More stories

  • in

    Plastic plumage colouration in response to experimental humidity supports Gloger’s rule

    West-Eberhard, M. J. Developmental Plasticity and Evolution (Oxford University Press, 2003).Book 

    Google Scholar 
    Piersma, T. & Van Gils, J. A. The Flexible Phenotype: A Body-Centred Integration of Ecology, Physiology, and Behaviour (Oxford University Press, 2011).
    Google Scholar 
    Piersma, T. & Drent, J. Phenotypic flexibility and the evolution of organismal design. Trends Ecol. Evol. 18, 228–233 (2003).Article 

    Google Scholar 
    Tabari, H. Climate change impact on flood and extreme precipitation increases with water availability. Sci. Rep. 10, 1–10 (2020).
    Google Scholar 
    Beck, H. E. et al. Present and future Köppen–Geiger climate classification maps at 1-km resolution. Sci. Data 5, 180214 (2018).Article 

    Google Scholar 
    Bogert, C. M. Thermoregulation in reptiles, a factor in evolution. Evolution 3, 195–211 (1949).Article 
    CAS 

    Google Scholar 
    Rensch, B. Das Prinzip geographischer Rassenkreise und das Problem der Artbildung (Gebrueder Borntraeger, 1929).
    Google Scholar 
    Clusella Trullas, S., van Wyk, J. H. & Spotila, J. R. Thermal melanism in ectotherms. J. Therm. Biol. 32, 235–245 (2007).Article 

    Google Scholar 
    Delhey, K. A review of Gloger’s rule, an ecogeographical rule of colour: Definitions, interpretations and evidence. Biol. Rev. 94, 1294–1316 (2019).
    Google Scholar 
    Stuart-Fox, D., Newton, E. & Clusella-Trullas, S. Thermal consequences of colour and near-infrared reflectance. Philos. Trans. R. Soc. B: Biol. Sci. 372, 20160345 (2017).Article 

    Google Scholar 
    Friedman, N. R. & Remês, V. Ecogeographical gradients in plumage coloration among Australasian songbird clades. Glob. Ecol. Biogeogr. 26, 261–274 (2017).Article 

    Google Scholar 
    Delhey, K. Darker where cold and wet: Australian birds follow their own version of Gloger’s rule. Ecography 41, 673–683 (2018).Article 

    Google Scholar 
    Galván, I., Rodríguez-Martínez, S. & Carrascal, L. M. Dark pigmentation limits thermal niche position in birds. Funct. Ecol. 32, 1531–1540 (2018).Article 

    Google Scholar 
    Medina, I. et al. Reflection of near-infrared light confers thermal protection in birds. Nat. Commun 9, 3610 (2018).Article 
    ADS 

    Google Scholar 
    Aldrich, J. W. & James, F. C. Ecogeographic variation in the American Robin (Turdus migratorius). Auk 108, 230–249 (1991).
    Google Scholar 
    Morales, H. E. et al. Neutral and selective drivers of colour evolution in a widespread Australian passerine. J. Biogeogr. 44, 522–536 (2017).Article 

    Google Scholar 
    Griffith, S. C., Owens, I. P. & Burke, T. Environmental determination of a sexually selected trait. Nature 400, 358–360 (1999).Article 
    ADS 
    CAS 

    Google Scholar 
    Fargallo, J. A., Laaksonen, T., Korpimäki, E. & Wakamatsu, K. A melanin-based trait reflects environmental growth conditions of nestling male Eurasian kestrels. Evol. Ecol. 21, 157–171 (2007).Article 

    Google Scholar 
    Fargallo, J. A., Martínez, F., Wakamatsu, K., Serrano, D. & Blanco, G. Sex-dependent expression and fitness consequences of sunlight derived color phenotypes. Am. Nat. 191, 726–743 (2018).Article 

    Google Scholar 
    Beebe, W. Geographic variation in birds, with especial reference to the effects of humidity. Zoologica 1, 3–41 (1907).
    Google Scholar 
    Bieber, H. Fellverdunklung beim hauskaninchen nach kälteeinwirkung. Zeitschrift für Säugetierkunde 38, 33–38 (1972).
    Google Scholar 
    Johnston, R. F. & Selander, R. K. House sparrows: Rapid evolution of races in North America. Science 144, 548–550 (1964).Article 
    ADS 
    CAS 

    Google Scholar 
    Galván, I., Wakamatsu, K. & Alonso-Álvarez, C. Black bib size is associated with feather content of pheomelanin in male house sparrows. Pigment Cell Melanoma Res. 27, 1159–1161 (2014).Article 

    Google Scholar 
    Endler, J. A. On the measurement and classification of colour in studies of animal colour patterns. Biol. J. Linn. Soc. 41, 315–352 (1990).Article 

    Google Scholar 
    Montgomerie, R. Analyzing colors. In Bird Colouration I. Mechanisms and Measurements (eds Hill, E. G. & McGraw, K. J.) (Harvard University Press, 2006).
    Google Scholar 
    McGraw, K. J., Dale, J. & Mackillop, E. A. Social environment during molt and the expression of melanin-based plumage pigmentation in male house sparrows (Passer domesticus). Behav. Ecol. Sociobiol. 53, 116–122 (2003).Article 

    Google Scholar 
    Lessells, C. M. & Boag, P. T. Unrepeatable repeatabilities a common mistake. Auk 104, 116–121 (1987).Article 

    Google Scholar 
    Anderson, T. R. Biology of the Ubiquitous House Sparrow (Oxford University Press, 2006).Book 

    Google Scholar 
    Gelman, A. & Hill, J. Data Analysis Using Regression and Multilevel/Hierarchical Models (Cambridge University Press, 2006).Book 

    Google Scholar 
    Nakagawa, S., Ockendon, N., Gillespie, D. O., Hatchwell, B. J. & Burke, T. Assessing the function of house sparrows’ bib size using a flexible meta-analysis method. Behav. Ecol. 18, 831–840 (2007).Article 

    Google Scholar 
    Hill, G. E. & McGraw, K. J. Bird Coloration, Volume I: Mechanisms and Measurements (Harvard University Press, 2006).Book 

    Google Scholar 
    D’Alba, L. & Shawkey, M. D. Melanosomes: Biogenesis, properties, and evolution of an ancient organelle. Physiol. Rev. 99, 1–19 (2018).Article 

    Google Scholar 
    Delhey, K., Burger, C., Fiedler, W. & Peters, A. Seasonal changes in colour: A comparison of structural, melanin- and carotenoid-based plumage colours. PLoS ONE 5, e11582 (2010).Article 
    ADS 

    Google Scholar 
    Galván, I., Mousseau, T. A. & Møller, A. P. Bird population declines due to radiation exposure at Chernobyl are stronger in species with pheomelanin-based coloration. Oecologia 165, 827–835 (2011).Article 
    ADS 

    Google Scholar 
    Meunier, J., Pinto, S. F., Burri, R. & Roulin, A. Eumelanin-based coloration and fitness parameters in birds: A meta-analysis. Behav. Ecol. Sociobiol. 65, 559–567 (2011).Article 

    Google Scholar 
    Roulin, A., Almasi, B., Meichtry-Stier, K. S. & Jenni, L. Eumelanin- and pheomelanin-based colour advertise resistance to oxidative stress in opposite ways. J. Evol. Biol. 24, 2241–2247 (2011).Article 
    CAS 

    Google Scholar 
    Gasparini, J. et al. Strength and cost of an induced immune response are associated with a heritable melanin-based colour trait in female tawny owls. J. Anim. Ecol. 78, 608–616 (2009).Article 

    Google Scholar 
    Fargallo, J. A. et al. Sex-specific phenotypic integration: Endocrine profiles, coloration, and behavior in fledgling boobies. Behav. Ecol. 25, 76–87 (2013).Article 

    Google Scholar 
    Wittkopp, P. J. & Beldade, P. Development and evolution of insect pigmentation: Genetic mechanisms and the potential consequences of pleiotropy. Semin. Cell Dev. Biol. 20, 65–71 (2009).Article 
    CAS 

    Google Scholar 
    Hubbard, J. K., Uy, J. A. C., Hauber, M. E., Hoekstra, H. E. & Safran, R. J. Vertebrate pigmentation: From underlying genes to adaptive function. Trends Genet. 26, 231–239 (2010).Article 
    CAS 

    Google Scholar 
    McKinnon, J. S. & Pierotti, M. E. Colour polymorphism and correlated characters: Genetic mechanisms and evolution. Mol. Ecol. 19, 5101–5125 (2010).Article 

    Google Scholar 
    Poston, J. P., Hasselquist, D., Stewart, I. R. & Westneat, D. F. Dietary amino acids influence plumage traits and immune responses of male house sparrows, Passer domesticus, but not as expected. Anim. Behav. 70, 1171–1181 (2005).Article 

    Google Scholar 
    McGraw, K. J. Dietary mineral content influences the expression of melanin-based ornamental coloration. Behav. Ecol. 18, 137–142 (2007).Article 

    Google Scholar 
    Fargallo, J. A., Martínez-Padilla, J., Toledano-Díaz, A., Santiago-Moreno, J. & Dávila, J. A. Sex and testosterone effects on growth, immunity and melanin coloration of nestling Eurasian kestrels. J. Anim. Ecol. 76, 201–209 (2007).Article 

    Google Scholar 
    Fitze, P. S. & Richner, H. Differential effects of a parasite on ornamental structures based on melanins and carotenoids. Behav. Ecol. 13, 401–407 (2002).Article 

    Google Scholar 
    Roulin, A., Altwegg, R., Jensen, H., Steinsland, I. & Schaub, M. Sex-dependent selection on an autosomal melanic female ornament promotes the evolution of sex ratio bias. Ecol. Lett. 13, 616–626 (2010).Article 

    Google Scholar 
    Sharma, A. Effect of ambient humidity on UV/visible photodegradation of melanin thin films. Photochem. Photobiol. 86, 852–855 (2010).Article 
    CAS 

    Google Scholar 
    Burtt, E. H. The adaptiveness of animal colors. Bioscience 31, 723–729 (1981).Article 

    Google Scholar 
    Heppner, F. The metabolic significance of differential absorption of radiant energy by black and white birds. Condor 72, 50–59 (1970).Article 

    Google Scholar 
    Clusella-Trullas, S., Terblanche, J. S., Blackburn, T. M. & Chown, S. L. Testing the thermal melanism hypothesis: A macrophysiological approach. Funct. Ecol. 22, 232–238 (2008).Article 

    Google Scholar 
    Zink, R. M. & Remsen, J. V. Evolutionary processes and patterns of geographic variation in birds. Curr. Ornithol. 4, 1–69 (1986).
    Google Scholar 
    Burtt, E. H. & Ichida, J. M. Gloger’s rule, feather-degrading bacteria, and color variation among song sparrows. Condor 106, 681–686 (2004).Article 

    Google Scholar 
    Ruiz-De-Castaneda, R., Burtt, E. H. Jr., Gonzalez-Braojos, S. & Moreno, J. Bacterial degradability of an intrafeather unmelanized ornament: A role for feather-degrading bacteria in sexual selection?. Biol. J. Linn. Soc. 105, 409–419 (2012).Article 

    Google Scholar 
    Goldstein, G. et al. Bacterial degradation of black and white feathers. Auk 121, 656–659 (2004).Article 

    Google Scholar 
    Ducrest, A. L., Keller, L. & Roulin, A. Pleiotropy in the melanocortin system, coloration and behavioural syndromes. Trends Ecol. Evol. 23, 502–510 (2008).Article 

    Google Scholar 
    Kim, S. Y., Fargallo, J. A., Vergara, P. & Martínez-Padilla, J. Multivariate heredity of melanin-based coloration, body mass and immunity. Heredity 111, 139–146 (2013).Article 
    CAS 

