components. Plant Pathol 44, 467–477 (1995).
    Article  Google Scholar 

    47.
    Bálint, M. et al. Millions of reads, thousands of taxa: Microbial community structure and associations analyzed via marker genesa. FEMS Microbiol. Rev. 40, 686–700 (2016).
    PubMed  Article  CAS  PubMed Central  Google Scholar 

    48.
    Clarke, K. R. & Gorley, R. N. PRIMERv7: User Manual/Tutorial (PRIMER-E, Plymouth, 2015).
    Google Scholar 

    49.
    Anderson, M. J., Gorley, R. N. & Clarke, K. R. PERMANOVA+ for PRIMER: Guide to Software and Statistical Methods 1–214 (PRIMER-E, Plymouth, 2008).
    Google Scholar 

    50.
    Anderson, M. J. & Willis, T. J. Canonical analysis of principal coordinates: A useful method of constrained ordination for ecology. Ecology 84, 511–525 (2003).
    Article  Google Scholar 

    51.
    Anderson, M. J., Ellingsen, K. E. & McArdle, B. H. Multivariate dispersion as a measure of beta diversity. Ecol. Lett. 9, 683–693 (2006).
    PubMed  Article  PubMed Central  Google Scholar 

    52.
    McArdle, B. H. & Anderson, M. J. Fitting multivariate models to community data. Ecology 82, 290–297 (2001).
    Article  Google Scholar 

    53.
    Broeckling, C. D., Broz, A. K., Bergelson, J., Manter, D. K. & Vivanco, J. M. Root exudates regulate soil fungal community composition and diversity. Appl. Environ. Microbiol. 74, 738–744 (2008).
    CAS  PubMed  Article  PubMed Central  Google Scholar 

    54.
    Hu, L. et al. Root exudate metabolites drive plant–soil feedbacks on growth and defense by shaping the rhizosphere microbiota. Nat. Commun. 9, 1–13 (2018).
    ADS  Article  CAS  Google Scholar 

    55.
    Badri, D. V. & Vivanco, J. M. Regulation and function of root exudates. Plant Cell Environ. 32, 666–681 (2009).
    CAS  PubMed  Article  PubMed Central  Google Scholar 

    56.
    Emmett, B. D., Youngblut, N. D., Buckley, D. H. & Drinkwater, L. E. Plant phylogeny and life history shape rhizosphere bacterial microbiome of summer annuals in an agricultural field. Front. Microbiol. 8, 1–16 (2017).
    Article  Google Scholar 

    57.
    Hawes, M. C., Gunawardena, U., Miyasaka, S. & Zhao, X. The role of root border cells in plant defense. Trends Plant Sci. 5, 128–133 (2000).
    CAS  PubMed  Article  PubMed Central  Google Scholar 

    58.
    Hawes, M. C., Bengough, G., Cassab, G. & Ponce, G. Root caps and rhizosphere. J. Plant Growth Regul. 21, 352–367 (2002).
    CAS  Article  Google Scholar 

    59.
    Koroney, A. S. et al. Root exudate of Solanum tuberosum is enriched in galactose-containing molecules and impacts the growth of pectobacterium atrosepticum. Ann. Bot. 118, 797–808 (2016).
    CAS  PubMed  Article  PubMed Central  Google Scholar 

    60.
    Moody, S. F., Clarke, A. E. & Bacic, A. Structural analysis of secreted slime from wheat and cowpea roots. Phytochemistry 27, 2857–2861 (1988).
    CAS  Article  Google Scholar 

    61.
    Wang, Q., Wang, N., Wang, Y., Wang, Q. & Duan, B. Differences in root-associated bacterial communities among fine root branching orders of poplar (Populus × euramericana (Dode) Guinier.). Plant Soil 421, 123–135 (2017).
    CAS  Article  Google Scholar 

    62.
    Tedersoo, L., Mett, M., Ishida, T. A. & Bahram, M. Phylogenetic relationships among host plants explain differences in fungal species richness and community composition in ectomycorrhizal symbiosis. New Phytol. 199, 822–831 (2013).
    PubMed  Article  PubMed Central  Google Scholar 

    63.
    Rich, S. M. & Watt, M. Soil conditions and cereal root system architecture: Review and considerations for linking Darwin and Weaver. J. Exp. Bot. 64, 1193–1208 (2013).
    CAS  PubMed  Article  PubMed Central  Google Scholar 

    64.
    Watt, M., Magee, L. J. & McCully, M. E. Types, structure and potential for axial water flow in the deepest roots of field-grown cereals. New Phytol. 178, 135–146 (2008).
    PubMed  Article  PubMed Central  Google Scholar 

    65.
    Watt, M., Schneebeli, K., Dong, P. & Wilson, I. W. The shoot and root growth of Brachypodium and its potential as a model for wheat and other cereal crops. Funct. Plant Biol. 36, 960–969 (2009).
    PubMed  Article  PubMed Central  Google Scholar 

    66.
    Yamaguchi, J. Measurement of root diameter in field-grown crops under a microscope without washing. Soil Sci. Plant Nutr. 48, 625–629 (2002).
    Article  Google Scholar 

    67.
    Yamaguchi, J., Tanaka, A. & Tanaka, A. Quantitative observation on the root system of various crops growing in the field. Soil Sci. Plant Nutr. 36, 483–493 (1990).
    Article  Google Scholar 

    68.
    Detheridge, A. P. et al. The legacy effect of cover crops on soil fungal populations in a cereal rotation. Agric. Ecosyst. Environ. 228, 49–61 (2016).
    Article  Google Scholar 

    69.
    Tedersoo, L. et al. Tree diversity and species identity effects on soil fungi, protists and animals are context dependent. ISME J. 10, 346–362 (2016).
    CAS  PubMed  Article  PubMed Central  Google Scholar 

    70.
    Chen, M. et al. Soil eukaryotic microorganism succession as affected by continuous cropping of peanut—Pathogenic and beneficial fungi were selected. PLoS ONE 7, e40659 (2012).
    ADS  CAS  PubMed  Article  PubMed Central  Google Scholar 

    71.
    Song, X., Pan, Y., Li, L., Wu, X. & Wang, Y. Composition and diversity of rhizosphere fungal community in Coptis chinensis Franch. Continuous cropping fields. PLoS ONE 13, 1–14 (2018).
    Google Scholar 

    72.
    Bennett, A. J., Bending, G. D., Chandler, D., Hilton, S. & Mills, P. Meeting the demand for crop production: The challenge of yield decline in crops grown in short rotations. Biol. Rev. 87, 52–71 (2012).
    PubMed  Article  PubMed Central  Google Scholar 

    73.
    Öpik, M., Moora, M., Liira, J. & Zobel, M. Composition of root-colonizing arbuscular mycorrhizal fungal communities in different ecosystems around the globe. J. Ecol. 94, 778–790 (2006).
    Article  Google Scholar 

    74.
    Sýkorová, Z., Wiemken, A. & Redecker, D. Cooccurring Gentiana verna and Gentiana acaulis and their neighboring plants in two Swiss upper montane meadows harbor distinct arbuscular mycorrhizal fungal communities. Appl. Environ. Microbiol. 73, 5426–5434 (2007).
    PubMed  Article  CAS  PubMed Central  Google Scholar 

    75.
    Francioli, D. et al. Plant functional group drives the community structure of saprophytic fungi in a grassland biodiversity experiment. Plant Soil https://doi.org/10.1007/s11104-020-04454-y (2020).
    Article  Google Scholar 

    76.
    Mariotte, P. et al. Plant–soil feedback: Bridging natural and agricultural sciences. Trends Ecol. Evol. 33, 129–142 (2018).
    PubMed  Article  PubMed Central  Google Scholar 

    77.
    Banerjee, S. et al. Agricultural intensification reduces microbial network complexity and the abundance of keystone taxa in roots. ISME J. 13, 1722–1736 (2019).
    PubMed  Article  PubMed Central  Google Scholar 

    78.
    Paungfoo-Lonhienne, C. et al. Nitrogen fertilizer dose alters fungal communities in sugarcane soil and rhizosphere. Sci. Rep. 5, 1–6 (2015).
    Article  CAS  Google Scholar 

    79.
    Rousk, J. et al. Soil bacterial and fungal communities across a pH gradient in an arable soil. ISME J. 4, 1340–1351 (2010).
    PubMed  Article  PubMed Central  Google Scholar 

    80.
    Rousk, J., Brookes, P. C. & Bååth, E. Fungal and bacterial growth responses to N fertilization and pH in the 150-year ‘Park Grass’ UK grassland experiment. FEMS Microbiol. Ecol. 76, 89–99 (2011).
    CAS  PubMed  Article  PubMed Central  Google Scholar 

    81.
    Strickland, M. S. & Rousk, J. Considering fungal: Bacterial dominance in soils—Methods, controls, and ecosystem implications. Soil Biol. Biochem. 42, 1385–1395 (2010).
    CAS  Article  Google Scholar 

    82.
    Marschner, P., Kandeler, E. & Marschner, B. Structure and function of the soil microbial community in a long-term fertilizer experiment. Soil Biol. Biochem. 35, 453–461 (2003).
    CAS  Article  Google Scholar 

    83.
    Ai, C. et al. Distinct responses of soil bacterial and fungal communities to changes in fertilization regime and crop rotation. Geoderma 319, 156–166 (2018).
    ADS  CAS  Article  Google Scholar 

    84.
    Giacometti, C. et al. Chemical and microbiological soil quality indicators and their potential to differentiate fertilization regimes in temperate agroecosystems. Appl. Soil Ecol. 64, 32–48 (2013).
    Article  Google Scholar 

    85.
    Liu, M. et al. Organic amendments with reduced chemical fertilizer promote soil microbial development and nutrient availability in a subtropical paddy field: The influence of quantity, type and application time of organic amendments. Appl. Soil. Ecol. 42, 166–175 (2009).
    Article  Google Scholar 

    86.
    Lin, X. et al. Long-term balanced fertilization decreases arbuscular mycorrhizal fungal diversity in an arable soil in north China revealed by 454 pyrosequencing. Environ. Sci. Technol. 46, 5764–5771 (2012).
    ADS  CAS  PubMed  Article  PubMed Central  Google Scholar 

    87.
    Mäder, P., Edenhofer, S., Boller, T., Wiemken, A. & Niggli, U. Arbuscular mycorrhizae in a long-term field trial comparing low-input (organic, biological) and high-input (conventional) farming systems in a crop rotation. Biol. Fertil. Soils 31, 150–156 (2000).
    Article  Google Scholar 

    88.
    Song, G. et al. Contrasting effects of long-term fertilization on the community of saprotrophic fungi and arbuscular mycorrhizal fungiin a sandy loam soil. Plant Soil Environ. 61, 127–136 (2015).
    CAS  Article  Google Scholar 

    89.
    Sun, R. et al. Fungal community composition in soils subjected to long-term chemical fertilization is most influenced by the type of organic matter. Environ. Microbiol. 18, 5137–5150 (2016).
    CAS  PubMed  Article  PubMed Central  Google Scholar 

    90.
    Setälä, H. & McLean, M. A. Decomposition rate of organic substrates in relation to the species diversity of soil saprophytic fungi. Oecologia 139, 98–107 (2004).
    ADS  PubMed  Article  PubMed Central  Google Scholar 

    91.
    van Agtmaal, M. et al. Exploring the reservoir of potential fungal plant pathogens in agricultural soil. Appl. Soil Ecol. 121, 152–160 (2017).
    Article  Google Scholar 

    92.
    Chung, Y. R., Hoitink, H. A. H. & Lipps, P. E. Interactions between organic-matter decomposition level and soilborne disease severity. Agric. Ecosyst. Environ. 24, 183–193 (1988).
    Article  Google Scholar  More

  • in

    Closely related species show species-specific environmental responses and different spatial conservation needs: Prionailurus cats in the Indian subcontinent

    1.
    Powers, R. P. & Jetz, W. Global habitat loss and extinction risk of terrestrial vertebrates under future land-use-change scenarios. Nat. Clim. Chang. 9, 323–329 (2019).
    ADS  Article  Google Scholar 
    2.
    Franklin, J. Mapping Species Distributions: Spatial Inference and Prediction (Cambridge University Press, Cambridge, 2010).
    Google Scholar 

    3.
    Zanin, M. & dos Neves, B. S. Current felid (Carnivora: Felidae) distribution, spatial bias, and occurrence predictability: testing the reliability of a global dataset for macroecological studies. Acta Oecol. 101, 103–488 (2019).
    Article  Google Scholar 

    4.
    Lomolino, M. V. & Heaney, L. R. Frontiers of Biogeography: New Directions in the Geography of Nature. (sidalc.net, 2004).