    Google Scholar 
    Horrocks, N. P. C. et al. Environmental proxies of antigen exposure explain variation in immune investment better than indices of pace of life. Oecologia 177, 281–290 (2015).Article 
    ADS 

    Google Scholar 
    McLean, N., Van Der Jeugd, H. P. & van de Pol, M. High intra-specific variation in avian body condition responses to climate limits generalisation across species. PLoS ONE 13, e0192401 (2018).Article 

    Google Scholar 
    Gardner, J. L. et al. Spatial variation in avian bill size is associated with humidity in summer among Australian passerines. Clim. Change Responses 3, 11 (2016).Article 

    Google Scholar 
    Gerson, A. R. et al. Flight at low ambient humidity increases protein catabolism in migratory birds. Science 333, 1434–1436 (2011).Article 
    ADS 
    CAS 

    Google Scholar  More

  • in

    Reply to: Plant traits alone are good predictors of ecosystem properties when used carefully

    Plant Ecology and Nature Conservation Group, Wageningen University, Wageningen, the NetherlandsFons van der Plas & Liesje MommerSystematic Botany and Functional Biodiversity, Life Science, Leipzig University, Leipzig, GermanyThomas Schröder-Georgi, Alexandra Weigelt, Kathryn Barry & Christian WirthGerman Centre for Integrative Biodiversity Research Halle-Jena-Leipzig, Leipzig, GermanyAlexandra Weigelt, Kathryn Barry, Adriana Alzate, Nico Eisenhauer, Anke Hildebrandt, Christiane Roscher & Christian WirthTerrestrial Ecology Research Group, School of Life Sciences Weihenstephan, Technical University of Munich, Munich, GermanySebastian Meyer & Wolfgang WeisserAquaculture and Fisheries Group, Wageningen University and Research Centre, Wageningen, the NetherlandsAdriana AlzateAgroécologie, AgroSup Dijon, Institut National de la Recherche Agronomique, Université de Bourgogne, Université de Bourgogne Franche-Comté, Dijon, FranceRomain L. BarnardEidgenössische Technische Hochschule Zürich, Zurich, SwitzerlandNina BuchmannDepartment of Experimental Plant Ecology, Institute for Water and Wetland Research, Radboud University Nijmegen, Nijmegen, the NetherlandsHans de KroonInstitute of Ecology and Evolution, University Jena, Jena, GermanyAnne Ebeling & Winfried VoigtInstitute of Biology, Leipzig University, Leipzig, GermanyNico EisenhauerHumboldt-Universität zu Berlin, Berlin, GermanyChristof EngelsInstitute of Plant Sciences, University of Bern, Bern, SwitzerlandMarkus FischerMax Planck Institute for Biogeochemistry, Jena, GermanyGerd Gleixner, Ernst-Detlef Schulze & Christian WirthHelmholtz Centre for Environmental Research, Leipzig, GermanyAnke HildebrandtFriedrich Schiller University Jena, Jena, GermanyAnke HildebrandtGeoecology, University of Tübingen, Tübingen, GermanyEva Koller-France & Yvonne OelmannInstitute of Geography and Geoecology, Karlsruhe Institute of Technology, Karlsruhe, GermanySophia Leimer & Wolfgang WilckeEcotron Européen de Montpellier, Centre National de la Recherche Scientifique, Montferrier-sur-Lez, FranceAlexandru MilcuCentre d’Ecologie Fonctionnelle et Evolutive, Unité Mixte de Recherche 5175 (Centre National de la Recherche Scientifique-Université de Montpellier-Université Paul-Valéry Montpellier-Ecole Pratique des Hautes Etudes), Montpellier, FranceAlexandru MilcuDepartment of Evolutionary Biology and Environmental Studies, University of Zurich, Zurich, SwitzerlandPascal A. NiklausUFZ, Helmholtz Centre for Environmental Research, Department Physiological Diversity, Leipzig, GermanyChristiane RoscherInstitute of Landscape Ecology, University of Münster, Münster, GermanyChristoph ScherberCentre for Biodiversity Monitoring, Zoological Research Museum Alexander Koenig, Bonn, GermanyChristoph ScherberGeobotany, Faculty of Biology, University of Freiburg, Freiburg, GermanyMichael Scherer-LorenzenCentre of Biodiversity and Sustainable Land Use, University of Göttingen, Göttingen, GermanyStefan ScheuJ.F. Blumenbach Institute of Zoology and Anthropology, Animal Ecology, University of Göttingen, Göttingen, GermanyStefan ScheuDepartment of Geography, University of Zurich, Zurich, SwitzerlandBernhard SchmidInstitute of Ecology, College of Urban and Environmental Sciences, Peking University, Beijing, ChinaBernhard SchmidLeuphana University Lüneburg, Institute of Ecology, Lüneburg, GermanyVicky TempertonAgroecology, Department of Crop Sciences, University of Göttingen, Göttingen, GermanyTeja TscharntkeF.v.d.P. wrote the initial draft of the manuscript. T.S.-G., A.W., K.B., S.M., A.A., R.L.B., N.B., H.d.K., A.E., N.E., C.E., M.F., G.G., A.H., E.K.-F., S.L., A.M., L.M., P.A.N., Y.O., C.R., C.S., M.S.-L., S.S., B.S., E.-D.S., V.T., T.T., W.V., W. Weisser, W. Wilcke and C.W. helped edit the manuscript. More

  • in

    The impact of the striped field mouse’s range expansion on communities of native small mammals

    Wilkinson, D. M. Dispersal biogeography. Encyclopedia of Life Science. (Nature Publishing Group, 2001).Jan, P.L. et al. Range expansion is associated with increased survival and fecundity in a long-lived bat species. Proc. R. Soc. B. 286, 1–9. https://doi.org/10.1098/rspb.2019.0384 (2019).Article 

    Google Scholar 
    IUCN. IUCN Guidelines for the Prevention of Biodiversity Loss Caused by Alien Invasive Species. https://portals.iucn.org/library/node/12413(2000).McKinney, M. L. Urbanization as a major cause of biotic homogenization. Biological Conservation 127, 247–260. https://doi.org/10.1016/j.biocon.2005.09.005 (2006).
    Google Scholar 
    Galko, J. et al. Invázne a nepôvodné druhy v lesoch Slovenska: hmyz—huby—rastliny. (Národné lesnícke centrum, 2018).Lockwood, J. L., Hoopes, M. F. & Marchetti, M. P. Invasion ecology_draft_2ed. (John Wiley & Sons, 2013).Colautti, R. I. & MacIsaac, H. J. A neutral terminology to define ‘invasive’ species: Defining invasive species. Divers. Distrib. 10, 135–141. https://doi.org/10.1111/j.1366-9516.2004.00061.x (2004).Article 

    Google Scholar 
    Ambros, M., Dudich, A., MIKLÓS, P., Stollmann, A. & Žiak, D. Ryšavka tmavopása (Apodemus agrarius)–novỳ druh cicavca Podunajskej roviny (Rodentia: Muridae). Lynx, series nova 41, (2010).Bradley, B. A. et al. Disentangling the abundance—impact relationship for invasive species. Proc. Natl. Acad. Sci. 116, 9919–9924. https://doi.org/10.5281/zenodo.2605254 (2019).Article 
    ADS 
    CAS 

    Google Scholar 
    Nackley, L. L., West, A. G., Skowno, A. L. & Bond, W. J. The nebulous ecology of native invasions. Trends Ecol. Evol. 32, 814–824. https://doi.org/10.1016/j.tree.2017.08.003 (2017).Article 

    Google Scholar 
    Archer, S. Tree-grass dynamics in a Prosopis-thornscrub savanna parkland: reconstructing the past and predicting the future. Ecoscience 2, 83–99. https://doi.org/10.1080/11956860.1995.11682272 (1995).Article 

    Google Scholar 
    Wigley, B. J., Bond, W. J. & Hoffman, M. T. Thicket expansion in a South African savanna under divergent land use: local vs. global drivers? Global Change Biol. 16, 964–976. https://doi.org/10.1111/j.1365-2486.2009.02030.x (2010).Spiegel, C. S., Hart, P. J., Woodworth, B. L., Tweed, E. J. & LeBrun, J. J. Distribution and abundance of forest birds in low-altitude habitat on Hawai’i Island: Evidence for range expansion of native species. Bird Conserv. Int. 16, 175–185. https://doi.org/10.1017/S0959270906000244 (2006).Article 

    Google Scholar 
    Livezey, K. B. Range expansion of Barred Owls, part II: facilitating ecological changes. Am. Midland Nat. 323–349 (2009).Veech, J. A., Small, M. F. & Baccus, J. T. The effect of habitat on the range expansion of a native and an introduced bird species. J. Biogeogr. 38, 69–77. https://doi.org/10.1111/j.1365-2699.2010.02397.x (2011).Article 

    Google Scholar 
    Krupa, J. J. & Haskins, K. E. Invasion of the meadow vole (Microtus pennsylvanicus) in southeastern Kentucky and its possible impact on the southern bog lemming (Synaptomys cooperi). American Midland Naturalist 14–22 (1996).Jareño, D. et al. Factors associated with the colonization of agricultural areas by common voles Microtus arvalis in NW Spain. Biol. Invasions 17, 2315–2327. https://doi.org/10.1007/s10530-015-08774 (2015).Article 

    Google Scholar 
    Malygin, V. M., Baskevich, M. I. & Khlyap, L. A. Invasions of the common vole sibling species. Russ. J. Biol. Invas. 11, 47–65 (2020).Article 

    Google Scholar 
    Tong, X., Wang, R. & Chen, X.-Y. Expansion or invasion? A Response to Nackley et al. Trends Ecol. Evol. 33, 234–235. https://doi.org/10.1016/j.tree.2018.01.008 (2018).Thompson, K. & Davis, M. A. Why research on traits of invasive plants tell us very little. Trends Ecol. Evol. 24,115–116. https://doi.org/10.1016/j.tree.2011.01.007 (2011).Davis, M. A. & Thompson, K. Eight ways to be a colonizer; two ways to be an invader: A proposed nomenclature scheme for invasion ecology. Bull. Ecol. Soc. Am. 81, 226–230 (2000).
    Google Scholar 
    Davis, M. A., Thompson, K., Grime, J. P. & Charles, S. Elton and the dissociation of invasion ecology from the rest of ecology. Diversity and Distribution 7, 97–102. https://doi.org/10.1046/j.1472-4642.2001.00099.x (2001).Article 

    Google Scholar 
    Davis, M. A. Invasion biology. (Oxford University Press on Demand, 2009).Davis, M. A. et al. Don’t judge species on their origins. Nature 474, 153–154 (2011).Article 
    CAS 

    Google Scholar 
    Klempa, B. et al. Complex evolution and epidemiology of Dobrava-Belgrade hantavirus: Definition of genotypes and their characteristics. Adv. Virol. 158, 521–529. https://doi.org/10.1007/s00705-012-1514-5 (2013).CAS 

    Google Scholar 
    Kraljik, J. et al. Genetic diversity of Bartonella genotypes found in the striped field mouse (Apodemus agrarius) in Central Europe. Parasitology 143, 1437–1442. https://doi.org/10.1017/S0031182016000962 (2016).Article 

    Google Scholar 
    Latinne, A. et al. Phylogeography of the striped field mouse, Apodemus agrarius (Rodentia: Muridae), throughout its distribution range in the Palaearctic region. Mamm. Biol. 100, 19–31. https://doi.org/10.1007/s42991-019-00001-0 (2020).Article 

    Google Scholar 
    Dudich, A. & Szabó, I. Über die Verbreitung der Hystrichopsylla Taschenberg, 1880 (Siphonaptera) in Ungarn. Folia Entomol. Hung 45, 27–32 (1984).
    Google Scholar 
    Dudich, A. Dynamika areálu ryšavky tmavopásej (Apodemus agrarius Pall.)–expanzia či invázia. Pp.: 53–62. Invázie a invázne organizmy. SEKOS pre SNK SCOPE, Nitra (1997).Karaseva, E. V., Tikhonova, G. N. & Bogomolov, P. L. Distribution of the Striped field mouse (Apodemus agrarius) and peculiarities of its ecology in different parts of its range. Zoologičeskij žurnal 71, 106–115 (1992).
    Google Scholar 
    Polechová, J. & Graciasová, R. Návrat myšice temnopásé, Apodemus agrarius (Rodentia: Muridae) na jižní Moravu. Lynx (Praha), ns 31, 153–155 (2000).Bryja, J. & Řehák, Z. Další doklady současné expanze areálu myšice temnopásé (Apodemus agrarius) na Moravě. Lynx (Praha), ns (2002).Herzig-Straschil, B., Bihari, Z. & Spitzenberger, F. Recent changes in the distribution of the field mouse (Apodemus agrarius) in the western part of the Carpathian basin. Annalen des Naturhistorischen Museums in Wien. Serie B für Botanik und Zoologie 421–428 (2003).Dudich, A., Ambros, M., Stollmann, A., Uhrin, M. & Urban, P. Ryšavka tmavopása Apodemus agrarius (Pallas) v Novohrade. Príroda okresu Veľký Krtíš – 15 rokov od celoslovenského tábora ochrancov prírody 110–115 (2003).Horáček, I. & Ložek, V. Biostratigraphic investigation in the Hámorská cave (Slovak karst). Pp.: 49–60. Krasové sedimenty. Fosilní záznam klimatickỳch oscilací a změn prostředí. Knihovna České speleologické společnosti, Svazek 21, (1993).Pazonyi, P. & Kordos, L. Late Eemian (Late Pleistocene) vertebrate fauna from the Horváti-lik (Uppony, NE Hungary). Fragmenta Palaeontologica Hungarica 22, 107–117 (2004).
    Google Scholar 
    Obuch, J., Danko, S. & Noga, M. Recent and subrecent diet of the barn owl (Tyto alba) in Slovakia. Slovak Raptor J. 10, 1–50 . https://doi.org/10.1515/srj-2016-0003 (2016).Article 

    Google Scholar 
    Obuch, J. Temporal changes in proportions of small mammals in the diet of the mammalian and avian predators in Slovakia. Lynx, n. s 55, 86–106. https://doi.org/10.37520/lynx.2021.007 (2022).Niethammer, J. Die Verbreitung der Brandmaus (Apodemus agrarius) in der Bundesrepublik Deutschland. Acta Sc. Nat. Brno 10, 43–55 (1976).
    Google Scholar 
    von Lehmann, E. Die Brandmaus in Hessen als Beispiel für die Problematik der Verbreitungsgrenzen vieler Säugetierarten. Natur und Museum 106, 112–117.Farský, O. Úlovky myšice temnopásé, Apodemus agrarius (Pallas), na Moravě a ve Slezsku v letech 1920 až 1940 [Caughts of the striped field mouse, Apodemus agrarius (Pallas), in Moravia and Silesia in the years 1920–1940]. Lynx, n. s 5, 11–18 (1965).Heroldová, M., Homolka, M. & Zejda, J. Některé nepublikované nálezy Apodemus agrarius v Čechách a na Moravě v návaznosti na současnỳ stav znalostí o jejím rozšíření (Rodentia: Muridae). Lynx, series nova 44, (2013).Ambros, M., Dudich, A. & Stollmann, A. Fauna drobných hmyzožravcov a hlodavcov (Insectivora, Rodentia) vybraných mokraďných biotopov južného Slovenska. Rosalia (Nitra) 14, 195–202 (1999).
    Google Scholar 
    Balát, F. Potrava sovy pálené na jižní Moravě a na jižním Slovensku [Food of Barn owl on the south Moravia and south Slovakia]. Zoologické listy 5, 237–256 (1956).
    Google Scholar 
    Baláž, I., Stollmann, A., Ambros, M. & Dudich, A. Drobné cicavce rezervácie Lohótsky močiar. Chránené územia Slovenska 58, 27–29 (2003).
    Google Scholar 
    Bridišová, Z., Baláž, I. & Ambros, M. Drobné cicavce prírodnej rezervácie Alúvium Žitavy [Small mammals of Alúvium Žitavy natural reservation]. Chránené územia Slovenska 69, 7–9 (2006).
    Google Scholar 
    Dudich, A., Lysỳ, J. & Štollmann, A. Súčasné poznatky o rozšírení drobnỳch zemnỳch cicavcov (Insectivora, Rodentia) južnej časti Podunajskej nížiny. Spravodaj Oblastného múzea v Komárne, Prírodné vedy 5, 157–186 (1985).
    Google Scholar 
    Kristofik, J. Small mammals in floodplain forests. Folia Zoologica (Czech Republic) (1999).Méhely, L. Két új poczokfaj a magyar faunában. Állattani közlemények 7, 3–14 (1908).
    Google Scholar 
    Noga, M. The wintering and food ecology of Long-eared Owl in South-Western part of Slovakia (Comenius University in Bratislava, 2007).
    Google Scholar 
    Pachinger, K., Novackỳ, M., Facuna, V. & Ambruš, B. Dynamika a zloženie synúzií mikromammálií na izolovanỳch ostrovoch vnútozemskej delty Dunaja v oblasti vodného diela Gabčíkovo. Acta Environ. Universitatis Comenianae 9, 71–77 (1997).
    Google Scholar 
    Poláčiková, Z. Small terrestrial mammals’ (Eulipotyphla, Rodentia) synusia of selected localities in western Slovakia. Ekológia (Bratislava) 29, 131–139. https://doi.org/10.4149/ekol_2010_02_131 (2010).
    Google Scholar 
    Reiterová, K. et al. Úloha drobnỳch cicavcov–dôležitỳch rezervoárov v cirkulácii larválnej toxoplazmózy. Slovenskỳ Veterinárny Časopis 4, 217–222 (2010).
    Google Scholar 
    Stanko, M., Mošanský, L. & Fričová, J. Small mammal communities (Eulipotyphla, Rodentia) of the middle part of alluvium Ipeľ river (Lučenská and Ipeľská basins). Ochrana prírody 26, 43–52 (2010).
    Google Scholar 
    Spitzenberger, F. & Engelberg, S. A new look at the dynamic western distribution border of Apodemus agrarius in Central Europe (Rodentia: Muridae). Lynx, series nova 45, (2014).Sládkovičová, V. H., Žiak, D. & Miklós, P. Synúzie drobných zemných cicavccov mokradných biotopov Podunajskej roviny. Folia faunistica Slovaca 18, 13–19 (2013).
    Google Scholar 
    Tulis, F. et al. Expansion of the Striped field mouse (Apodemus agrarius) in the south-western Slovakia during 2010–2015. Folia Oecologica 43, 64–73 (2016).
    Google Scholar 
    Ambros, M. et al. Zmeny v rozšírení ryšavky tmavopásej (Apodemus agrarius) na Slovensku. in Zborník príspevkov z vedeckého kongresu ‘Zoológia 2022’ 10 (2022).Dudich, A. Ektoparazitofauna cicavcov a vtákov južnej časti Podunajskej nížiny so zreteľom na Žitný ostrov. 1. Siphonaptera. Žitnoostrovské múzeum Dunajská Streda—Spravodaj múzea 9, 61–96 (1986).Dudich, A. Príspevok k poznaniu drobných zemných cicavcov (Insectivora, Rodentia) a ich ektoparazitov (Acarina, Anoplura, Siphonaptera) okolia ŠPR Čenkovská lesostep (Podunajská nížina). Iuxta Danubium (Komárno) 10, 186–191 (1993).
    Google Scholar 
    Cyprich, D., Krumpál, M. & Dúha, J. Blchy (Siphonaptera) cicavcov (Mammalia) Štátnej prírodnej rezervácii Šúr. Ochrana prírody 8, 241–289 (1987).
    Google Scholar 
    Lapin, M., Faško, P., Melo, M., Štastný, P. & Tomlain, J. Climate zones. in Landscape Atlas of the Slovak Republic (Harmanec: VKÚ, 2002).Faško, P. & Štastný, P. Average annual precipitation. in Landscape Atlas of the Slovak Republic (2002).Russell, J. C., Stjernman, M., Lindström, Å. & Smith, H. G. Community occupancy before-after-control-impact (CO-BACI) analysis of Hurricane Gudrun on Swedish forest birds. Ecol. Appl. 25, 685–694.  https://doi.org/10.1890/14-0645.1 (2015).Article 

    Google Scholar 
    Desrosiers, M., Planas, D. & Mucci, A. Short-term responses to watershed logging on biomass mercury and methylmercury accumulation by periphyton in boreal lakes. Can. J. Fish. Aquat. Sci. 63, 1734–1745. https://doi.org/10.1139/f06-077 (2006).Article 
    CAS 

    Google Scholar 
    Hanisch, J. R., Tonn, W. M., Paszkoswki, C. A. & Scrimgeour, G. J. Stocked trout have minimal effects on littoral invertebrate assemblages of productive fish-bearing lakes: A whole-lake BACI study: Stocked trout have minimal effects on littoral invertebrates. Freshw. Biol. 58, 895–907. https://doi.org/10.1111/fwb.12095 (2013).Article 

    Google Scholar 
    Louhi, P., Mäki-Petäys, A., Erkinaro, J., Paasivaara, A. & Muotka, T. Impacts of forest drainage improvement on stream biota: A multisite BACI-experiment. For. Ecol. Manage. 260, 1315–1323. https://doi.org/10.1016/j.foreco.2010.07.024 (2010).Article 

    Google Scholar 
    Conner, M. M., Saunders, W. C., Bouwes, N. & Jordan, C. Evaluating impacts using a BACI design, ratios, and a Bayesian approach with a focus on restoration. Environ Monit Assess 188, 555. https://doi.org/10.1007/s10661-016-5526-6
    (2016).Article 

    Google Scholar 
    Popescu, V. D., de Valpine, P., Tempel, D. & Peery, M. Z. Estimating population impacts via dynamic occupancy analysis of Before-After Control–Impact studies. Ecol. Appl. 22, 1389–1404. https://doi.org/10.1890/11-1669.1 (2012).Article 

    Google Scholar 
    Horváth, G. F. & Herczeg, R. Site occupancy response to natural and anthropogenic disturbances of root vole: Conservation problem of a vulnerable relict subspecies. J. Nat. Conserv. 21, 350–358. https://doi.org/10.1016/j.jnc.2013.03.004 (2013).Article 

    Google Scholar 
    Pounder, K. C. et al. Novel Hantavirus in Wildlife, United Kingdom. Emerg. Infect. Dis. 19, 673–675. https://doi.org/10.3201/eid1904.121057 (2013).Article 

    Google Scholar 
    Kreisinger, J., Bastien, G., Hauffe, H. C., Marchesi, J. & Perkins, S. E. Interactions between multiple helminths and the gut microbiota in wild rodents. Phil. Trans. R. Soc. B 370, 20140295. https://doi.org/10.1098/rstb.2014.0295 (2015).Article 

    Google Scholar 
    Kim, H.-C. et al. Hantavirus surveillance and genetic diversity targeting small mammals at Camp Humphreys, a US military installation and new expansion site Republic of Korea. PLoS ONE 12, e0176514. https://doi.org/10.1371/journal.pone.0176514 (2017).Article 

    Google Scholar 
    Burnham, K. P., Anderson, D. R. & Burnham, K. P. Model selection and multimodel inference: a practical information-theoretic approach. (Springer, 2002).Sugiura, N. Further analysis of the data by Akaike’s information criterion and the finite corrections: Further analysis of the data by akaike’ s. Commun. Stat. Theory Methods 7, 13–26 (1978).Article 
    MATH 