    5.
    Meyer, C., Kreft, H., Guralnick, R. & Jetz, W. Global priorities for an effective information basis of biodiversity distributions. Nat. Commun. 6, 1–8 (2015).
    Google Scholar 

    6.
    Peterson, A. T., Soberon, J. & Sanchez-Cordero, V. Conservatism of ecological niches in evolutionary time. Science 285, 1265–1267 (1999).
    CAS  PubMed  Article  PubMed Central  Google Scholar 

    7.
    Raxworthy, C. J. et al. Predicting distributions of known and unknown reptile species in Madagascar. Nature 426, 837–841 (2003).
    ADS  CAS  PubMed  Article  PubMed Central  Google Scholar 

    8.
    Hu, J. et al. Niche conservatism in Gynandropaa frogs on the southeastern Qinghai-Tibetan Plateau. Sci. Rep. 6, 32624 (2016).
    ADS  CAS  PubMed  Article  PubMed Central  Google Scholar 

    9.
    Morinière, J. et al. Phylogenetic niche conservatism explains an inverse latitudinal diversity gradient in freshwater arthropods. Sci. Rep. 6, 26340 (2016).
    ADS  PubMed  Article  CAS  PubMed Central  Google Scholar 

    10.
    Liu, H. et al. Strong phylogenetic signals and phylogenetic niche conservatism in ecophysiological traits across divergent lineages of Magnoliaceae. Sci. Rep. 5, 12246 (2015).
    ADS  CAS  PubMed  Article  PubMed Central  Google Scholar 

    11.
    Liu, H., Edwards, E. J., Freckleton, R. P. & Osborne, C. P. Phylogenetic niche conservatism in C4 grasses. Oecologia 170, 835–845 (2012).
    ADS  PubMed  Article  PubMed Central  Google Scholar 

    12.
    Crisp, M. D. et al. Phylogenetic biome conservatism on a global scale. Nature 458, 754–756 (2009).
    ADS  CAS  PubMed  Article  PubMed Central  Google Scholar 

    13.
    Lorenzen, E. D. et al. Species-specific responses of Late Quaternary megafauna to climate and humans. Nature 479, 359–364 (2011).
    ADS  CAS  PubMed  Article  PubMed Central  Google Scholar 

    14.
    Aguirre-Gutiérrez, J., Serna-Chavez, H. M., Villalobos-Arambula, A. R., de la Rosa, J. A. P. & Raes, N. Similar but not equivalent: ecological niche comparison across closely-related Mexican white pines. Div. Dist. 21, 245–257 (2014).
    Article  Google Scholar 

    15.
    Perret, D. L., Leslie, A. B. & Sax, D. F. Naturalized distributions show that climatic disequilibrium is structured by niche size in pines (Pinus L.). Glob. Ecol. Biogeogr. 28, 429–441 (2018).
    Google Scholar 

    16.
    Graham, C. H., Ron, S. R., Santos, J. C., Schneider, C. J. & Moritz, C. Integrating phylogenetics and environmental niche models to explore speciation mechanisms in dendrobatid frogs. Evolution 58, 1781–1793 (2004).
    PubMed  Article  PubMed Central  Google Scholar 

    17.
    Kozak, K. H. & Wiens, J. J. Climatic zonation drives latitudinal variation in speciation mechanisms. Proc. Biol. Sci. 274, 2995–3003 (2007).
    PubMed  PubMed Central  Google Scholar 

    18.
    Moussalli, A., Moritz, C., Williams, S. E. & Carnaval, A. C. Variable responses of skinks to a common history of rainforest fluctuation: concordance between phylogeography and palaeo-distribution models. Mol. Ecol. 18, 483–499 (2009).
    PubMed  Article  PubMed Central  Google Scholar 

    19.
    Afonso Silva, A. C. et al. Tropical specialist vs. climate generalist: Diversification and demographic history of sister species of Carlia skinks from northwestern Australia. Mol. Ecol. 26, 4045–4058 (2017).
    CAS  PubMed  Article  PubMed Central  Google Scholar 

    20.
    Logan, M. L., Huynh, R. K., Precious, R. A. & Calsbeek, R. G. The impact of climate change measured at relevant spatial scales: new hope for tropical lizards. Glob. Chang. Biol. 19, 3093–3102 (2013).
    ADS  PubMed  Article  PubMed Central  Google Scholar 

    21.
    Moritz, C. et al. Impact of a century of climate change on small-mammal communities in Yosemite National Park, USA. Science 322, 261–264 (2008).
    ADS  CAS  PubMed  Article  PubMed Central  Google Scholar 

    22.
    Kamilar, J. M. & Muldoon, K. M. The climatic niche diversity of malagasy primates: a phylogenetic perspective. PLoS ONE 5, e11073 (2010).
    ADS  PubMed  Article  CAS  PubMed Central  Google Scholar 

    23.
    Braz, A. G., Lorini, M. L. & Vale, M. M. Climate change is likely to affect the distribution but not parapatry of the Brazilian marmoset monkeys (Callithrix spp.). Div. Dist. 25, 536–550 (2018).
    Article  Google Scholar 

    24.
    Cooper, N., Freckleton, R. P. & Jetz, W. Phylogenetic conservatism of environmental niches in mammals. Proc. Biol. Sci. 278, 2384–2391 (2011).
    PubMed  PubMed Central  Google Scholar 

    25.
    Lyu, Y., Wang, X. & Luo, J. Geographic patterns of insect diversity across China’s nature reserves: the roles of niche conservatism and range overlapping. Ecol. Evol. 10, 3305–3317 (2020).
    PubMed  Article  PubMed Central  Google Scholar 

    26.
    Hiller, A. E. et al. Niche conservatism predominates in adaptive radiation: comparing the diversification of Hawaiian arthropods using ecological niche modelling. Biol. J. Linn. Soc. Lond. 127, 479–492 (2019).
    Article  Google Scholar 

    27.
    Kabir, M. et al. Habitat suitability and movement corridors of grey wolf (Canis lupus) in Northern Pakistan. PLoS ONE 12, e0187027 (2017).
    PubMed  Article  CAS  PubMed Central  Google Scholar 

    28.
    Amano, T. & Sutherland, W. J. Four barriers to the global understanding of biodiversity conservation: wealth, language, geographical location and security. Proc. Biol. Sci. 280, 20122649 (2013).
    PubMed  PubMed Central  Google Scholar 

    29.
    Bellard, C. et al. Vulnerability of biodiversity hotspots to global change. Glob. Ecol. Biogeogr. 23, 1376–1386 (2014).
    Article  Google Scholar 

    30.
    Molotoks, A. et al. Global projections of future cropland expansion to 2050 and direct impacts on biodiversity and carbon storage. Glob. Chang. Biol. 24, 5895–5908 (2018).
    PubMed  Article  PubMed Central  Google Scholar 

    31.
    Mittermeier, R. A., Turner, W. R., Larsen, F. W., Brooks, T. M. & Gascon, C. Global biodiversity conservation: the critical role of hotspots. Biodivers. Hotspots https://doi.org/10.1007/978-3-642-20992-5_1 (2011).
    Article  Google Scholar 

    32.
    Johnson, W. E. et al. The late Miocene radiation of modern Felidae: a genetic assessment. Science 311, 73–77 (2006).
    ADS  CAS  PubMed  Article  PubMed Central  Google Scholar 

    33.
    Tamma, K., Marathe, A. & Ramakrishnan, U. Past influences present: mammalian species from different biogeographic pools sort environmentally in the Indian subcontinent. Front. Biogeogr. 8 (2016).

    34.
    Mukherjee, S., Duckworth, J. W., Silva, A., Appel, A. & Kittle, A. Prionailurus rubiginosus. IUCN Red List Threat. Species https://doi.org/10.2305/IUCN.UK.2016-1.RLTS.T18149A50662471.en (2016).
    Article  Google Scholar 

    35.
    Mukherjee, S. et al. Prionailurus viverrinus. IUCN Red List Threat. Species https://doi.org/10.2305/IUCN.UK.2016-2.RLTS.T18150A50662615.en (2016).
    Article  Google Scholar 

    36.
    Ross, J. et al. Prionailurus bengalensis. IUCN Red List Threat. Species https://doi.org/10.2305/IUCN.UK.2015-4.RLTS.T18146A50661611.en (2015).
    Article  Google Scholar 

    37.
    Nowell, K. & Jackson, P. Wild cats: status survey and conservation action plan ((IUCN, Gland, Switzerland, 1996).