    Google Scholar 
    Hurvich, C. M. & Tsai, C.-L. Regression and time series model selection in small samples. Biometrika 76, 297–307 (1989).Article 
    MathSciNet 
    MATH 

    Google Scholar 
    Morris, E. K. et al. Choosing and using diversity indices: insights for ecological applications from the German Biodiversity Exploratories. Ecol. Evol. 4, 3514–3524. https://doi.org/10.1002/ece3.1155 (2014).Article 

    Google Scholar 
    Ingram, J. C. Berger–Parker Index. in Encyclopedia of Ecology 332–334 (Elsevier, 2008). https://doi.org/10.1016/B978-008045405-4.00091-4.Bürkner, P.-C. brms : An R package for bayesian multilevel models using Stan. J. Stat. Soft. 80, (2017).Wang, Y., Naumann, U., Eddelbuettel, D., Wilshire, J. & Warton, D. mvabund: Statistical Methods for Analysing Multivariate Abundance Data. (2022).Warton, D. I., Thibaut, L. & Wang, Y. A. The PIT-trap—A “model-free” bootstrap procedure for inference about regression models with discrete, multivariate responses. PLoS ONE 12, e0181790. https://doi.org/10.1371/journal.pone.0181790 (2017).Article 

    Google Scholar 
    Oksanen, J. et al. vegan: Community Ecology Package. (2022).R Core Team. R: A Language and Environment for Statistical Computing. (R Foundation for Statistical Computing, 2022).Percie du Sert, N. et al. Reporting animal research: Explanation and elaboration for the ARRIVE guidelines 2.0. PLoS Biol. 18, e3000411. https://doi.org/10.1371/journal.pbio.3000411 (2020).Szacki, J. & Liro, A. Movements of small mammals in the heterogeneous landscape. Landsc. Ecol. 5, 219–224 (1991).Article 

    Google Scholar 
    Szacki, J., Babinska-Werka, J. & Liro, A. The influence of landscape spatial structure on small mammal movements. Acta Theriol. 2, (1993).Pimentel, D., Pimentel, M. & Wilson, A. Plant, animal, and microbe invasive species in the United States and World. Biol. Invasions 193 (2007).Valéry, L., Hervé, F., Lefeuvre, J. C. & Simberloff, D. Invasive species can also be native. Trends Ecol. Evol. https://doi.org/10.1016/j.tree.2009.07.003 (2009).Article 

    Google Scholar 
    Deinet, S. et al. Wildlife comeback in Europe: The recovery of selected mammal and bird species. (2013).Gompper, M. Top Carnivores in the Suburbs? Ecological and Conservation Issues Raised by Colonization of North-eastern North America by Coyotes: The expansion of the coyote’s geographical range may broadly influence community structure, and rising coyote densities in the suburbs may alter how the general public views wildlife. BioScience 52, 185–190. https://doi.org/10.1641/0006-3568 (2002).Dalecky, A. et al. Range expansion of the invasive house mouse Mus musculus domesticus in Senegal, West Africa: A synthesis of trapping data over three decades, 1983–2014. Mammal. Rev. 45, 176–190. https://doi.org/10.1111/mam.12043 (2015).Article 

    Google Scholar 
    Konečnỳ, A. et al. Invasion genetics of the introduced black rat (Rattus rattus) in Senegal West Africa. Mol. Ecol. 22, 286–300. https://doi.org/10.1111/mec.12112 (2013).Article 

    Google Scholar 
    Bramley, G. N. Home ranges and interactions of kiore (Rattus exulans) and Norway rats (R. norvegicus) on Kapiti Island, New Zealand. New Zeal. J. Ecol. 328–334 (2014).O’Rourke, R. L., Anson, J. R., Saul, A. M. & Banks, P. B. Limits to alien black rats (Rattus rattus) acting as equivalent pollinators to extinct native small mammals: The influence of stem width on mammal activity at native Banksia ericifolia inflorescences. Biol. Invasions 22, 329–338. https://doi.org/10.1007/s10530-019-02090-x (2020).Article 

    Google Scholar 
    White, T. A. et al. Range expansion in an invasive small mammal: influence of life-history and habitat quality. Biol. Invasions 14, 2203–2215. https://doi.org/10.1007/s10530-012-0225-x (2012).Article 

    Google Scholar 
    McDevitt, A. D. et al. Invading and expanding: Range dynamics and ecological consequences of the greater white-toothed shrew (Crocidura russula) invasion in Ireland. PLoS ONE 9, e100403. https://doi.org/10.1371/journal.pone.0100403 (2014).Article 
    ADS 
    CAS 

    Google Scholar 
    Aguilar, J.-P., Pélissié, T., Sigé, B. & Michaux, J. Occurrence of the stripe field mouse lineage (Apodemus agrarius Pallas 1771; Rodentia; Mammalia) in the Late Pleistocene of southwestern France. C.R. Palevol 7, 217–225 (2008).Article 

    Google Scholar 
    Kordos, L. Historico-zoogeographical and ecological investigation of the subfossil vertebrate fauna of the Aggtelek Karst. Vert. Hung. 18, 85–100 (1978).
    Google Scholar 
    Hairston, N. G., Smith, F. E. & Slobodkin, L. B. Community structure, population control, and competition. Am. Nat. 94, 421–425 (1960).Article 

    Google Scholar 
    Agnew, P., Hide, M., Sidobre, C. & Michalakis, Y. A minimalist approach to the effects of density-dependent competition on insect life-history traits. Ecol. Entomol. 27, 396–402 (2002).Article 

    Google Scholar 
    Krebs, C. J. Beyond population regulation and limitation. Wildl. Res. 29, 1–10 (2002).Article 

    Google Scholar 
    Huitu, O., Norrdahl, K. & Korpimäki, E. Competition, predation and interspecific synchrony in cyclic small mammal communities. Ecography 27, 197–206.  https://doi.org/10.1111/j.0906-7590.2003.03684.x (2004).Article 

    Google Scholar 
    Lofgren, O. Niche expansion and increased maturation rate of Clethrionomys glareolus in the absence of competitors. J. Mammal. 76, 1100–1112 (1995).Article 

    Google Scholar 
    Hansson, L. Competition between Rodents in Successional Stages of Taiga Forests: Microtus agrestis vs. Clethrionomys glareolus. Oikos 40, 258 (1983).Article 

    Google Scholar 
    Eccard, J. A. & Ylönen, H. Direct interference or indirect exploitation? An experimental study of fitness costs of interspecific competition in voles. Oikos 99, 580–590. https://doi.org/10.1034/j.1600-0706.2002.11833.x (2002).Article 

    Google Scholar 
    Gliwicz, J. Competition among forest rodents: Effects of Apodemus flavicollis and Clethrionomys glareolus on A. agrarius. Acta Zool. Fennica 1984. (1984).Neet, C. R. & Hausser, J. Habitat selection in zones of parapatric contact between the common shrew Sorex araneus and Millet’s shrew S. coronatus. J. Anim. Ecol. 235–250 (1990).Zub, K., Jędrzejewska, B., Jędrzejewski, W. & Bartoń, K. A. Cyclic voles and shrews and non-cyclic mice in a marginal grassland within European temperate forest. Acta Theriol. 57, 205–216 (2012).Article 
    CAS 

    Google Scholar 
    Henttonen, H. et al. Long-term population dynamics of the common shrew Sorex araneus in Finland. in Annales Zoologici Fennici 349–355 (JSTOR, 1989).Gębczyńska, Z., Gębczyński, M., Morzuch, K. & Zielińska, D. M. Food eaten by four species of rodents in polluted forests. Acta Theriol. 34, 465–477 (1989).Article 

    Google Scholar 
    Babinska-Werka, J. Response of rodents to an increased and quantitatively diverse food base. Acta theriologica 35, (1990).Margaletic, J., Glavaš, M. & Bäumler, W. The development of mice and voles in an oak forest with a surplus of acorns. J. Pest Sci. 75, 95–98 (2002).Holisova, V. The food of Apodemus agrarius (Pall.). Zoologické listy 16, 1–14 (1967).Obrtel, R. & Hološová, V. The trophic niche of Apodemus agrarius in northern Moravia. Folia Zool. 30, 125–138 (1981).
    Google Scholar 
    Grant, P. R. Interspecific competition among rodents. Annu. Rev. Ecol. Syst. 3, 79–106 (1972).Article 

    Google Scholar 
    Redfield, J. A., Krebs, C. J. & Taitt, M. J. Competition between Peromyscus maniculatus and Microtus townsendii in grasslands of coastal British Columbia. J. Anim. Ecol. 607–616 (1977).Kincaid, W. B. & Cameron, G. N. Effects of species removal on resource utilization in a Texas rodent community. J. Mammal. 63, 229–235 (1982).Article 

    Google Scholar 
    Yurkonis, K. A., Meiners, S. J. & Wachholder, B. E. Invasion impacts diversity through altered community dynamics. J. Ecol. 93, 1053–1061 (2005).Article 

    Google Scholar 
    Powell, K. I., Chase, J. M. & Knight, T. M. A synthesis of plant invasion effects on biodiversity across spatial scales. Am. J. Bot. 98, 539–548. https://doi.org/10.3732/ajb.1000402 (2011).Article 

    Google Scholar 
    Jaksic, F. M. Vertebrate invaders and their ecological impacts in Chile. Biodivers. Conserv. 7, 1427–1445 (1998).Article 

    Google Scholar 
    Richter-Boix, A. et al. Effects of the non-native amphibian species Discoglossus pictus on the recipient amphibian community: Niche overlap, competition and community organization. Biol. Invasions 15, 799–815. https://doi.org/10.1007/s10530-012-0328-4 (2013).Article 

    Google Scholar 
    Kumschick, S., Bacher, S. & Blackburn, T. M. What determines the impact of alien birds and mammals in Europe? Biol. Invasions 15, 785–797. https://doi.org/10.1007/s10530-012-0326-6 (2013).Article 

    Google Scholar 
    Tedeschi, L., Biancolini, D., Capinha, C., Rondinini, C. & Essl, F. Introduction, spread, and impacts of invasive alien mammal species in Europe. Mam. Rev. Mam. 12277. https://doi.org/10.1111/mam.12277 (2021).Harris, D. B. Review of negative effects of introduced rodents on small mammals on islands. Biol. Invasions 11, 1611–1630. https://doi.org/10.1007/s10530-008-9393-0 (2009).Article 

    Google Scholar 
    Traveset, A. et al. A review on the effects of alien rodents in the Balearic (Western Mediterranean Sea) and Canary Islands (Eastern Atlantic Ocean). Biol. Invasions 11, 1653–1670. https://doi.org/10.1007/s10530-008-9395-y (2009).Article 

    Google Scholar 
    Jung, T. S., Nagorsen, D. W., Kukka, P. M. & Barker, O. E. Alien invaders: recent establishment of an urban population of house mice (Mus musculus) in the Yukon. Northwest. Nat. 93, 240–242 (2012).Article 

    Google Scholar 
    McKinney, M. L. & Lockwood, J. L. Biotic homogenization: A few winners replacing many losers in the next mass extinction. Trends Ecol. Evol. 14, 450–453. https://doi.org/10.1016/S0169-5347(99)01679-1 (1999).Article 
    CAS 

    Google Scholar 
    MacGregor-Fors, I., Morales-Pérez, L., Quesada, J. & Schondube, J. E. Relationship between the presence of House Sparrows (Passer domesticus) and Neotropical bird community structure and diversity. Biol. Invasions 12, 87–96. https://doi.org/10.1007/s10530-009-9432-5 (2010).Article 