    38.
    Sunquist, M. & Sunquist, F. Wild Cats of the World (University of Chicago Press, Chicago, 2012).
    Google Scholar 

    39.
    Pocock, R. I. The Fauna of British India Including Ceylon and Burma Vol. 1 (Taylor And Francis Ltd, London, 1939).
    Google Scholar 

    40.
    Mukherjee, S. et al. Ecology driving genetic variation: a comparative phylogeography of jungle cat (Felis chaus) and leopard cat (Prionailurus bengalensis) in India. PLoS ONE 5, e13724 (2010).
    ADS  PubMed  Article  CAS  PubMed Central  Google Scholar 

    41.
    Gray, T. N. E. et al. Felis chaus. IUCN Red List Threat. Species https://doi.org/10.2305/IUCN.UK.2016-2.RLTS.T8540A50651463.en (2016).
    Article  Google Scholar 

    42.
    Boitani, L. et al. What spatial data do we need to develop global mammal conservation strategies?. Philos. Trans. R. Soc. Lond. B Biol. Sci. 366, 2623–2632 (2011).
    PubMed  Article  PubMed Central  Google Scholar 

    43.
    Bartholomé, E. & Belward, A. S. GLC2000: a new approach to global land cover mapping from Earth observation data. Int. J. Remote Sens. 26, 1959–1977 (2005).
    ADS  Article  Google Scholar 

    44.
    Hijmans, R. J., Phillips, S., Leathwick, J. & Elith, J. dismo: species distribution modeling (2011).

    45.
    Grassman, L. I. Jr., Tewes, M. E., Silvy, N. J. & Kreetiyutanont, K. Spatial organization and diet of the leopard cat (Prionailurus bengalensis) in north-central Thailand. J. Zool. 266, 45–54 (2005).
    Article  Google Scholar 

    46.
    Thatte, P. et al. Human footprint differentially impacts genetic connectivity of four wide-ranging mammals in a fragmented landscape. Divers. Distrib. 7, 247 (2019).
    Google Scholar 

    47.
    Kalle, R., Ramesh, T., Qureshi, Q. & Sankar, K. Predicting the distribution pattern of small carnivores in response to environmental factors in the Western Ghats. PLoS ONE 8, e79295 (2013).
    ADS  PubMed  Article  CAS  PubMed Central  Google Scholar 

    48.
    Wilting, A. et al. Modelling the species distribution of flat-headed cats (Prionailurus planiceps), an endangered South-East Asian small felid. PLoS ONE 5, e9612 (2010).
    ADS  PubMed  Article  CAS  PubMed Central  Google Scholar 

    49.
    Srivathsa, A., Parameshwaran, R., Sharma, S. & Ullas Karanth, K. Estimating population sizes of leopard cats in the Western Ghats using camera surveys. J. Mammal. 96, 742–750 (2015).
    Article  Google Scholar 

    50.
    Bashir, T., Bhattacharya, T., Poudyal, K., Sathyakumar, S. & Qureshi, Q. Integrating aspects of ecology and predictive modelling: implications for the conservation of the leopard cat (Prionailurus bengalensis) in the Eastern Himalaya. Acta Theriol. 59, 35–47 (2014).
    Article  Google Scholar 

    51.
    Hijmans, R. J., Cameron, S. E., Parra, J. L., Jones, P. G. & Jarvis, A. Very high resolution interpolated climate surfaces for global land areas. Int. J. Climatol. 25, 1965–1978 (2005).
    Article  Google Scholar 

    52.
    Fick, S. E. & Hijmans, R. J. WorldClim 2: new 1-km spatial resolution climate surfaces for global land areas. Int. J. Climatol. 37, 4302–4315 (2017).
    Article  Google Scholar 

    53.
    Mayaux, P. et al. Validation of the global land cover 2000 map. IEEE Trans. Geosci. Remote Sens. 44, 1728–1739 (2006).
    ADS  Article  Google Scholar 

    54.
    Hijmans, R. J. raster: geographic data analysis and modelling (2014).

    55.
    Mukherjee, S., Goyal, S. P., Johnsingh, A. J. T. & Leite, M. R. The importance of rodents in the diet of jungle cat (Felis chaus), caracal (Caracal caracal) and golden jackal (Canis aureus) in Sariska Tiger Reserve, Rajasthan, India. J. Zool. 262, 405–411 (2004).
    Article  Google Scholar 

    56.
    Shehzad, W. et al. Carnivore diet analysis based on next-generation sequencing: application to the leopard cat (Prionailurus bengalensis) in Pakistan. Mol. Ecol. 21, 1951–1965 (2012).
    CAS  PubMed  Article  PubMed Central  Google Scholar 

    57.
    Rajaratnam, R., Sunquist, M., Rajaratnam, L. & Ambu, L. Diet and habitat selection of the leopard cat (Prionailurus bengalensis borneoensis) in an agricultural landscape in Sabah, Malaysian Borneo. J. Trop. Ecol. 23, 209–217 (2007).
    Article  Google Scholar 

    58.
    Phillips, S. J., Anderson, R. P. & Schapire, R. E. Maximum entropy modeling of species geographic distributions. Ecol. Modell. 190, 231–259 (2006).
    Article  Google Scholar 

    59.
    Phillips, S. J. et al. Sample selection bias and presence-only distribution models: implications for background and pseudo-absence data. Ecol. Appl. 19, 181–197 (2009).
    PubMed  Article  PubMed Central  Google Scholar 

    60.
    Anderson, R. P. & Gonzalez, I. Species-specific tuning increases robustness to sampling bias in models of species distributions: an implementation with Maxent. Ecol. Modell. 222, 2796–2811 (2011).
    Article  Google Scholar 

    61.
    Kramer-Schadt, S. et al. The importance of correcting for sampling bias in MaxEnt species distribution models. Divers. Distrib. 19, 1366–1379 (2013).
    Article  Google Scholar 

    62.
    Elith, J. et al. Novel methods improve prediction of species’ distributions from occurrence data. Ecography 29, 129–151 (2006).
    Article  Google Scholar 

    63.
    Pearson, R. G., Raxworthy, C. J., Nakamura, M. & Townsend Peterson, A. Predicting species distributions from small numbers of occurrence records: a test case using cryptic geckos in Madagascar. J. Biogeogr. 34, 102–117 (2006).
    Article  Google Scholar 

    64.
    Phillips, S. J. & Dudík, M. Modeling of species distributions with Maxent: new extensions and a comprehensive evaluation. Ecography 31, 161–175 (2008).
    Article  Google Scholar 

    65.
    Barbet-Massin, M., Jiguet, F., Albert, C. H. & Thuiller, W. Selecting pseudo-absences for species distribution models: how, where and how many?: How to use pseudo-absences in niche modelling?. Methods Ecol. Evol. 3, 327–338 (2012).
    Article  Google Scholar 

    66.
    Elith, J. et al. A statistical explanation of MaxEnt for ecologists. Div. Dist. 17, 43–57 (2011).
    Article  Google Scholar 

    67.
    Merow, C., Smith, M. J. & Silander, J. A. A practical guide to MaxEnt for modeling species’ distributions: what it does, and why inputs and settings matter. Ecography 36, 1058–1069 (2013).
    Article  Google Scholar 

    68.
    Galante, P. J. et al. The challenge of modeling niches and distributions for data-poor species: a comprehensive approach to model complexity. Ecography 41, 726–736 (2017).
    Article  Google Scholar 

    69.
    Pearce, J. & Ferrier, S. Evaluating the predictive performance of habitat models developed using logistic regression. Ecol. Modell. 133, 225–245 (2000).
    Article  Google Scholar 

    70.
    Warren, D. L. & Seifert, S. N. Ecological niche modeling in Maxent: the importance of model complexity and the performance of model selection criteria. Ecol. Appl. 21, 335–342 (2011).
    PubMed  Article  Google Scholar 

    71.
    Schoener, T. W. The anolis lizards of Bimini: resource partitioning in a complex fauna. Ecology 49, 704–726 (1968).
    Article  Google Scholar 

    72.
    Warren, D. L., Glor, R. E. & Turelli, M. Environmental niche equivalency versus conservatism: quantitative approaches to niche evolution. Evolution 62, 2868–2883 (2008).
    PubMed  Article  Google Scholar 

    73.
    Muscarella, R. et al. ENMeval: An R package for conducting spatially independent evaluations and estimating optimal model complexity for Maxent ecological niche models. Methods Ecol. Evol. 5, 1198–1205 (2014).
    Article  Google Scholar 

    74.
    Swets, J. Measuring the accuracy of diagnostic systems. Science 240, 1285–1293 (1988).
    ADS  MathSciNet  CAS  PubMed  MATH  Article  PubMed Central  Google Scholar 

    75.
    Ferro, C. A. T. & Stephenson, D. B. Extremal dependence indices: improved verification measures for deterministic forecasts of rare binary events. Weather Forecast. 26, 699–713 (2011).
    ADS  Article  Google Scholar 

    76.
    Broennimann, O. et al. Measuring ecological niche overlap from occurrence and spatial environmental data. Glob. Ecol. Biogeogr. 21, 481–497 (2011).
    Article  Google Scholar 

    77.
    Di Cola, V. et al. ecospat: an R package to support spatial analyses and modeling of species niches and distributions. Ecography 40, 774–787 (2017).
    Article  Google Scholar 

    78.
    UNEP-WCMC & IUCN. Protected Planet:The World Database on Protected Areas. Protected Planethttps://www.protectedplanet.net (2018).

    79.
    R Core Team. R: A language and environment for statistical computing (R Foundation for Statistical Computing, Vienna, 2018).
    Google Scholar 

    80.
    Otto-Bliesner, B. L., Marshall, S. J., Overpeck, J. T., Miller, G. H. & Hu, A. Simulating Arctic climate warmth and icefield retreat in the last interglaciation. Science 311, 1751–1753 (2006).
    ADS  CAS  PubMed  Article  PubMed Central  Google Scholar 

    81.
    Wilting, A. et al. Evolutionary history and conservation significance of the Javan leopard Panthera pardus melas. J. Zool. 299, 239–250 (2016).
    Article  Google Scholar 

    82.
    Cooper, D. M. et al. Predicted pleistocene-holocene range shifts of the tiger (Panthera tigris). Divers. Distrib. 22, 1199–1211 (2016).
    Article  Google Scholar 

    83.
    McSweeney, C. F., Jones, R. G., Lee, R. W. & Rowell, D. P. Selecting CMIP5 GCMs for downscaling over multiple regions. Clim. Dyn. 44, 3237–3260 (2014).
    Article  Google Scholar 

    84.
    Nogués-Bravo, D. Predicting the past distribution of species climatic niches. Glob. Ecol. Biogeogr. 18, 521–531 (2009).
    Article  Google Scholar 

    85.
    Rowan, J. et al. Geographically divergent evolutionary and ecological legacies shape mammal biodiversity in the global tropics and subtropics. Proc. Natl. Acad. Sci. USA 117, 1559–1565 (2020).
    ADS  CAS  PubMed  Article  PubMed Central  Google Scholar 

    86.
    Clavel, J., Julliard, R. & Devictor, V. Worldwide decline of specialist species: toward a global functional homogenization?. Front. Ecol. Environ. 9, 222–228 (2011).
    Article  Google Scholar 

    87.
    Hof, A. R., Jansson, R. & Nilsson, C. Future climate change will favour non-specialist mammals in the (sub)arctics. PLoS ONE 7, e52574 (2012).
    ADS  CAS  PubMed  Article  PubMed Central  Google Scholar 

    88.
    Davey, C. M., Chamberlain, D. E., Newson, S. E., Noble, D. G. & Johnston, A. Rise of the generalists: evidence for climate driven homogenization in avian communities. Glob. Ecol. Biogeogr. 21, 568–578 (2011).
    Article  Google Scholar 

    89.
    Pradervand, J.-N., Pellissier, L., Randin, C. F. & Guisan, A. Functional homogenization of bumblebee communities in alpine landscapes under projected climate change. Clim. Change Responses 1, 1 (2014).
    Article  Google Scholar 

    90.
    Tewksbury, J. J., Huey, R. B. & Deutsch, C. A. Ecology. Putting the heat on tropical animals. Science 320, 1296–1297 (2008).
    CAS  PubMed  Article  PubMed Central  Google Scholar 