    Google Scholar 
    Phillips, B. L. Behaviour on Invasion Fronts, and the Behaviour of Invasion Fronts. in Biological Invasions and Animal Behaviour (eds. Weis, J. S. & Sol, D.) 82–95 (Cambridge University Press, 2016). https://doi.org/10.1017/CBO9781139939492.007.Pietrek, A. G., Goheen, J. R., Riginos, C., Maiyo, N. J. & Palmer, T. M. Density dependence and the spread of invasive big-headed ants (Pheidole megacephala) in an East African savanna. Oecologia 195, 667–676. https://doi.org/10.1007/s00442-021-04859-1 (2021).Article 
    ADS 

    Google Scholar 
    Stanko, M. Ryšavka tmavopása (Apodemus agrarius, Rodentia) na Slovensku. (Parazitologický ústav SAV, 2014).Thompson, K., Hodgson, J. G. & Rich, T. C. Native and alien invasive plants: more of the same?. Ecography 18, 390–402 (1995).Article 

    Google Scholar  More

  • in

    A comparative study of fifteen cover crop species for orchard soil management: water uptake, root density traits and soil aggregate stability

    Evapotranspiration measurements and above-ground biomassFigure 1 shows daily evapotranspiration (ET, mm day−1) of each CC tested before mowing (DOY, day of the year, 184) and at 2, 8, 17 and 25 days after mowing (DOY 190, 196, 205 and 213); bare soil was also included as a reference. Before mowing, ET rates showed significant differences between and within the three groups. CR plants had a mean ET of 8.1 mm day−1, which was lower, compared to the other two groups (10.6 and 18.6 mm day−1 for GR and LE, respectively) and the bare soil control (8.5 mm day−1). On DOY 184, values as high as 9.4 (Glechoma hederacea L., GH) and 9.8 mm day−1 (Trifolium subterraneum L. cv. Denmark, TS) were found (Fig. 1), while ranging around 7 mm day-1, Dichondra repens J.R.Forst. & G.Forst. (DR), Hieracium pilosella L. (HP), and Sagina subulata (Swartz) C. Presl (SS) ET were lower than soil evaporation itself.Figure 1Vertical bars represent the daily water use as referred to unit of soil (ET, mm day−1) for the bare soil (yellow) and all the cover crop species as divided into creeping plants (shades of blue), legumes (shades of green) and grasses (shades of orange). Evapotranspiration was measured though a gravimetric method before (i.e. − 4) and at 2, 8, 17 and 25 days after mowing. ET data are mean values ± SE (n = 4).Full size imageOn the same day, a large ET variation was recorded within the GR group as Festuca arundinacea Schreb. cv. Thor (FA) scored the highest daily ET values (13.4 mm day−1), whereas in Festuca ovina L. cv. Ridu (FO), water loss was reduced by 45% (7.5 mm day−1). Within the 15 CCs, LE registered the highest pre-mowing ET with Trifolium michelianum Savi cv. Bolta (TM) peaking at 22.6 mm day−1. However, within LE, Medicago polymorpha L. cv. Scimitar (MP) showed ET values as low as 12.1 mm day−1 (Fig. 1).Two days after mowing, all tested CCs recorded ET values lower than 9 mm day−1 (Fig. 1). Moreover, water use reduction among LE ranged between 56% (M. polymorpha, MP) and 73% (T. michelianum, TM), such that T. michelianum (TM, 6.1 mm day−1), Medicago truncatula Gaertn. cv. Paraggio (MT, 5.6 mm day−1) and M. polymorpha (MP, 5.2 mm day−1) registered ET values lower than the bare soil (7.0 mm day−1). Even though registering a consistent ET reduction after mowing, GR retained ET rates slightly higher than bare soil, except for F. ovina (FO), which recorded the lowest at 6.3 mm day−1. Subsequent samplings showed that most of the CCs had a progressive recovery in water use (Fig. 1) and data taken 17 days after mowing confirmed that Lotus corniculatus L. cv. Leo (LC) and all GR fetched pre-mowing ET rates. Medicago lupulina L. cv. Virgo (ML) registered a partial recovery with similar rates (about 13 mm day−1) at 17 and 25 days after the mowing event. F. ovina and all remaining LE stayed below 10 mm day−1 with ET values close to the control until the end of the trial. At 17 days from grass cutting, under a quite high exceeding-the-pot biomass, both G. hederacea (GH) and T. subterraneum (TS) reached ET values as high as 12.0 and 11.4 mm day−1, respectively. On the other hand, D. repens (DR), H. pilosella (HP), and S. subulata (SS) even though with slightly higher ET values than those registered at the beginning of the trial (DOY 184), remained close to the soil evaporation rates until DOY 213.Aboveground dry clipped biomass at the first mowing date (ADW_MW1, DOY 188) showed large differences among groups, as represented in Table 1. ADW_MW1 within LE was quite variable, as values ranged between 274.3 g m−2 (M. polymorpha, MP) and 750.0 g m−2 (T. michelianum, TM). With a mean value of 565.9 g m−2, LE aboveground biomass was 80% higher than the mean GR ADW_MW1 (110.2 g m-2). F. ovina (FO) scored the lowest value at 48.4 g m−2 among grasses, while within the creeping group, G. hederacea (GH) and T. subterraneum (TS) had biomass development outside the pot edges totalling 89.6 g m−2 and 23.2 g m−2, respectively.Table 1 Aboveground dry biomass clipped at the first mowing event (ADW _MW1), the corresponding leaf area surface index (LAI) and water use per leaf area unit (ETLEAF) of all cover crops tested.Full size tableLeaf area index (LAI, m2 m−2) at mowing showed the highest values in LE with LAI peaking at 12.4 (Table 1). Among GR, LAI did not show significant differences, being around 1.2. Concerning CR, LAI was assessed at 0.2 and 0.8 for T. subterraneum (TS) and G. hederacea (GH) respectively, while LAI estimated through photo analysis ranged between 1.3 (D. repens, DR) and 3.6 (T. subterraneum TS).Evapotranspiration per leaf area unit (ETLEAF) was notably higher in GR, ranging between 7.75 (F. ovina, FO) and 9.22 (Lolium perenne L. cv. Playfast, LP) mm m−2 day−1 (Table 1). In descending order, ETLEAF was the highest in D. repens (DR, 5.46 mm m−2 day−1). Similar ETLEAF was found when comparing some LE and CR species such as M. truncatula (MT, 3.40 mm m−2 day−1), M. lupulina (ML, 4.05 mm m−2 day−1), G. hederacea (GH, 3.68 mm m−2 day−1), H. pilosella (HP, 3.86 mm m-2 day-1) and T. subterraneum (TS, 2.74 mm m−2 day−1). T. michelianum (TM), with 1.81 mm m-2 day-1 scored the lowest ETLEAF of all species (Table 1).Plotting LAI versus the before-mowing ET yielded a significant quadratic relationship (R2  > 0.76) (Fig. 2a) which helped to distinguish two different data clouds. Till LAI values of about 6, the model was linear, having at its lower end all GR and CR species with the inclusion of M. polymorpha (MP) as a legume, while, at the other end, M. truncatula (MT), L. corniculatus (LC) and M. lupulina (ML) were grouped together. T. michelianum (TM) was isolated from all CCs at 22.56 mm day−1.Figure 2Panel (a): quadratic regression of leaf area index (LAI, m2 m−2) vs cover crop evapotranspiration per unit of soil (ET, mm day−1). Each data point is mean value ± SE (n = 4). The quadratic model equation is y = − 0.128×2 + 2.9968x + 5.4716, R2 = 0.76. Panel (b): the quadratic regression between LAI corresponding to the clipped biomass (m2 m−2) and cover crop ET reduction (%). Each data point is mean value ± SE (n = 4). Quadratic model equation is y = − 0.8985×2 + 16.503x + 5.1491, R2 = 0.94.Full size imageWhen regressing the fraction of ET reduction, compared to pre-mowing values vs LAI (Fig. 2b), the same quadratic model achieved a very close fit (R2 = 0.94, p  1 mm) root diameters as affected by soil cover.Full size tableThe highest values of diameter class length (DCL, mm cm−3) for very fine roots (DCL_VF,  1.0 mm) roots although, most notably, L. corniculatus roots showed the highest abundance for both DCL_M (23.08 cm cm−3) and DCL_C (0.54 cm cm−3).At the 10–20 cm soil depth, GR confirmed the highest values for both very fine and fine roots, with F. arundinacea reaching maximum DCL of 2.269 and 5.215 cm cm-3, respectively (Table 2). L. corniculatus largely outscored any other species for both medium and coarse root diameter (6.173 and 0.037 cm cm−3, respectively), with F. arundinacea ranking second (3.157 and 0.016 cm cm−3, respectively).The highest root dry weight (RDW, mg cm-3) within the topsoil layer was reached by L. corniculatus (8.7 mg cm−3) and F. arundinacea (7.6 mg cm-3). Notably, such values were significantly higher than those recorded on the remaining species, except for the F. arundinacea vs F. rubra commutata comparison (Table 2). At 10–20 depth, scant variation was recorded in RDW measured in grasses, whereas L. corniculatus held its supremacy within legumes (4.5 mg cm−3). Within the creeping type, D. repens (DR) and G. hederacea (GH) scored RDW values as high as those determined for grass species (namely F. arundinacea , P. pratensis and F. rubra commutata), whereas S. subulata (SS) essentially had no root development.Soil aggregates and mean weight diameter (MWD)Table 3 reports the proportional aggregate weight (g kg−1) for both 0–10 and 10–20 cm soil depths. Compared to bare soil, the largest increase in large macroaggregates (LM,  > 2000 µm) in the top 10 cm of soil was achieved by L. corniculatus with 461 g kg−1. L. corniculatus differed from the rest of the LE group, whose grand mean (90 g kg−1) was the lowest of the three tested groups. As a legume, T. subterraneum (TS, 122 g kg−1) recorded the lowest values compared to fellow CR species, ranging between 211 (D. repens, DR) and 316 g kg−1 (G. hederacea, GH). GR recorded LM values slightly lower than those of CR, with a mean value of 217 vs 224 g kg-1.Table 3 Proportional aggregate weight (g kg−1) of sand-free aggregate-size fractions acquired from wet sieving as affected by soil cover and mean weight diameter (MWD). Aggregate-size fraction divided as macroaggregates with large size ( > 2 mm, LM) and small size (2 mm—250 μm, sM), microaggregates (250 μm—53 μm, m), and silt and clay ( More

  • in

    Potential for mercury methylation by Asgard archaea in mangrove sediments

    Hsu-Kim H, Kucharzyk KH, Zhang T, Deshusses MA. Mechanisms regulating mercury bioavailability for methylating microorganisms in the aquatic environment: A critical review. Environ Sci Technol. 2013;47:2441–56.Article 
    CAS 

    Google Scholar 
    Podar M, Gilmour CC, Brandt CC, Soren A, Brown SD, Crable BR, et al. Global prevalence and distribution of genes and microorganisms involved in mercury methylation. Sci Adv. 2015;1:e1500675.Article 

    Google Scholar 
    Liu YR, Johs A, Bi L, Lu X, Hu HW, Sun D, et al. Unraveling microbial communities associated with methylmercury production in paddy soils. Environ Sci Technol. 2018;52:13110–8.Article 
    CAS 

    Google Scholar 
    Lee C-S, Fisher NS. Bioaccumulation of methylmercury in a marine copepod. Environ Toxicol Chem. 2017;36:1287–93.Article 
    CAS 

    Google Scholar 
    Parks JM, Johs A, Podar M, Bridou R, Hurt RAJ, Smith SD, et al. The genetic basis for bacterial mercury methylation. Science 2013;339:1332–5.Article 
    CAS 

    Google Scholar 
    McDaniel EA, Peterson BD, Stevens SLR, Tran PQ, Anantharaman K, McMahon KD. Expanded phylogenetic diversity and metabolic flexibility of mercury-methylating microorganisms. mSystems 2020;5:e00299–20.Article 
    CAS 