    91.
    Huey, R. B. et al. Why tropical forest lizards are vulnerable to climate warming. Proc. Biol. Sci. 276, 1939–1948 (2009).
    PubMed  PubMed Central  Google Scholar 

    92.
    Araújo, M. B. et al. Quaternary climate changes explain diversity among reptiles and amphibians. Ecography 31, 8–15 (2008).
    Article  Google Scholar 

    93.
    Fordham, D. A., Saltré, F., Brown, S. C., Mellin, C. & Wigley, T. M. L. Why decadal to century timescale palaeoclimate data are needed to explain present-day patterns of biological diversity and change. Glob. Chang. Biol. 24, 1371–1381 (2018).
    ADS  PubMed  Article  PubMed Central  Google Scholar 

    94.
    Fordham, D. A. et al. PaleoView: a tool for generating continuous climate projections spanning the last 21 000 years at regional and global scales. Ecography 40, 1348–1358 (2017).
    Article  Google Scholar 

    95.
    Andam, K. S., Ferraro, P. J., Pfaff, A., Sanchez-Azofeifa, G. A. & Robalino, J. A. Measuring the effectiveness of protected area networks in reducing deforestation. Proc. Natl. Acad. Sci. USA 105, 16089–16094 (2008).
    ADS  CAS  PubMed  Article  PubMed Central  Google Scholar 

    96.
    Pressey, R. L. et al. How well protected are the forests of north-eastern New South Wales? Analyses of forest environments in relation to formal protection measures, land tenure, and vulnerability to clearing. For. Ecol. Manag. 85, 311–333 (1996).
    Article  Google Scholar 

    97.
    Joppa, L. N. & Pfaff, A. High and far: biases in the location of protected areas. PLoS ONE 4, e8273 (2009).
    ADS  PubMed  Article  CAS  PubMed Central  Google Scholar 

    98.
    Connor, T. et al. Effects of grain size and niche breadth on species distribution modeling. Ecography 41, 1270–1282 (2017).
    Article  Google Scholar 

    99.
    Seo, C., Thorne, J. H., Hannah, L. & Thuiller, W. Scale effects in species distribution models: implications for conservation planning under climate change. Biol. Lett. 5, 39–43 (2009).
    PubMed  Article  PubMed Central  Google Scholar 

    100.
    Latinne, A. et al. Influence of past and future climate changes on the distribution of three Southeast Asian murine rodents. J. Biogeogr. 42, 1714–1726 (2015).
    Article  Google Scholar 

    101.
    Radchuk, V., Kramer-Schadt, S., Fickel, J. & Wilting, A. Distributions of mammals in Southeast Asia: the role of the legacy of climate and species body mass. J. Biogeogr. https://doi.org/10.1111/jbi.13675 (2019).
    Article  Google Scholar 

    102.
    Patel, R. P. et al. Genetic structure and phylogeography of the leopard cat (Prionailurus bengalensis) inferred from mitochondrial genomes. J. Hered. 108, 349–360 (2017).
    PubMed  Article  PubMed Central  Google Scholar 

    103.
    Sreehari, R. & Nameer, P. O. Small carnivores of Parambikulam Tiger Reserve, southern Western Ghats, India. J. Threat. Taxa 8, 9306 (2016).
    Article  Google Scholar 

    104.
    Past Interglacials Working Group of PAGES. Interglacials of the last 800,000 years. Rev. Geophys. 54, 162–219 (2016).
    ADS  Article  Google Scholar 

    105.
    Luo, S.-J. et al. Sympatric Asian felid phylogeography reveals a major Indochinese-Sundaic divergence. Mol. Ecol. 23, 2072–2092 (2014).
    CAS  PubMed  Article  PubMed Central  Google Scholar 

    106.
    Mukherjee, S., Adhya, T., Thatte, P. & Ramakrishnan, U. Survey of the fishing cat prionailurus viverrinus Bennett, 1833 (Carnivora: Felidae) and some aspects impacting its conservation in India. J. Threat. Taxa 04, 3355–3361 (2012).
    Article  Google Scholar 

    107.
    Shekhar Palei, H., Palei, H. S., Das, U. P. & Debata, S. The vulnerable fishing cat Prionailurus viverrinus in Odisha, eastern India: status and conservation implications. Zool. Ecol. 28, 69–74 (2018).
    Article  Google Scholar 

    108.
    Nayak, S., Shah, S. & Borah, J. First record of rusty-spotted cat Prionailurus rubiginosus (Mammalia: Carnivora: Felidae) from Ramgarh-Vishdhari Wildlife Sanctuary in semi-arid landscape of Rajasthan, India. J. Threat. Taxa 9, 9761 (2017).
    Article  Google Scholar 

    109.
    Lamichhane, B. R. et al. Rusty-spotted cat: 12th cat species discovered in Western Terai of Nepal. Cat News 64, 30–32 (2016).
    Google Scholar 

    110.
    Anwar, M. & Vattakavan, J. Rusty spotted cat in Katerniaghat Wildlife Sanctuary, Uttar Pradesh State, India. Cat News 56, 12–13 (2012).
    Google Scholar 

    111.
    Harihar, A., Chanchani, P., Pariwakam, M., Noon, B. R. & Goodrich, J. Defensible inference: questioning global trends in tiger populations. Conserv. Lett. 10, 502–505 (2017).
    Article  Google Scholar 

    112.
    Mantyka-Pringle, C. S. et al. Climate change modifies risk of global biodiversity loss due to land-cover change. Biol. Conserv. 187, 103–111 (2015).
    Article  Google Scholar 

    113.
    Newbold, T. Future effects of climate and land-use change on terrestrial vertebrate community diversity under different scenarios. Proc. Biol. Sci.285 (2018).

    114.
    Prestele, R. et al. Hotspots of uncertainty in land-use and land-cover change projections: a global-scale model comparison. Glob. Chang. Biol. 22, 3967–3983 (2016).
    ADS  PubMed  Article  PubMed Central  Google Scholar  More

  • in

    Decline of six native mason bee species following the arrival of an exotic congener

    1.
    Brown, W. L. & Wilson, E. O. Character displacement. Syst. Zool. 5, 49 (1956).
    Article  Google Scholar 
    2.
    Jeffries, M. & Lawton, J. Enemy free space and the structure of ecological communities. Biol. J. Linn. Soc. 23, 269–286 (1984).
    Article  Google Scholar 

    3.
    Reynolds, J. D. Crayfish extinctions and crayfish plague in central Ireland. Biol. Conserv. 45, 279–285 (1988).
    Article  Google Scholar 

    4.
    Stephen, W. P. Solitary bees in North American agriculture: A perspective. In For non-native crops, whence pollinators of the future (eds Strickler, K. & Cane, J. H.) 41–66 (Entomological Society of America, 2003).

    5.
    Goulson, D. & Hanley, M. E. Distribution and forage use of exotic bumblebees in South Island New Zealand. N. Z. J. Ecol. 28, 225–232 (2004).
    Google Scholar 

    6.
    Morales, C. L. & Aizen, M. A. Invasive mutualisms and the structure of plant–pollinator interactions in the temperate forests of north-west Patagonia. Argentina. J. Ecol. 94, 171–180 (2006).
    Article  Google Scholar 

    7.
    Vergara, C. H. Environmental impact of exotic bees introduced for crop pollination. In Bee Pollination in Agricultural Ecosystems (eds James, R. R. & Pitts-Singer, T. L.) 145–165 (Oxford University Press, Oxford, 2008).
    Google Scholar 

    8.
    Roberts, R. B. The nesting biology, behavior and immature stages of Lithurge chrysurus, an adventitious wood-boring bee in New Jersey (Hymenoptera: Megachilidae). J. Kans. Entomol. Soc. 51, 735–745 (1978).
    Google Scholar 

    9.
    Mangum, W. A. & Brooks, R. W. First records of Megachile (Callomegachile) sculpturalis Smith (Hymenoptera: Megachilidae) in the Continental United States. J. Kans. Entomol. Soc. 70, 140–142 (1997).
    Google Scholar 

    10.
    Russo, L. Positive and negative impacts of non-native bee species around the world. Insects 7, 69 (2016).
    PubMed Central  Article  Google Scholar 

    11.
    Goulson, D. Effects of introduced bees on native ecosystems. Annu. Rev. Ecol. Evol. Syst. 34, 1–26 (2003).
    Article  Google Scholar 

    12.
    Inoue, M. N., Yokoyama, J. & Washitani, I. Displacement of Japanese native bumblebees by the recently introduced Bombus terrestris (L.) (Hymenoptera: Apidae). J. Insect Conserv. 12, 135–146 (2008).
    Article  Google Scholar 

    13.
    Morales, C. L., Arbetman, M. P., Cameron, S. A. & Aizen, M. A. Rapid ecological replacement of a native bumble bee by invasive species. Front. Ecol. Environ. 11, 529–534 (2013).
    Article  Google Scholar 

    14.
    Schmid-Hempel, R. et al. The invasion of southern South America by imported bumblebees and associated parasites. J. Anim. Ecol. 83, 823–837 (2014).
    PubMed  Article  PubMed Central  Google Scholar 

    15.
    Cane, J. H. Exotic non-social bees (Hymenoptera: Apoidea) in North America: Ecological implications. In For non-native crops, whence pollinators of the future (eds Strickler, K. & Cane, J. H.) 113–126 (Entomological Society of America, 2003).

    16.
    Paini, D. R. Impact of the introduced honey bee (Apis mellifera) (Hymenoptera: Apidae) on native bees: A review. Austral Ecol. 29, 399–407 (2004).
    Article  Google Scholar 

    17.
    Mallinger, R. E., Gaines-Day, H. R. & Gratton, C. Do managed bees have negative effects on wild bees? A systematic review of the literature. PLoS ONE 12, e0189268 (2017).
    PubMed  PubMed Central  Article  CAS  Google Scholar 

    18.
    Ascher, J. S. & Pickering, J. Apoidea species—identification guide—Discover Life. https://www.discoverlife.org/mp/20q?guide=Apoidea_species&flags=HAS: (2020).

    19.
    Batra, S. Osmia cornifrons and Pithitis smaragdula, two Asian bees introduced into the United States for crop pollination. in Proceedings 4th International Symposium on Pollination. Maryland Agricultural Experimental Station Miscellaneous Publication (1978).

    20.
    Droege, S. USGS PWRC – Native Bee Inventory and Monitoring Lab (BIML). https://doi.org/10.15468/6AUTVB (2020).

    21.
    Cane, J. H., Griswold, T. & Parker, F. D. Substrates and materials used for nesting by North American Osmia bees (Hymenoptera: Apiformes: Megachilidae). Ann. Entomol. Soc. Am. 100, 350–358 (2007).
    Article  Google Scholar 

    22.
    Droege, S., Engler, J., Sellers, E. & O’Brien, L. National Protocol Framework for the Inventory and Monitoring of Bees (U.S. Fish and Wildlife Service, Washington, D.C., 2016).
    Google Scholar 

    23.
    LeBuhn, G., Droege, S., Connor, E., Gemmill-Herren, B. & Azzu, N. Protocol to Detect and Monitor Pollinator Communities: Guidance for Practitioners (Food and Agriculture Organization of the United Nations, Rome, 2016).
    Google Scholar 

    24.
    Droege, S. Impact of color and size of bowl trap on numbers of bees captured. J. Insect Conserv. https://doi.org/10.1007/s10841-016-9914-6 (2006).
    Article  Google Scholar 

    25.
    Gonzalez, V. H. et al. Effect of pan trap size on the diversity of sampled bees and abundance of bycatch. J. Insect Conserv. https://doi.org/10.1007/s10841-020-00224-4 (2020).
    Article  Google Scholar 

    26.
    Wilson, J. S. et al. Sampling bee communities using pan traps: Alternative methods increase sample size. J. Insect Conserv. 20, 919–922 (2016).
    Article  Google Scholar 

    27.
    Westphal, C. et al. Measuring bee diversity in different European habitats and biogeographical regions. Ecol. Monogr. 78, 653–671 (2008).
    Article  Google Scholar 

    28.
    Greenleaf, S. S., Williams, N. M., Winfree, R. & Kremen, C. Bee foraging ranges and their relationship to body size. Oecologia 153, 589–596 (2007).
    ADS  PubMed  Article  PubMed Central  Google Scholar 

    29.
    Frey, B. J. & Dueck, D. Clustering by passing messages between data points. Science 315, 972–976 (2007).
    ADS  MathSciNet  CAS  PubMed  MATH  Article  PubMed Central  Google Scholar 

    30.
    Shapiro, L. H., Tepedino, V. J. & Minckley, R. L. Bowling for bees: optimal sample number for “bee bowl” sampling transects. J. Insect Conserv. 18, 1105–1113 (2014).
    Article  Google Scholar 

    31.
    Joe, H. & Zhu, R. Generalized poisson distribution: The property of mixture of poisson and comparison with negative binomial distribution. Biom. J. 47, 219–229 (2005).
    MathSciNet  PubMed  MATH  Article  PubMed Central  Google Scholar 

    32.
    Didham, R. K. et al. Interpreting insect declines: Seven challenges and a way forward. Insect Conserv. Divers. 13, 103–114 (2020).
    Article  Google Scholar 

    33.
    Maeta, Y. Comparative studies on the biology of bees of the genus Osmia of Japan, with special reference to their management for pollinations of crops (Hymenoptera: Megachilidae). Bull. Tohoku Nat. Agric. Exp. Stn. 57, 1–221 (1978).
    Google Scholar 

    34.
    Bosch, J. & Kemp, W. P. How to Manage the Blue Orchard Bee: As an Orchard Pollinator (Sustainable Agriculture Network, San José, 2001).
    Google Scholar 

    35.
    Kraemer, M. E., Favi, F. D. & Niedziela, C. E. Nesting and pollen preference of Osmia lignaria lignaria (Hymenoptera: Megachilidae) in Virginia and North Carolina orchards. Environ. Entomol. 43, 932–941 (2014).
    CAS  PubMed  Article  PubMed Central  Google Scholar 

    36.
    Tompkins, D. M., White, A. R. & Boots, M. Ecological replacement of native red squirrels by invasive greys driven by disease. Ecol. Lett. 6, 189–196 (2003).
    Article  Google Scholar 

    37.
    Prenter, J., MacNeil, C., Dick, J. T. A. & Dunn, A. M. Roles of parasites in animal invasions. Trends Ecol. Evol. 19, 385–390 (2004).
    PubMed  Article  PubMed Central  Google Scholar 

    38.
    Stephen, W. P., Vandenberg, J. D. & Fichter, B. L. Etiology and epizootiology of chalkbrood in the alfalfa leafcutting bee, Megachile rotundata, with notes on Ascosphaera species. Oregon State Univ. Agric. Exp. Stn. Bull. 653, 1–10 (1981).
    Google Scholar 

    39.
    Hedtke, S. M., Blitzer, E. J., Montgomery, G. A. & Danforth, B. N. Introduction of non-native pollinators can lead to trans-continental movement of bee-associated fungi. PLoS ONE 10, e0130560 (2015).
    PubMed  PubMed Central  Article  CAS  Google Scholar 

    40.
    Klinger, E. G. Virulence Evolution of Fungal Pathogens in Social and Solitary Bees with an Emphasis on Multiple Infections. (Utah State University, Logan 2015).
    Google Scholar 

    41.
    Kamijo, K. A revision of the species of the Monodontomerinae occurring in Japan (Hymenoptera: Chlacidoidea) [Taxonomic Studies on the Torymidae of Japan, 2]. Insecta Matsumurana 26, 89–98 (1963).
    Google Scholar 

    42.
    Kamijo, K. Description of five new species of Eulophinae from Japan and other notes (Hymenoptera: Chalcidoidea). Insecta Matsumurana 28, 69–78 (1965).
    Google Scholar 

    43.
    Grissell, E. Discovery of Monodontomerus osmiae Kamijo (Hymenoptera: Torymidae) in the New World. Proc. Entomol. Soc. Wash. 105, 243–245 (2003).
    Google Scholar 

    44.
    Majka, C. G., Philips, T. K. & Sheffield, C. Ptinus sexpunctatus Panzer (Coleoptera: Anobiidae, Ptininae) newly recorded in North America. Entomol. News 118, 73–76 (2007).
    Article  Google Scholar 

    45.
    Torchin, M. E. & Mitchell, C. E. Parasites, pathogens, and invasions by plants and animals. Front. Ecol. Environ. 2, 183–190 (2004).
    Article  Google Scholar 

    46.
    MacIvor, J. S. & Packer, L. ‘Bee hotels’ as tools for native pollinator conservation: A premature verdict? PLoS ONE 10, e0122126 (2015).
    PubMed  PubMed Central  Article  CAS  Google Scholar 

    47.
    Park, Y. L. et al. Nest-to-nest dispersal of Chaetodactylus krombeini (Acari, Chaetodactylidae) associated with Osmia cornifrons (Hym., Megachilidae). J. Appl. Entomol. 133, 174–180 (2009).
    Article  Google Scholar 

    48.
    Maeta, Y. & Kitamura, T. Studies on the apple pollination by Osmia. II. Characteristics and underlying problems in utilizing Osmia. Kontyu 33, 17–34 (1965).

    49.
    Kobayashi, M. Problems in the utilisation of Eristalis cerealis as pollinator. Shokubutsu Boeki 26, 473–478 (1972).
    Google Scholar 

    50.
    Biddinger, D. J. et al. Development of the mason bee, Osmia cornifrons, as an alternative pollinator to honey bees and as a targeted delivery system for biocontrol agents in the management of fire blight. Penn Fruit News 90, 35–44 (2009).
    Google Scholar 

    51.
    West, T. P. & McCutcheon, T. W. Evaluating Osmia cornifrons as pollinators of highbush blueberry. Int. J. Fruit Sci. 9, 115–125 (2009).
    Article  Google Scholar 

    52.
    Portman, Z. M., Bruninga-Socolar, B. & Cariveau, D. P. The state of bee monitoring in the United States: a call to refocus away from bowl traps and towards more effective methods. Ann. Entomol. Soc. Am. https://doi.org/10.1093/aesa/saaa010 (2020).
    Article  Google Scholar  More

  • in

    Health assessment of important tributaries of Three Georges Reservoir based on the benthic index of biotic integrity

    Investigation method
    From March 2015 to December 2018, we surveyed 36 important tributaries of the TGR (Fig. 3) and conducted an investigation of macroinvertebrates. For the sake of convenience, we labeled tributaries from the reservoir dam to its tail area sequentially as R01–R36, i.e., R01 (Xiangxi River), R02 (Qinggan River), R03 (Shennong River), R04 (Baolong River), R05 (Guandu River), R06 (Daning River), R07 (Daxi-F River), R08 (Caotang River), R09 (Meixi River), R10 (Changtan River), R11 (Modao River), R12 (Tangxi River), R13 (Xiao River), R14 (Zhuxi River), R15 (Rangdu River), R16 (Ruxi River), R17 (Huangjin River), R18 (Dongxi River), R19 (Chixi River), R20 (Long River), R21 (Bixi River), R22 (Quxi River), R23 (Zhenxi River), R24 (Wu River), R25 (Lixiang River), R26 (Longxi River), R27 (Taohua River), R28 (Yulin River), R29 (Wubu River), R30 (Changtang River), R31 (Chaoyang River), R32 (Jialing River), R33 (Huaxi River), R34 (Yipin River), R35 (Daxi-J River), and R36 (Qi River).
    Figure 3

    Schematic diagram of important tributaries of TGR and sampling points (plotted by ArcGIS 10.5, https://www.32r.com/soft/16101.html).

    Full size image

    This study was approved by the Environmental Protection Bureau of the Three Georges Reservoir.
    A total of 175 sampling points were set up in all tributaries. Four parallel samples were taken from each sampling point. At least one sample was taken from each microhabitat (mainly including four microhabitats, i.e., shoal, deep pool, pebble and aquatic habitat). Parallel samples from the same sampling point were mixed together. The quantitative and qualitative sample collection methods were combined in this study. The quantitative collection was performed first, and then the qualitative collection for the same sampling point. The qualitative samples were collected by D-net. Quantitative samples of wadable sampling points were collected using a Surber net with an area of 0.3 m × 0.3 m. Quantitative samples of non-wadable sampling points were collected using a D-net with a bottom side length of 0.3 m. The collected samples were put into sample bottles (bags) and fixed with 5% formaldehyde solution. Then the samples were identified and classified under the laboratory conditions.
    Selection of reference sites and impaired sites
    A reference site refers to a sampling point with no or little anthropogenic disturbance, while a impaired site refers to a sampling point subject to obvious anthropogenic disturbance29. A total of 15 reference sites and 160 impaired sites were selected from 175 sampling points based on anthropogenic disturbance, vegetation coverage, population distribution, and the distribution of industry and agriculture in the vicinity of the sampling site6,8 (Table 7, Supplementary Fig. 1).
    Table 7 Assessment criteria for reference sites and impaired sites and the assessment outcomes.
    Full size table