    Google Scholar 
    Cooper CJ, Zheng K, Rush KW, Johs A, Sanders BC, Pavlopoulos GA, et al. Structure determination of the HgcAB complex using metagenome sequence data: Insights into microbial mercury methylation. Commun Biol. 2020;3:320.Article 
    CAS 

    Google Scholar 
    Kerin EJ, Gilmour CC, Roden E, Suzuki MT, Coates JD, Mason RP. Mercury methylation by dissimilatory iron-reducing bacteria. Appl Environ Microbiol. 2006;72:7919–21.Article 
    CAS 

    Google Scholar 
    Gilmour CC, Podar M, Bullock AL, Graham AM, Brown SD, Somenahally AC, et al. Mercury methylation by novel microorganisms from new environments. Environ Sci Technol. 2013;47:11810–20.Article 
    CAS 

    Google Scholar 
    Capo E, Bravo AG, Soerensen AL, Bertilsson S, Pinhassi J, Feng C, et al. Deltaproteobacteria and Spirochaetes-like bacteria are abundant putative mercury methylators in oxygen-deficient water and marine particles in the Baltic Sea. Front Microbiol. 2020;11:574080.Article 

    Google Scholar 
    Gionfriddo CM, Tate MT, Wick RR, Schultz MB, Zemla A, Thelen MP, et al. Microbial mercury methylation in Antarctic sea ice. Nat Microbiol. 2016;1:16127.Article 
    CAS 

    Google Scholar 
    Jones DS, Walker GM, Johnson NW, Mitchell CPJ, Coleman Wasik JK, Bailey JV. Molecular evidence for novel mercury methylating microorganisms in sulfate-impacted lakes. ISME J. 2019;13:1659–75.Article 
    CAS 

    Google Scholar 
    Christensen GA, Gionfriddo CM, King AJ, Moberly JG, Miller CL, Somenahally AC, et al. Determining the reliability of measuring mercury cycling gene abundance with correlations with mercury and methylmercury concentrations. Environ Sci Technol. 2019;53:8649–63.Article 
    CAS 

    Google Scholar 
    Villar E, Cabrol L, Heimburger-Boavida LE. Widespread microbial mercury methylation genes in the global ocean. Environ Microbiol Rep. 2020;12:277–87.Article 
    CAS 

    Google Scholar 
    Lin H, Ascher DB, Myung Y, Lamborg CH, Hallam SJ, Gionfriddo CM, et al. Mercury methylation by metabolically versatile and cosmopolitan marine bacteria. ISME J. 2021;15:1810–25.Article 
    CAS 

    Google Scholar 
    King JK, Kostka JE, Frischer ME, Saunders FM, Jahnke RA. A quantitative relationship that demonstrates mercury methylation rates in marine sediments are based on the community composition and activity of sulfate-reducing bacteria. Environ Sci Technol. 2001;35:2491–6.Article 
    CAS 

    Google Scholar 
    Regnell O, Watras CJ. Microbial mercury methylation in aquatic environments: A critical review of published field and laboratory studies. Environ Sci Technol. 2019;53:4–19.Article 
    CAS 

    Google Scholar 
    Xie R, Wang Y, Huang D, Hou J, Li L, Hu H, et al. Expanding Asgard members in the domain of Archaea sheds new light on the origin of eukaryotes. Sci China Life Sci. 2022;65:818–29.Article 
    CAS 

    Google Scholar 
    Seitz KW, Dombrowski N, Eme L, Spang A, Lombard J, Sieber JR, et al. Asgard archaea capable of anaerobic hydrocarbon cycling. Nat Commun. 2019;10:1822.Article 

    Google Scholar 
    Zaremba-Niedzwiedzka K, Caceres EF, Saw JH, Backstrom D, Juzokaite L, Vancaester E, et al. Asgard archaea illuminate the origin of eukaryotic cellular complexity. Nature 2017;541:353–8.Article 
    CAS 

    Google Scholar 
    Liu Y, Makarova KS, Huang W-C, Wolf YI, Nikolskaya AN, Zhang X, et al. Expanded diversity of Asgard archaea and their relationships with eukaryotes. Nature 2021;593:553–7.Article 
    CAS 

    Google Scholar 
    Zhang JW, Dong HP, Hou LJ, Liu Y, Ou YF, Zheng YL, et al. Newly discovered Asgard archaea Hermodarchaeota potentially degrade alkanes and aromatics via alkyl/benzyl-succinate synthase and benzoyl-CoA pathway. ISME J. 2021;15:1826–43.Article 
    CAS 

    Google Scholar 
    Cai M, Liu Y, Yin X, Zhou Z, Friedrich MW, Richter-Heitmann T, et al. Diverse Asgard archaea including the novel phylum Gerdarchaeota participate in organic matter degradation. Sci China Life Sci. 2020;63:886–97.Article 
    CAS 

    Google Scholar 
    Baker BJ, De Anda V, Seitz KW, Dombrowski N, Santoro AE, Lloyd KG. Diversity, ecology and evolution of Archaea. Nat Microbiol. 2020;5:887–900.Article 
    CAS 

    Google Scholar 
    Farag Ibrahim F, Zhao R, Biddle Jennifer F, Atomi H. “Sifarchaeota,” a novel Asgard phylum from Costa Rican sediment capable of polysaccharide degradation and anaerobic methylotrophy. Appl Environ Micro. 2021;87:e02584–20.
    Google Scholar 
    Adam PS, Borrel G, Brochier-Armanet C, Gribaldo S. The growing tree of Archaea: new perspectives on their diversity, evolution and ecology. ISME J. 2017;11:2407–25.Article 

    Google Scholar 
    Cai M, Richter-Heitmann T, Yin X, Huang W-C, Yang Y, Zhang C, et al. Ecological features and global distribution of Asgard archaea. Sci Total Environ. 2021;758:143581.Article 
    CAS 

    Google Scholar 
    Zhang C-J, Chen Y-L, Sun Y-H, Pan J, Cai M-W, Li M. Diversity, metabolism and cultivation of archaea in mangrove ecosystems. Mar Life Sci Tech. 2020;3:252–62.Article 

    Google Scholar 
    Dai SS, Yang Z, Tong Y, Chen L, Liu SY, Pan R, et al. Global distribution and environmental drivers of methylmercury production in sediments. J Hazard Mater. 2021;407:124700.Article 
    CAS 

    Google Scholar 
    Tang WL, Liu YR, Guan WY, Zhong H, Qu XM, Zhang T. Understanding mercury methylation in the changing environment: Recent advances in assessing microbial methylators and mercury bioavailability. Sci Total Environ. 2020;714:136827.Article 
    CAS 

    Google Scholar 
    Tsui MTK, Finlay JC, Balogh SJ, Nollet YH. In situ production of methylmercury within a stream channel in northern California. Environ Sci Technol. 2010;44:6998–7004.Article 
    CAS 

    Google Scholar 
    Liu Y, Zhou Z, Pan J, Baker BJ, Gu JD, Li M. Comparative genomic inference suggests mixotrophic lifestyle for Thorarchaeota. ISME J. 2018;12:1021–31.Article 
    CAS 

    Google Scholar 
    Lei P, Zhong H, Duan D, Pan K. A review on mercury biogeochemistry in mangrove sediments: Hotspots of methylmercury production? Sci Total Environ. 2019;680:140–50.Article 
    CAS 

    Google Scholar 
    Beckers F, Rinklebe J. Cycling of mercury in the environment: Sources, fate, and human health implications: A review. Crit Rev Env Sci Tec. 2017;47:693–794.Article 
    CAS 

    Google Scholar 
    de Oliveira DC, Correia RR, Marinho CC, Guimaraes JR. Mercury methylation in sediments of a Brazilian mangrove under different vegetation covers and salinities. Chemosphere 2015;127:214–21.Article 

    Google Scholar 
    Li R, Xu H, Chai M, Qiu GY. Distribution and accumulation of mercury and copper in mangrove sediments in Shenzhen, the world’s most rapid urbanized city. Environ Moni Assess. 2016;188:87.Article 

    Google Scholar 
    O’Connor D, Hou D, Ok YS, Mulder J, Duan L, Wu Q, et al. Mercury speciation, transformation, and transportation in soils, atmospheric flux, and implications for risk management: A critical review. Environ Int. 2019;126:747–61.Article 

    Google Scholar 
    Obrist D, Kirk JL, Zhang L, Sunderland EM, Jiskra M, Selin NE. A review of global environmental mercury processes in response to human and natural perturbations: Changes of emissions, climate, and land use. Ambio 2018;47:116–40.Article 

    Google Scholar 
    Capo E, Peterson BD, Kim M, Jones DS, Acinas SG, Amyot M, et al. A consensus protocol for the recovery of mercury methylation genes from metagenomes. Mol Ecol Resour. 2022; https://doi.org/10.1111/1755-0998.13687.Gionfriddo CM, Wymore AM, Jones DS, Wilpiszeski RL, Lynes MM, Christensen GA, et al. An improved hgcAB primer set and direct high-throughput sequencing expand Hg-methylator diversity in nature. Front Microbiol. 2020;11:541554.Article 

    Google Scholar 
    Yu R-Q, Barkay T. Chapter two – microbial mercury transformations: Molecules, functions and organisms. Adv Appl Microbiol. 2022;118:31–90.Article 

    Google Scholar 
    Chételat J, Richardson MC, MacMillan GA, Amyot M, Poulain AJ. Ratio of methylmercury to dissolved organic carbon in water explains methylmercury bioaccumulation across a latitudinal gradient from north-temperate to arctic lakes. Environ Sci Technol. 2018;52:79–88.Article 

    Google Scholar 
    Liu Y-R, Dong J-X, Han L-L, Zheng Y-M, He J-Z. Influence of rice straw amendment on mercury methylation and nitrification in paddy soils. Environ Pollut. 2016;209:53–9.Article 
    CAS 

    Google Scholar 
    Moreau JW, Gionfriddo CM, Krabbenhoft DP, Ogorek JM, DeWild JF, Aiken GR, et al. The effect of natural organic matter on mercury methylation by Desulfobulbus propionicus 1pr3. Front Microbiol. 2015;6:1389.Article 

    Google Scholar 
    Chen C-F, Ju Y-R, Chen C-W, Dong C-D. The distribution of methylmercury in estuary and harbor sediments. Sci Total Environ. 2019;691:55–63.Article 
    CAS 

    Google Scholar 
    Bravo AG, Bouchet S, Guédron S, Amouroux D, Dominik J, Zopfi J. High methylmercury production under ferruginous conditions in sediments impacted by sewage treatment plant discharges. Water Res. 2015;80:245–55.Article 
    CAS 

    Google Scholar 
    Wang H, Su J, Zheng T, Yang X. Insights into the role of plant on ammonia-oxidizing bacteria and archaea in the mangrove ecosystem. J Soil Sediment. 2015;15:1212–23.Article 
    CAS 

    Google Scholar 
    Imachi H, Nobu MK, Nakahara N, Morono Y, Ogawara M, Takaki Y, et al. Isolation of an archaeon at the prokaryote–eukaryote interface. Nature 2020;577:519–25.Article 
    CAS 

    Google Scholar 
    Zhou J, Riccardi D, Beste A, Smith JC, Parks JM. Mercury methylation by HgcA: Theory supports carbanion transfer to Hg(II). Inorg Chem. 2014;53:772–7.Article 
    CAS 

    Google Scholar 
    Smith Steven D, Bridou R, Johs A, Parks Jerry M, Elias Dwayne A, Hurt Richard A, et al. Site-directed mutagenesis of HgcA and HgcB reveals amino acid residues important for mercury methylation. Appl Environ Micro. 2015;81:3205–17.Article 
    CAS 