    Creation and selection of the assessment metric index system
    With reference to the river health assessment indexes in China13,15,18,24, North America6,24 and Europe5, and based on the ecological characteristics such as species composition and abundance, sensitivity, tolerance and functional feeding groups, we constructed 26 candidate metrics (Table 8) for B-IBI. These candidate metrics have significant or noticeable response to human activities, and normally, can be applied to relatively large geographic areas; therefore, they can be used to indicate the ecological quality of rivers6,23,24. Among these metrics, 17 were associated with species composition and abundance, which included the total number of taxon, the number of EPT taxa, the number of crustacean and mollusca taxa, the number of ephemerida taxa, the number of pteroptera taxa, the number of trichoptera taxa, the number of diptera taxa, the number of chironomidea taxa, the percentage of EPT, the percentage of crustacean and mollusca, the percentage of ephemerida, the percentage of pteroptera, the percentage of trichoptera, the percentage of dipteral, the percentage of chironomidea, the percentage of oligochaeta and the Shannon-Weiner diversity index. Species composition and abundance-related indexes reflect the diversity of macrobenthic communities. An increase in species diversity is associated with the improvement of community health, which indicates that the niche space and food sources are sufficient to support the survival and reproduction of multiple species. The candidate metrics related to sensitivity and tolerance in this study were the number of sensitive taxa, the number of tolerant taxa, the percentage of dominant species and the percentage of the top three dominant species. Different zoobenthos show different degrees of sensitivity and tolerance to the influencing factors in the river habitat, for which these characteristics can be used to assess the health status of the river. In addition, the taxa and percentage of functional feeding groups are closely associated with their living environment, and the parameters that were used to represent functional feeding in this study were the percentages of shredders, herbivores, filterers, scrapers and predators. Some of the representative images of the identified taxa were shown in Supplementary Fig. 1E,F.
    Table 8 Candidate parameters for B-IBI and their response to anthropogenic disturbance.
    Full size table

    The selection of core metrics for B-IBI mainly includes three steps: analysis of distribution range of candidate metrics, analysis of discriminant ability of candidate metrics and analysis of correlation between candidate metrics23.
    Analysis of distribution range of candidate metrics
    According to the numerical value of each biological metric in the reference site, an initial analysis was conducted to exclude the following two types of metrics: metrics with excessive nought values, which did not meet the requirement for a universal applicability; metrics with a scatter value distribution, and a standard deviation greater than or equal to the mean, indicating that the standard deviation of this value was relatively big and unstable, thereby unsuitable to be used as biological metrics6.
    Analysis of discriminant ability of candidate metrics
    After analyzing the distribution range of candidate metrics, those unsuitable for biological evaluation were eliminated. The distribution of the remaining eligible metrics for the reference site and the impaired site was analyzed using the box-plot, to mainly compare the distribution range of the 25th quantile to the 75th quantile of the reference site and the impaired site and the overlap of “box” InterQuartile Range (IQR), and judge which biological metrics could best distinguish between the reference site and impaired site. An IQ value ≥ 2 indicates a small overlapping part between the reference site and the impacted site, which means a significant difference in the related parameter between the reference site and the impacted site, suggesting a noticeable response to human activity6,24. The IQ scoring criteria were as follows6,24: 3 point, no overlapping between the two box bodies; 2 points, the box bodies have a small part of overlapping, but the median of neither body falls within the limits of its counterpart; 1 point, most parts of the box bodies overlap, and the median of at least one box body lies within the limits of its counterpart; 0 points, one box body falls within the limits of the other, or the medians of each body are within the other’s limits.
    Correlation analysis of candidate metrics
    Pearson correlation analysis was further performed of the metrics that met the preliminary conditions. If the correlation coefficient (left| {text{r}} right|) between two metrics is greater than 0.75, and they are intrinsically linked. Then most of the information reflected is overlapping. Therefore, it is OK to select one of them. If no intrinsic connection is found between two metrics, then both metrics can be selected even if the correlation coefficient is greater than 0.758.
    After screening through the above three steps, core metrics of the B-IBI are finally determined.
    Construction of B-IBI
    The core biological metrics screened out by the above method were used as the metrics for final biological assessment. The metrics used for biological assessment were standardized using the ratio scoring method, to unify the evaluation metric23.
    (1)
    For a metric that decreased with increasing interference, the metric was normalized by dividing the value of this metric at each sample point with the 95% quantile of all sample points:

    $${text{V}}_{{text{i}}}^{prime } = {text{V}}_{{text{i}}} /{text{V}}_{{{95}% }} ;$$

    (2)
    For a metric that increased with increasing interference, the metric was normalized by using the 5% quantile of this metric at all sample points as the reference object:

    $${text{V}}_{{text{i}}}^{prime } = left( {{text{V}}_{{{text{MAX}}}} – {text{V}}_{{text{i}}} } right)/left( {{text{V}}_{{{text{MAX}}}} – {text{V}}_{{{5}% }} } right),$$

    where Vi′ is the normalized value of the metric at the ith sampling point; Vi the actual value of the metric at the ith sampling point; V95% the 95% quantile of the metric; V5% is the 5% quantile of the metric; VMAX is the maximum value of this metric in all sampling points. The health thresholds of 5% quantile and 95% quantile can eliminate extreme abnormal values and retain most of biological information.

    B-IBI assessment criteria
    The 95% quantile of B-IBI distribution of all the sections/tributaries used for the health threshold can eliminate extreme abnormal values and retain most biological information. The distribution range lower than this value is divided into four portions, and the quartile close to the 95% quantile indicates a small disturbance. The biological integrity grade and the corresponding range of IBI6 are determined according to the 95% quantile and the quartile value, and the section/river health was classified into five grades, namely, excellent, good, fair, poor and very poor. More

  • in

    Amycolatopsis acididurans sp. nov., isolated from peat swamp forest soil in Thailand

    1.
    Salam N, Jiao J-Y, Zhang X-T, Li W-J. Update on the classification of higher ranks in the phylum Actinobacteria. Int J Syst Evol Microbiol. 2020;70:1331–55.
    CAS  Article  Google Scholar 
    2.
    Ningsih F, et al. Gandjariella thermophila gen. nov., sp. nov., a new member of the family Pseudonocardiaceae, isolated from forest soil in a geothermal area. Int J Syst Evol Microbiol. 2019;69:3080–6.
    CAS  Article  Google Scholar 

    3.
    Nouioui I, et al. Genome-based taxonomic classification of the phylum Actinobacteria. Front Microbiol. 2018;9:2007.
    Article  Google Scholar 

    4.
    Parte AC. LPSN—list of prokaryotic names with standing in nomenclature (bacterio.net), 20 years on. Int J Syst Evol Microbiol. 2018;68:1825–9.
    Article  Google Scholar 

    5.
    Alanjary M, Steinke K, Ziemert N. AutoMLST: an automated web server for generating multi-locus species trees highlighting natural product potential. Nucleic Acids Res. 2019;47:W276–82.
    CAS  Article  Google Scholar 

    6.
    Teo WFA, Srisuk N, Duangmal K. Amycolatopsis acidicola sp. nov., isolated from peat swamp forest soil. Int J Syst Evol Microbiol. 2020;70:1547–54.
    CAS  Article  Google Scholar 

    7.
    Niu M-M, et al. Amycolatopsis nivea sp. nov., isolated from a Yellow River sample. Int J Syst Evol Microbiol. 2020;70:3084–90.
    CAS  Article  Google Scholar 

    8.
    Narsing Rao MP, et al. Amycolatopsis alkalitolerans sp. nov., isolated from Gastrodia elata Blume. J Antibiot. 2020;73:35–39.
    CAS  Article  Google Scholar 

    9.
    Mingma R, Inahashi Y, Matsumoto A, Takahashi Y, Duangmal K. Amycolatopsis pithecelloba sp. nov., a novel actinomycete isolated from roots of Pithecellobium dulce in Thailand. J Antibiot. 2020;73:230–5.
    CAS  Article  Google Scholar 

    10.
    Wang H-F, et al. Amycolatopsis anabasis sp. nov., a novel endophytic actinobacterium isolated from roots of Anabasis elatior. Int J Syst Evol Microbiol. 2020;70:3391–8.
    CAS  Article  Google Scholar 

    11.
    Sangal V, et al. Revisiting the taxonomic status of the biomedically and industrially important genus Amycolatopsis, using a phylogenomic approach. Front Microbiol. 2018;9:2281.
    Article  Google Scholar 

    12.
    Adamek M, et al. Comparative genomics reveals phylogenetic distribution patterns of secondary metabolites in Amycolatopsis species. BMC Genomics. 2018;19:426.
    Article  Google Scholar 

    13.
    Waksman SA. The Actinomycetes: their nature, occurrence, activities, and importance. Waltham, Massachusetts: Chronica Botanica Company; 1950.

    14.
    Donadio S, Cavaletti L, Monciardini P. Genus I Actinospica Cavaletti, Monciardini, Schumann, Rohde, Bamonte, Busti, Sosio and Donadio 2006, 1751VP. In: Goodfellow M, et al., editors. Bergey’s Manual of Systematic Bacteriology. 2nd. New York: Springer; 2012. p. 232–4.

    15.
    Shirling EB, Gottlieb D. Methods for characterization of Streptomyces species. Int J Syst Bacteriol. 1966;16:313–40.
    Article  Google Scholar 

    16.
    Tan GYA, Ward AC, Goodfellow M. Exploration of Amycolatopsis diversity in soil using genus-specific primers and novel selective media. Syst Appl Microbiol. 2006;29:557–69.
    CAS  Article  Google Scholar 

    17.
    Williams ST, Davies FL, Mayfield CI, Khan MR. Studies on the ecology of actinomycetes in soil—II: The pH requirements of streptomycetes from two acid soils. Soil Biol Biochem. 1971;3:187–95.
    CAS  Article  Google Scholar 

    18.
    Flowers TH, Williams ST. Nutritional requirements of acidophilic streptomycetes. Soil Biol Biochem. 1977;9:225–6.
    CAS  Article  Google Scholar 

    19.
    Becker B, Lechevalier MP, Lechevalier HA. Chemical composition of cell-wall preparations from strains of various form-genera of aerobic actinomycetes. Appl Microbiol. 1965;13:236–43.
    CAS  Article  Google Scholar 

    20.
    Hasegawa T, Takizawa M, Tanida S. A rapid analysis for chemical grouping of aerobic actinomycetes. J Gen Appl Microbiol. 1983;29:319–22.
    CAS  Article  Google Scholar 

    21.
    Staneck JL, Roberts GD. Simplified approach to identification of aerobic actinomycetes by thin-layer chromatography. Appl Microbiol. 1974;28:226–31.
    CAS  Article  Google Scholar 

    22.
    Tomiyasu I. Mycolic acid composition and thermally adaptative changes in Nocardia asteroides. J Bacteriol. 1982;151:828–37.
    CAS  Article  Google Scholar 

    23.
    Minnikin DE, Patel PV, Alshamaony L, Goodfellow M. Polar lipid composition in the classification of nocardia and related bacteria. Int J Syst Evol Microbiol. 1977;27:104–17.
    CAS  Google Scholar 

    24.
    Kieser T, Bibb MJ, Buttner MJ, Chater KF, Hopwood DA. Practical Streptomyces Genetics. Norwich: John Innes Foundation; 2000.

    25.
    Bankevich A, et al. SPAdes: a new genome assembly algorithm and its applications to single-cell sequencing. J Comput Biol. 2012;19:455–77.
    CAS  Article  Google Scholar 

    26.
    Gurevich A, Saveliev V, Vyahhi N, Tesler G. QUAST: quality assessment tool for genome assemblies. Bioinformatics. 2013;29:1072–5.
    CAS  Article  Google Scholar 

    27.
    Tatusova T, et al. NCBI prokaryotic genome annotation pipeline. Nucleic Acids Res. 2016;44:6614–24.
    CAS  Article  Google Scholar 

    28.
    Cosentino S, Voldby Larsen M, Møller Aarestrup F, Lund O. PathogenFinder—distinguishing friend from foe using bacterial whole genome sequence data. PLoS ONE. 2013;8:e77302.
    CAS  Article  Google Scholar 

    29.
    Blin K, et al. AntiSMASH 5.0: updates to the secondary metabolite genome mining pipeline. Nucleic Acids Res. 2019;47:W81–7.
    CAS  Article  Google Scholar 

    30.
    Chun J, et al. Proposed minimal standards for the use of genome data for the taxonomy of prokaryotes. Int J Syst Evol Microbiol. 2018;68:461–6.
    CAS  Article  Google Scholar 

    31.
    Yoon S-H, et al. Introducing EzBioCloud: a taxonomically united database of 16S rRNA gene sequences and whole-genome assemblies. Int J Syst Evol Microbiol. 2017;67:1613–7.
    CAS  Article  Google Scholar 

    32.
    Tarlachkov SV, Starodumova IP. TaxonDC: calculating the similarity value of the 16S rRNA gene sequences of prokaryotes or ITS regions of fungi. J Bioinf Genom. 2017;3:1–4.
    Google Scholar 

    33.
    Richter M, Rosselló-Móra R, Oliver Glöckner F, Peplies J. JSpeciesWS: a web server for prokaryotic species circumscription based on pairwise genome comparison. Bioinformatics. 2016;32:929–31.
    CAS  Article  Google Scholar 

    34.
    Meier-Kolthoff JP, Auch AF, Klenk H-P, Göker M. Genome sequence-based species delimitation with confidence intervals and improved distance functions. BMC Bioinform. 2013;14:1–14.
    Article  Google Scholar 

    35.
    Kumar S, Stecher G, Tamura K. MEGA7: molecular evolutionary genetics analysis version 7.0 for bigger datasets. Mol Biol Evol. 2017;33:1870–4.
    Article  Google Scholar 

    36.
    Meier-Kolthoff JP, Göker M. TYGS is an automated high-throughput platform for state-of-the-art genome-based taxonomy. Nat Commun. 2019;10:2182.
    Article  Google Scholar 

    37.
    Lechevalier MP, De Bievre C, Lechevalier H. Chemotaxonomy of aerobic actinomycetes: phospholipid composition. Biochem Syst Ecol. 1977;5:249–60.
    CAS  Article  Google Scholar 

    38.
    Lechevalier MP, Lechevalier H. Chemical composition as a criterion in the classification of aerobic actinomycetes. Int J Syst Evol Microbiol. 1970;20:435–43.
    CAS  Google Scholar 

    39.
    Seyedsayamdost MR, Traxler MF, Zheng S-L, Kolter R, Clardy J. Structure and biosynthesis of amychelin, an unusual mixed-ligand siderophore from Amycolatopsis sp. AA4. J Am Chem Soc. 2011;133:11434–7.
    CAS  Article  Google Scholar 

    40.
    Kodani S, Komaki H, Suzuki M, Hemmi H, Ohnishi-Kameyama M. Isolation and structure determination of new siderophore albachelin from Amycolatopsis alba. BioMetals. 2015;28:381–9.
    CAS  Article  Google Scholar  More

  • in

    Decrease in social cohesion in a colonial seabird under a perturbation regime

    1.
    Holling, C. S. Resilience and stability of ecological systems. Annu. Rev. Ecol. Syst. 4, 1–23 (1973).
    Article  Google Scholar 
    2.
    Dai, L., Korolev, K. S. & Gore, J. Relation between stability and resilience determines the performance of early warning signals under different environmental drivers. Proc. Natl. Acad. Sci. 112, 10056–10061 (2015).
    ADS  CAS  PubMed  Article  PubMed Central  Google Scholar 

    3.
    Dakos, V., Carpenter, S. R., van Nes, E. H. & Scheffer, M. Resilience indicators: Prospects and limitations for early warnings of regime shifts. Philos. Trans. R. Soc. B Biol. Sci. 370, 20130263–20130263 (2014).
    Article  Google Scholar 

    4.
    Colchero, F. et al. The diversity of population responses to environmental change. Ecol. Lett. https://doi.org/10.1111/ele.13195 (2018).
    Article  PubMed  PubMed Central  Google Scholar 

    5.
    Coulson, T. et al. Data from: Modeling adaptive and nonadaptive responses of populations to environmental change. Am. Nat. https://doi.org/10.5061/dryad.4c117 (2017).
    Article  PubMed  PubMed Central  Google Scholar 

    6.
    Donohue, I. et al. Navigating the complexity of ecological stability. Ecol. Lett. 19, 1172–1185 (2016).
    PubMed  Article  PubMed Central  Google Scholar 

    7.
    Fernández-Chacón, A. et al. When to stay, when to disperse and where to go: Survival and dispersal patterns in a spatially structured seabird population. Ecography 36, 1117–1126 (2013).
    Article  Google Scholar 

    8.
    Sterk, M., van de Leemput, I. A. & Peeters, E. T. How to conceptualize and operationalize resilience in socio-ecological systems?. Curr. Opin. Environ. Sustain. 28, 108–113 (2017).
    Article  Google Scholar 

    9.
    Brand, F. S. & Jax, K. Focusing the meaning(s) of resilience: Resilience as a descriptive concept and a boundary object. Ecol. Soc. 12, 23 (2007).
    Article  Google Scholar 

    10.
    Barrett, L., Henzi, S. P. & Lusseau, D. Taking sociality seriously: The structure of multi-dimensional social networks as a source of information for individuals. Philos. Trans. R. Soc. B Biol. Sci. 367, 2108–2118 (2012).
    Article  Google Scholar 

    11.
    Centola, D. How Behavior Spreads: The Science of Complex Contagions. (2018).

    12.
    Firth, J. A. Considering complexity: Animal social networks and behavioural contagions. Trends Ecol. Evol. 35, 100–104 (2020).
    PubMed  Article  PubMed Central  Google Scholar 

    13.
    Kerth, G., Perony, N. & Schweitzer, F. Bats are able to maintain long-term social relationships despite the high fission–fusion dynamics of their groups. Proc. R. Soc. B Biol. Sci. 278, 2761–2767 (2011).
    Article  Google Scholar 

    14.
    Rosenthal, S. B., Twomey, C. R., Hartnett, A. T., Wu, H. S. & Couzin, I. D. Revealing the hidden networks of interaction in mobile animal groups allows prediction of complex behavioral contagion. Proc. Natl. Acad. Sci. 112, 4690–4695 (2015).
    ADS  CAS  PubMed  Article  PubMed Central  Google Scholar 

    15.
    Snijders, L., Blumstein, D. T., Stanley, C. R. & Franks, D. W. Animal social network theory can help wildlife conservation. Trends Ecol. Evol. 32, 567–577 (2017).
    PubMed  Article  PubMed Central  Google Scholar 

    16.
    Webber, Q. M. R. & Vander Wal, E. An evolutionary framework outlining the integration of individual social and spatial ecology. J. Anim. Ecol. 87, 113–127 (2018).
    PubMed  Article  PubMed Central  Google Scholar 

    17.
    Sueur, C. & Mery, F. Social Interaction in Animals: Linking Experimental Approach and Social Network Analysis (Frontiers Media SA, Lausanne, 2017).
    Google Scholar 

    18.
    LaBarge, L. R., Allan, A. T. L., Berman, C. M., Margulis, S. W. & Hill, R. A. Reactive and pre-emptive spatial cohesion in a social primate. Anim. Behav. 163, 115–126 (2020).
    Article  Google Scholar 

    19.
    Firth, J. A. et al. Wild birds respond to flockmate loss by increasing their social network associations to others. Proc. R. Soc. B Biol. Sci. 284, 20170299 (2017).
    Article  Google Scholar 

    20.
    Farine, D. R. Structural trade-offs can predict rewiring in shrinking social networks. J. Anim. Ecol. 1365–2656, 13140. https://doi.org/10.1111/1365-2656.13140 (2019).
    Article  Google Scholar 

    21.
    Maldonado-Chaparro, A. A., Alarcón-Nieto, G., Klarevas-Irby, J. A. & Farine, D. R. Experimental disturbances reveal group-level costs of social instability. Proc. R. Soc. B Biol. Sci. 285, 20181577 (2018).
    Article  Google Scholar 

    22.
    Puga-Gonzalez, I., Sosa, S. & Sueur, C. Social style and resilience of macaques’ networks, a theoretical investigation. Primates 60, 233–246 (2019).
    PubMed  Article  PubMed Central  Google Scholar 

    23.
    Williams, R. & Lusseau, D. A killer whale social network is vulnerable to targeted removals. Biol. Lett. 2, 497–500 (2006).
    PubMed  PubMed Central  Article  Google Scholar 

    24.
    Oro, D. Perturbation, Social Feedbacks, and Population Dynamics in Social Animals (Oxford Univerity Press, Oxford, 2020).
    Google Scholar 

    25.
    Firth, J. A. & Sheldon, B. C. Experimental manipulation of avian social structure reveals segregation is carried over across contexts. Proc. R. Soc. B Biol. Sci. 282, 20142350–20142350 (2015).
    Article  Google Scholar 

    26.
    Genton, C. et al. How Ebola impacts social dynamics in gorillas: A multistate modelling approach. J. Anim. Ecol. 84, 166–176 (2015).
    PubMed  Article  PubMed Central  Google Scholar 

    27.
    Leu, S. T., Farine, D. R., Wey, T. W., Sih, A. & Bull, C. M. Environment modulates population social structure: Experimental evidence from replicated social networks of wild lizards. Anim. Behav. 111, 23–31 (2016).
    Article  Google Scholar 

    28.
    Silk, J., Cheney, D. & Seyfarth, R. A practical guide to the study of social relationships: A practical guide to the study of social relationships. Evol. Anthropol. Issues News Rev. 22, 213–225 (2013).
    Article  Google Scholar 

    29.
    Brown, C. R. The ecology and evolution of colony-size variation. Behav. Ecol. Sociobiol. 70, 1613–1632 (2016).
    Article  Google Scholar 

    30.
    Rolland, C., Danchin, E. & de Fraipont, M. The evolution of coloniality in birds in relation to food, habitat, predation, and life-history traits: A comparative analysis. Am. Nat. 151, 514–529 (1998).
    CAS  PubMed  Article  Google Scholar 

    31.
    Shizuka, D. et al. Across-year social stability shapes network structure in wintering migrant sparrows. Ecol. Lett. 17, 998–1007 (2014).
    PubMed  Article  PubMed Central  Google Scholar 

    32.
    Brandl, H. B., Griffith, S. C., Farine, D. R. & Schuett, W. Wild zebra finches that nest synchronously have long-term stable social ties. J. Anim. Ecol. 1365–2656, 13082. https://doi.org/10.1111/1365-2656.13082 (2019).
    Article  Google Scholar 

    33.
    Moreno, J. L. Who Shall Survive?: A New Approach to the Problem of Human Interrelations (Nervous and Mental Disease Publishing Co, New York, 1934). .