    Google Scholar 
    Sousa FL, Neukirchen S, Allen JF, Lane N, Martin WF. Lokiarchaeon is hydrogen dependent. Nat Microbiol. 2016;1:16034.Article 
    CAS 

    Google Scholar 
    Schaefer JK, Rocks SS, Zheng W, Liang L, Gu B, Morel FMM. Active transport, substrate specificity, and methylation of Hg(II) in anaerobic bacteria. Proc Natl Acad Sci USA 2011;108:8714.Article 
    CAS 

    Google Scholar 
    Sakai S, Imachi H, Hanada S, Ohashi A, Harada H, Kamagata Y. Methanocella paludicola gen. nov., sp. nov., a methane-producing archaeon, the first isolate of the lineage ‘Rice Cluster I’, and proposal of the new archaeal order Methanocellales ord. nov. Int J Syst Evol Microbiol. 2008;58:929–36.Article 

    Google Scholar 
    Dridi B, Fardeau ML, Ollivier B, Raoult D, Drancourt M. Methanomassiliicoccus luminyensis gen. nov., sp. nov., a methanogenic archaeon isolated from human faeces. Int J Syst Evol Microbiol. 2012;62:1902–7.Article 
    CAS 

    Google Scholar 
    Dietz R, Sonne C, Basu N, Braune B, O’Hara T, Letcher RJ, et al. What are the toxicological effects of mercury in arctic biota? Sci Total Environ. 2013;443:775–90.Article 
    CAS 

    Google Scholar 
    Gilmour Cynthia C, Bullock Allyson L, McBurney A, Podar M, Elias Dwayne A, Lovley Derek R. Robust mercury methylation across diverse methanogenic archaea. mBio 2018;9:e02403–17.
    Google Scholar 
    Pan J, Chen Y, Wang Y, Zhou Z, Li M. Vertical distribution of Bathyarchaeotal communities in mangrove wetlands suggests distinct niche preference of Bathyarchaeota subgroup 6. Micro Ecol. 2019;77:417–28.Article 

    Google Scholar 
    Zhang C-J, Pan J, Duan C-H, Wang Y-M, Liu Y, Sun J, et al. Prokaryotic diversity in mangrove sediments across southeastern China fundamentally differs from that in other biomes. mSystems 2019;4:e00442–19.Article 
    CAS 

    Google Scholar 
    Peng Y, Leung HC, Yiu SM, Chin FY. IDBA-UD: a de novo assembler for single-cell and metagenomic sequencing data with highly uneven depth. Bioinformatics 2012;28:1420–8.Article 
    CAS 

    Google Scholar 
    Li D, Liu C-M, Luo R, Sadakane K, Lam T-W. MEGAHIT: an ultra-fast single-node solution for large and complex metagenomics assembly via succinct de Bruijn graph. Bioinformatics 2015;31:1674–6.Article 
    CAS 

    Google Scholar 
    Hyatt D, Chen G-L, LoCascio PF, Land ML, Larimer FW, Hauser LJ. Prodigal: prokaryotic gene recognition and translation initiation site identification. BMC Bioinforma. 2010;11:119.Article 

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

    Google Scholar 
    Kang DD, Froula J, Egan R, Wang Z. MetaBAT, an efficient tool for accurately reconstructing single genomes from complex microbial communities. PeerJ 2015;3:e1165.Article 

    Google Scholar 
    Sieber CMK, Probst AJ, Sharrar A, Thomas BC, Hess M, Tringe SG, et al. Recovery of genomes from metagenomes via a dereplication, aggregation and scoring strategy. Nat Microbiol. 2018;3:836–43.Article 
    CAS 

    Google Scholar 
    Parks DH, Imelfort M, Skennerton CT, Hugenholtz P, Tyson GW. CheckM: assessing the quality of microbial genomes recovered from isolates, single cells, and metagenomes. Genome Res. 2015;25:1043–55.Article 
    CAS 

    Google Scholar 
    Olm MR, Brown CT, Brooks B, Banfield JF. dRep: A tool for fast and accurate genomic comparisons that enables improved genome recovery from metagenomes through de-replication. ISME J. 2017;11:2864–8.Article 
    CAS 

    Google Scholar 
    Chaumeil PA, Mussig AJ, Hugenholtz P, Parks DH. GTDB-Tk: a toolkit to classify genomes with the Genome Taxonomy Database. Bioinformatics 2019;36:1925–7.
    Google Scholar 
    Kanehisa M, Sato Y, Morishima K. BlastKOALA and GhostKOALA: KEGG tools for functional characterization of genome and metagenome sequences. J Mol Biol. 2016;428:726–31.Article 
    CAS 

    Google Scholar 
    Huerta-Cepas J, Forslund K, Szklarczyk D, Jensen LJ, von Mering C, Bork P. Fast genome-wide functional annotation through orthology assignment by eggNOG-mapper. Mol Biol Evol. 2017;34:2115–22.Article 
    CAS 

    Google Scholar 
    Finn RD, Clements J, Eddy SR. HMMER web server: interactive sequence similarity searching. Nucleic Acids Res. 2011;39:W29–W37.Article 
    CAS 

    Google Scholar 
    Eddy SR. Accelerated profile HMM searches. PLoS Comput Biol. 2011;7:e1002195.Article 
    CAS 

    Google Scholar 
    Edgar RC. MUSCLE: Multiple sequence alignment with high accuracy and high throughput. Nucleic Acids Res. 2004;32:1792–7.Article 
    CAS 

    Google Scholar 
    Capella-Gutiérrez S, Silla-Martínez JM, Gabaldón T. trimAl: a tool for automated alignment trimming in large-scale phylogenetic analyses. Bioinformatics 2009;25:1972–3.Article 

    Google Scholar 
    Nguyen L-T, Schmidt HA, von Haeseler A, Minh BQ. IQ-TREE: a fast and effective stochastic algorithm for estimating maximum-likelihood phylogenies. Mol Biol Evol. 2015;32:268–74.Article 
    CAS 

    Google Scholar 
    Price MN, Dehal PS, Arkin AP. FastTree 2 – approximately maximum-likelihood trees for large alignments. Plos ONE. 2010;5:e9490.Article 

    Google Scholar 
    Letunic I, Bork P. Interactive tree of life (iTOL) v3: an online tool for the display and annotation of phylogenetic and other trees. Nucleic Acids Res. 2016;44:W242–5.Article 
    CAS 

    Google Scholar 
    Jumper J, Evans R, Pritzel A, Green T, Figurnov M, Ronneberger O, et al. Highly accurate protein structure prediction with AlphaFold. Nature 2021;596:583–9.Article 
    CAS 

    Google Scholar 
    Trott O, Olson AJ. AutoDock Vina: Improving the speed and accuracy of docking with a new scoring function, efficient optimization, and multithreading. J Comput Chem. 2010;31:455–61.CAS 

    Google Scholar 
    Langmead B, Salzberg SL. Fast gapped-read alignment with Bowtie 2. Nat Methods. 2012;9:357–9.Article 
    CAS 

    Google Scholar 
    Quinlan AR, Hall IM. BEDTools: a flexible suite of utilities for comparing genomic features. Bioinforma (Oxf, Engl). 2010;26:841–2.Article 
    CAS 

    Google Scholar 
    Li H, Durbin R. Fast and accurate short read alignment with Burrows-Wheeler transform. Bioinformatics 2009;25:1754–60.Article 
    CAS 

    Google Scholar  More

  • in

    Investigating metropolitan change through mathematical morphology and a dynamic factor analysis of structural and functional land-use indicators

    Alphan, H. Land use change and urbanisation of Adana, Turkey. Land Degrad. Dev. 14, 575–586 (2003).Article 

    Google Scholar 
    Catalàn, B., Sauri, D. & Serra, P. Urban sprawl in the Mediterranean? Patterns of growth and change in the Barcelona Metropolitan Region 1993–2000. Landsc. Urban Plan. 85(3–4), 174–184 (2008).
    Google Scholar 
    Chen, K., Long, H., Liao, L., Tu, S. & Li, T. Land use transitions and urban-rural integrated development: Theoretical framework and China’s evidence. Land Use Policy 92, 104465 (2020).Article 

    Google Scholar 
    Bianchini, L. et al. Forest transition and metropolitan transformations in developed countries: Interpreting apparent and latent dynamics with local regression models. Land 11(1), 12 (2021).Article 

    Google Scholar 
    Angel, S., Parent, J., Civco, D. L., Blei, A. & Potere, D. The dimensions of global urban expansion: Estimates and projections for all countries, 2000–2050. Prog. Plan. 75(2), 53–107 (2011).Article 

    Google Scholar 
    Fischer, A. P. Forest landscapes as social-ecological systems and implications for management. Landsc. Urban Plan. 177, 138–147 (2018).Article 

    Google Scholar 
    Darvishi, A., Yousefi, M. & Marull, J. Modelling landscape ecological assessments of land use and cover change scenarios. Application to the Bojnourd Metropolitan Area (NE Iran). Land Use Policy 99, 105098 (2020).Article 

    Google Scholar 
    Cheng, L. L., Tian, C. & Yin, T. T. Identifying driving factors of urban land expansion using Google earth engine and machine-learning approaches in Mentougou District, China. Sci. Rep. 12(1), 1–13 (2022).Article 
    CAS 

    Google Scholar 
    Kasanko, M. et al. Are European Cities becoming dispersed? A comparative analysis of fifteen European urban areas. Landsc. Urban Plan. 77(1–2), 111–130 (2006).Article 

    Google Scholar 
    Terzi, F. & Bolen, F. Urban sprawl measurement of Istanbul. Eur. Plan. Stud. 17(10), 1559–1570 (2009).Article 

    Google Scholar 
    Angel, S., Parent, J. & Civco, D. L. Ten compactness properties of circles: measuring shape in geography. Can. Geogr. 54, 441–461 (2010).Article 

    Google Scholar 
    Salvati, L., Gemmiti, R. & Perini, L. Land degradation in Mediterranean urban areas: An unexplored link with planning?. Area 44(3), 317–325 (2012).Article 

    Google Scholar 
    Attorre, F., Bruno, M., Francesconi, F., Valenti, R. & Bruno, F. Landscape changes of Rome through tree-lined roads. Landsc. Urban Plan. 49, 115–128 (2000).Article 

    Google Scholar 
    Turok, I. & Mykhnenko, V. The trajectories of European cities, 1960–2005. Cities 24(3), 165–182 (2007).Article 

    Google Scholar 
    Ioannidis, C., Psaltis, C. & Potsiou, C. Towards a strategy for control of suburban informal buildings through automatic change detection. Comput. Environ. Urban Syst. 33, 64–74 (2009).Article 

    Google Scholar 
    Grekousis, G., Manetos, P. & Photis, Y. N. Modeling urban evolution using neural networks, fuzzy logic and GIS: The case of the athens metropolitan area. Cities 30, 193–203 (2013).Article 

    Google Scholar 
    Salvati, L. Towards a polycentric region? The socioeconomic trajectory of Rome, an ‘Eternally Mediterranean’ city. Tijdschr. Econ. Soc. Geogr. 105(3), 268–284 (2014).Article 

    Google Scholar 
    Chorianopoulos, I., Pagonis, T., Koukoulas, S. & Drymoniti, S. Planning, competitiveness and sprawl in the Mediterranean city: The case of Athens. Cities 27, 249–259 (2010).Article 

    Google Scholar 
    Munafò, M., Salvati, L. & Zitti, M. Estimating soil sealing rate at national level—Italy as a case study. Ecol. Ind. 26, 137–140 (2013).Article 

    Google Scholar 
    Morelli, V. G., Rontos, K. & Salvati, L. Between suburbanisation and re-urbanisation: Revisiting the urban life cycle in a Mediterranean compact city. Urban Res. Pract. 7(1), 74–88 (2014).Article 