    34.
    Scott, J. Social network analysis. Sociology 22, 109–127 (1988).
    Article  Google Scholar 

    35.
    Croft, D. P., James, R. & Krause, J. Exploring Animal Social Networks (Princeton University Press, Princeton, 2008).
    Google Scholar 

    36.
    Farine, D. R. & Whitehead, H. Constructing, conducting and interpreting animal social network analysis. J. Anim. Ecol. 84, 1144–1163 (2015).
    PubMed  PubMed Central  Article  Google Scholar 

    37.
    Ward, A. & Webster, M. Sociality: The Behaviour of Group-Living Animals (Springer, New York, 2016).
    Google Scholar 

    38.
    Whitehead, H. Analyzing Animal Societies Quantitative Methods for Vertebrate Social Analysis. (2014).

    39.
    James, R., Croft, D. P. & Krause, J. Potential banana skins in animal social network analysis. Behav. Ecol. Sociobiol. 63, 989–997 (2009).
    Article  Google Scholar 

    40.
    Hasenjager, M. J. & Dugatkin, L. A. Chapter three—social network analysis in behavioral ecology. In Advances in the Study of Behavior (ed. Naguib, M.) 47, 39–114 (Academic Press, New York, 2015).
    Google Scholar 

    41.
    Payo-Payo, A. et al. Predator arrival elicits differential dispersal, change in age structure and reproductive performance in a prey population. Sci. Rep. 8, 1971 (2018).
    ADS  CAS  PubMed  PubMed Central  Article  Google Scholar 

    42.
    Martínez-Abraín, A., Oro, D., Forero, M. G. & Conesa, D. Modeling temporal and spatial colony-site dynamics in a long-lived seabird. Popul. Ecol. 45, 133–139 (2003).
    Article  Google Scholar 

    43.
    Genovart, M., Oro, D. & Tenan, S. Immature survival, fertility, and density dependence drive global population dynamics in a long-lived species. Ecology 99, 2823–2832 (2018).
    CAS  PubMed  Article  PubMed Central  Google Scholar 

    44.
    Almaraz, P. & Oro, D. Size-mediated non-trophic interactions and stochastic predation drive assembly and dynamics in a seabird community. Ecology 92, 1948–1958 (2011).
    PubMed  Article  PubMed Central  Google Scholar 

    45.
    Shizuka, D. & Johnson, A. E. How demographic processes shape animal social networks. Behav. Ecol. https://doi.org/10.1093/beheco/arz083 (2019).
    Article  Google Scholar 

    46.
    Francesiaz, C. et al. Familiarity drives social philopatry in an obligate colonial breeder with weak interannual breeding-site fidelity. Anim. Behav. 124, 125–133 (2017).
    Article  Google Scholar 

    47.
    Cantor, M. & Farine, D. R. Simple foraging rules in competitive environments can generate socially structured populations. Ecol. Evol. 8, 4978–4991 (2018).
    PubMed  PubMed Central  Article  Google Scholar 

    48.
    Cantor, M. et al. Animal social networks: Revealing the causes and implications of social structure in ecology and evolution. https://osf.io/m62gb (2019). https://doi.org/10.32942/osf.io/m62gb.

    49.
    Anderson, D. J. & Hodum, P. J. Predator behavior favors clumped nesting in an oceanic seabird. Ecology 74, 2462–2464 (1993).
    Article  Google Scholar 

    50.
    Oro, D. Colonial seabird nesting in dense and small sub-colonies: An advantage against aerial predation. Condor 98, 848–850 (1996).
    Article  Google Scholar 

    51.
    Gil, M. A., Hein, A. M., Spiegel, O., Baskett, M. L. & Sih, A. Social information links individual behavior to population and community dynamics. Trends Ecol. Evol. 33, 535–548 (2018).
    PubMed  Article  PubMed Central  Google Scholar 

    52.
    Lewanzik, D., Sundaramurthy, A. K. & Goerlitz, H. R. Insectivorous bats integrate social information about species identity, conspecific activity and prey abundance to estimate cost–benefit ratio of interactions. J. Anim. Ecol. 88, 1462–1473 (2019).
    PubMed  PubMed Central  Article  Google Scholar 

    53.
    Doligez, B. Public information and breeding habitat selection in a wild bird population. Science 297, 1168–1170 (2002).
    ADS  CAS  PubMed  Article  PubMed Central  Google Scholar 

    54.
    Payo-Payo, A. et al. Colonisation in social species: The importance of breeding experience for dispersal in overcoming information barriers. Sci. Rep. 7, 20 (2017).
    ADS  Article  CAS  Google Scholar 

    55.
    Arganda, S., Pérez-Escudero, A. & de Polavieja, G. G. A common rule for decision making in animal collectives across species. Proc. Natl. Acad. Sci. 109, 20508–20513 (2012).
    ADS  CAS  PubMed  Article  PubMed Central  Google Scholar 

    56.
    Pérez-Escudero, A. & de Polavieja, G. G. Adversity magnifies the importance of social information in decision-making. J. R. Soc. Interface 14, 20170748 (2017).
    PubMed  PubMed Central  Article  Google Scholar 

    57.
    Maldonado-Chaparro, A. A., Blumstein, D. T., Armitage, K. B. & Childs, D. Z. Transient LTRE analysis reveals the demographic and trait-mediated processes that buffer population growth. Ecol. Lett. 21, 1693–1703 (2018).
    PubMed  PubMed Central  Article  Google Scholar 

    58.
    Pruitt, J. N. et al. Social tipping points in animal societies. Proc. R. Soc. B 285, 20181282 (2018).
    PubMed  Article  PubMed Central  Google Scholar 

    59.
    Dall, S. R. X., Houston, A. I. & McNamara, J. M. The behavioural ecology of personality: Consistent individual differences from an adaptive perspective. Ecol. Lett. 7, 734–739 (2004).
    Article  Google Scholar 

    60.
    Doering, G. N., Scharf, I., Moeller, H. V. & Pruitt, J. N. Social tipping points in animal societies in response to heat stress. Nat. Ecol. Evol. 2, 1298–1305 (2018).
    PubMed  Article  PubMed Central  Google Scholar 

    61.
    Wolf, M., van Doorn, G. S., Leimar, O. & Weissing, F. J. Life-history trade-offs favour the evolution of animal personalities. Nature 447, 581–584 (2007).
    ADS  CAS  PubMed  Article  PubMed Central  Google Scholar 

    62.
    Clobert, J., Le Galliard, J.-F., Cote, J., Meylan, S. & Massot, M. Informed dispersal, heterogeneity in animal dispersal syndromes and the dynamics of spatially structured populations. Ecol. Lett. 12, 197–209 (2009).
    PubMed  Article  PubMed Central  Google Scholar 

    63.
    Cote, J., Clobert, J., Brodin, T., Fogarty, S. & Sih, A. Personality-dependent dispersal: Characterization, ontogeny and consequences for spatially structured populations. Philos. Trans. R. Soc. B Biol. Sci. 365, 4065–4076 (2010).
    CAS  Article  Google Scholar 

    64.
    Fogarty, S., Cote, J. & Sih, A. Social personality polymorphism and the spread of invasive species: A model. Am. Nat. 177, 273–287 (2011).
    PubMed  Article  PubMed Central  Google Scholar 

    65.
    O’Shea-Wheller, T. A., Masuda, N., Sendova-Franks, A. B. & Franks, N. R. Variability in individual assessment behaviour and its implications for collective decision-making. Proc. R. Soc. B Biol. Sci. 284, 20162237 (2017).
    Article  Google Scholar 

    66.
    Nimmo, D. G., Mac Nally, R., Cunningham, S. C., Haslem, A. & Bennett, A. F. Vive la résistance: Reviving resistance for 21st century conservation. Trends Ecol. Evol. 30, 516–523 (2015).
    CAS  PubMed  Article  PubMed Central  Google Scholar 

    67.
    IUCN. Larus audouinii: BirdLife International: The IUCN Red List of Threatened Species 2018: e.T22694313A132541241. (2018). https://doi.org/10.2305/IUCN.UK.2018-2.RLTS.T22694313A132541241.en.

    68.
    Martínez-Abraín, A., Jiménez, J. & Oro, D. Pax Romana: ‘refuge abandonment’ and spread of fearless behavior in a reconciling world. Anim. Conserv. 22, 3–13 (2019).
    Article  Google Scholar 

    69.
    Genovart, M., Jover, L., Ruiz, X. & Oro, D. Offspring sex ratios in subcolonies of Audouin’s gull, Larus audouinii, with differential breeding performance. Can. J. Zool. 81, 905–910 (2003).
    Article  Google Scholar 

    70.
    Oro, D. Audouin’s gull account. In The Birds of Western Palearctic (ed. Ogilvie, M. A.) 47–61 (Oxford University Press, Oxford, 1998).
    Google Scholar 

    71.
    Genovart, M., Pradel, R. & Oro, D. Exploiting uncertain ecological fieldwork data with multi-event capture-recapture modelling: An example with bird sex assignment. J. Anim. Ecol. 81, 970–977 (2012).
    PubMed  Article  Google Scholar 

    72.
    Oro, D., Tavecchia, G. & Genovart, M. Comparing demographic parameters for philopatric and immigrant individuals in a long-lived bird adapted to unstable habitats. Oecologia 165, 935–945 (2010).
    ADS  PubMed  Article  Google Scholar 

    73.
    Hoff, P. D. Additive and multiplicative effects network models. arXiv:180708038 Stat (2018).

    74.
    Minhas, S., Hoff, P. D. & Ward, M. D. Inferential approaches for network analyses: AMEN for latent factor models. arXiv:161100460 Stat (2016).

    75.
    Warner, R. M., Kenny, D. A. & Stoto, M. A new round robin analysis of variance for social interaction data. J. Pers. Soc. Psychol. 37, 1742–1757 (1979).
    Article  Google Scholar 

    76.
    Gimenez, O. et al. Inferring animal social networks with imperfect detection. Ecol. Model. 401, 69–74 (2019).
    Article  Google Scholar 

    77.
    Hoppitt, W. J. E. & Farine, D. R. Association indices for quantifying social relationships: How to deal with missing observations of individuals or groups. Anim. Behav. 136, 227–238 (2018).
    Article  Google Scholar 

    78.
    Farine, D. R. Animal social network inference and permutations for ecologists in R using asnipe. Methods Ecol. Evol. 4, 1187–1194 (2013).
    Article  Google Scholar 

    79.
    Warnes,GR, Bolker, G, Gorjanc, G & Grothendieck, G. gdata: Various R programming tools for data manipulation. R package version (2014).

    80.
    Csardi, G. & Nepusz, T. The igraph software package for complex network research. InterJournal 20, 20 (2006).
    Google Scholar 

    81.
    Farine, D. R. A guide to null models for animal social network analysis. Methods Ecol. Evol. 8, 1309–1320 (2017).
    PubMed  PubMed Central  Article  Google Scholar 

    82.
    Ginsberg, J. R. & Young, T. P. Measuring association between individuals or groups in behavioural studies. Anim. Behav. 44, 377–379 (1992).
    Article  Google Scholar 

    83.
    Cairns, S. J. & Schwager, S. J. A comparison of association indices. Anim. Behav. 35, 1454–1469 (1987).
    Article  Google Scholar  More