    Google Scholar 
    Basem Ajjur, S. & Al-Ghamdi, S. G. Exploring urban growth–climate change–flood risk nexus in fast growing cities. Sci. Rep. 12, 12265 (2022).Article 
    ADS 

    Google Scholar 
    Li, H. & Wu, J. Use and misuse of landscape indices. Landsc. Ecol. 19, 389–399 (2004).Article 

    Google Scholar 
    Salvati, L. Agro-forest landscape and the ‘fringe’city: A multivariate assessment of land-use changes in a sprawling region and implications for planning. Sci. Total Environ. 490, 715–723 (2014).Article 
    ADS 
    CAS 

    Google Scholar 
    Sang, X. et al. Intensity and stationarity analysis of land use change based on CART algorithm. Sci. Rep. 9(1), 1–12 (2019).Article 
    ADS 
    CAS 

    Google Scholar 
    Ettehadi Osgouei, P., Sertel, E. & Kabadayı, M. E. Integrated usage of historical geospatial data and modern satellite images reveal long-term land use/cover changes in Bursa/Turkey, 1858–2020. Sci. Rep. 12(1), 1–17 (2022).Article 

    Google Scholar 
    He, S., Yu, S., Li, G. & Zhang, J. Exploring the influence of urban form on land-use efficiency from a spatiotemporal heterogeneity perspective: Evidence from 336 Chinese cities. Land Use Policy 95, 104576 (2020).Article 

    Google Scholar 
    Bockarjova, M., Wouter Botzen, W. J., Bulkeley, H. A. & Toxopeus, H. Estimating the social value of nature-based solutions in European cities. Sci. Rep. 12, 19833 (2022).Article 
    ADS 
    CAS 

    Google Scholar 
    Liu, J. & Niyogi, D. Meta-analysis of urbanisation impact on rainfall modification. Sci. Rep. 9(1), 1–14 (2019).ADS 

    Google Scholar 
    Holland, J. H. Studying complex adaptive systems. J. Syst. Sci. Complex. 19(1), 1–8 (2006).Article 
    MathSciNet 
    MATH 

    Google Scholar 
    Salvati, L. & Serra, P. Estimating rapidity of change in complex urban systems: A multidimensional, local-scale approach. Geogr. Anal. 48(2), 132–156 (2016).Article 

    Google Scholar 
    Bura, S., Guerin-Pace, F., Mathian, H., Pumain, D. & Sanders, L. Multi-agents systems and the dynamics of a settlement system. Geogr. Anal. 28(2), 161–178 (1996).Article 

    Google Scholar 
    Hasse, J. E. & Lathrop, R. G. Land resource impact indicators of urban sprawl. Appl. Geogr. 23, 159–175 (2003).Article 

    Google Scholar 
    Grafius, D. R., Corstanje, R. & Harris, J. A. Linking ecosystem services, urban form and green space configuration using multivariate landscape metric analysis. Landsc. Ecol. 33(4), 557–573 (2018).Article 

    Google Scholar 
    Pumain, D. Hierarchy in Natural and Social Sciences (Kluwer-Springer, 2005).
    Google Scholar 
    Cabral, P., Augusto, G., Tewolde, M. & Araya, Y. Entropy in urban systems. Entropy 15(12), 5223–5236 (2013).Article 
    ADS 

    Google Scholar 
    Salvati, L. & Carlucci, M. In-between stability and subtle changes: Urban growth, population structure, and the city life cycle in Rome. Popul. Space Place 22(3), 216–227 (2016).Article 

    Google Scholar 
    Batty, M. & Longley, P. Fractal Cities (Academic Press, 1994).MATH 

    Google Scholar 
    Berry, B. J. L. Cities as systems within systems of cities. Pap. Reg. Sci. 13, 147–163 (2005).Article 

    Google Scholar 
    Petrosillo, I. et al. The resilient recurrent behavior of mediterranean semi-arid complex adaptive landscapes. Land 10(3), 296 (2021).Article 

    Google Scholar 
    Portugali, J. Complexity, Cognition and the City, Understanding Complex Systems (Springer, 2011).Book 

    Google Scholar 
    Wu, J., Jenerette, G. D., Buyantuyev, A. & Redman, C. L. Quantifying spatiotemporal patterns of urbanisation: The case of the two fastest growing metropolitan regions in the United States. Ecol. Complex. 8(1), 1–8 (2011).Article 

    Google Scholar 
    Sun, Y., Gao, C., Li, J., Li, W. & Ma, R. Examining urban thermal environment dynamics and relations to biophysical composition and configuration and socioeconomic factors: A case study of the Shanghai metropolitan region. Sustain. Cities Soc. 40, 284–295 (2018).Article 

    Google Scholar 
    Phillips, M. A. & Ritala, P. A complex adaptive systems agenda for ecosystem research methodology. Technol. Forecast. Soc. Change 148, 119739 (2019).Article 

    Google Scholar 
    Walker, B., Holling, C. S., Carpenter, S. R. & Kinzig, A. Resilience, adaptability and transformability in social-ecological systems. Ecol. Soc. 9(2), 5 (2004).Article 

    Google Scholar 
    Kelly, C. et al. Community resilience and land degradation in forest and shrublandsocio-ecological systems: A case study in Gorgoglione, Basilicata regionn, Italy. Land Use Policy 46, 11–20 (2015).Article 

    Google Scholar 
    Preiser, R., Biggs, R., De Vos, A. & Folke, C. Social-ecological systems as complex adaptive systems. Ecol. Soc. 23(4), 46 (2018).Article 

    Google Scholar 
    Ferrara, A. et al. Shaping the role of ‘fast’ and ‘slow’ drivers of change in forest-shrubland socio-ecological systems. J. Environ. Manag. 169, 155–166 (2016).Article 

    Google Scholar 
    Lamy, T., Liss, K. N., Gonzalez, A. & Bennett, E. M. Landscape structure affects the provision of multiple ecosystem services. Environ. Res. Lett. 11(12), 124017 (2016).Article 
    ADS 

    Google Scholar 
    Riitters, K. H., Vogt, P., Soille, P., Kozak, J. & Estreguil, C. Neutral model analysis of landscape patterns from mathematical morphology. Landsc. Ecol. 22(7), 1033–1043 (2007).Article 

    Google Scholar 
    Riitters, K., Vogt, P., Soille, P. & Estreguil, C. Landscape patterns from mathematical morphology on maps with contagion. Landsc. Ecol. 24(5), 699–709 (2009).Article 

    Google Scholar 
    Anas, A., Arnott, R. & Small, K. Urban spatial structure. J. Econ. Lit. 36(3), 1426–1464 (1998).
    Google Scholar 
    Arroyo-Mora, J. P., Sánchez-Azofeifa, G. A., Rivard, B., Calvo, J. C. & Janzen, D. H. Dynamics in landscape structure and composition for the Chorotega region, Costa Rica from 1960 to 2000. Agr. Ecosyst. Environ. 106(1), 27–39 (2005).Article 

    Google Scholar 
    Siles, G., Charland, A., Voirin, Y. & Bénié, G. B. Integration of landscape and structure indicators into a web-based geoinformation system for assessing wetlands status. Eco. Inform. 52, 166–176 (2019).Article 

    Google Scholar 
    Soille, P. Morphological Image Analysis: Principles and Applications (Springer, 2003).MATH 

    Google Scholar 
    Soille, P. & Vogt, P. Morphological segmentation of binary patterns. Pattern Recogn. Lett. 30, 456–459 (2009).Article 
    ADS 

    Google Scholar 
    Vogt, P. et al. Mapping spatial patterns with morphological image processing. Landsc. Ecol. 22(2), 171–177 (2007).Article 

    Google Scholar 
    Bajocco, S., Ceccarelli, T., Smiraglia, D., Salvati, L. & Ricotta, C. Modeling the ecological niche of long-term land use changes: The role of biophysical factors. Ecol. Ind. 60, 231–236 (2016).Article 

    Google Scholar 
    Yin, Y., Zhou, K. & Chen, Y. Deconstructing the driving factors of land development intensity from multi-scale in differentiated functional zones. Sci. Rep. 12(1), 1–13 (2022).Article 

    Google Scholar 
    Duvernoy, I., Zambon, I., Sateriano, A. & Salvati, L. Pictures from the other side of the fringe: Urban growth and peri-urban agriculture in a post-industrial city (Toulouse, France). J. Rural. Stud. 57, 25–35 (2018).Article 

    Google Scholar 
    Smiraglia, D., Ceccarelli, T., Bajocco, S., Salvati, L. & Perini, L. Linking trajectories of land change, land degradation processes and ecosystem services. Environ. Res. 147, 590–600 (2016).Article 
    CAS 

    Google Scholar 
    Shaker, R. R. Examining sustainable landscape function across the Republic of Moldova. Habitat Int. 72, 77–91 (2018).Article 
    ADS 

    Google Scholar 
    Zheng, H. & Li, H. Spatial–temporal evolution characteristics of land use and habitat quality in Shandong Province, China. Sci. Rep. 12(1), 1–12 (2022).Article 

    Google Scholar 
    Tombolini, I., Munafò, M. & Salvati, L. Soil sealing footprint as an indicator of dispersed urban growth: A multivariate statistics approach. Urban Res. Pract. 9(1), 1–15 (2016).Article 

    Google Scholar 
    Salvati, L., Sateriano, A., Grigoriadis, E. & Carlucci, M. New wine in old bottles: The (changing) socioeconomic attributes of sprawl during building boom and stagnation. Ecol. Econ. 131, 361–372 (2017).Article 

    Google Scholar 
    Zambon, I., Benedetti, A., Ferrara, C. & Salvati, L. Soil matters? A multivariate analysis of socioeconomic constraints to urban expansion in Mediterranean Europe. Ecol. Econ. 146, 173–183 (2018).Article 

    Google Scholar 
    Paul, V. & Tonts, M. Containing urban sprawl: Trends in land use and spatial planning in the Metropolitan Region of Barcelona. J. Environ. Plann. Manag. 48(1), 7–35 (2005).Article 

    Google Scholar 
    Serra, P., Vera, A., Tulla, A. F. & Salvati, L. Beyond urban–rural dichotomy: Exploring socioeconomic and land-use processes of change in Spain (1991–2011). Appl. Geogr. 55, 71–81 (2014).Article 

    Google Scholar 
    Seifollahi-Aghmiuni, S., Kalantari, Z., Egidi, G., Gaburova, L. & Salvati, L. Urbanisation-driven land degradation and socioeconomic challenges in peri-urban areas: Insights from Southern Europe. Ambio 51(6), 1446–1458 (2022).Article 

    Google Scholar 
    Pili, S., Grigoriadis, E., Carlucci, M., Clemente, M. & Salvati, L. Towards sustainable growth? A multi-criteria assessment of (changing) urban forms. Ecol. Ind. 76, 71–80 (2017).Article 

    Google Scholar 
    Salvati, L., Sateriano, A. & Grigoriadis, E. Crisis and the city: Profiling urban growth under economic expansion and stagnation. Lett. Spat. Resour. Sci. 9(3), 329–342 (2016).Article 

    Google Scholar 
    Champion, T. & Hugo, G. New Forms of Urbanisation: Beyond the Urban-Rural Dichotomy (Ashgate, 2004).
    Google Scholar 
    Frondoni, R., Mollo, B. & Capotorti, G. A landscape analysis of land cover change in the municipality of Rome (Italy): Spatio-temporal characteristics and ecological implications of land cover transitions from 1954 to 2001. Landsc. Urban Plan. 100(1–2), 117–128 (2011).Article 

    Google Scholar 
    Perrin, C., Nougarèdes, B., Sini, L., Branduini, P. & Salvati, L. Governance changes in peri-urban farmland protection following decentralisation: A comparison between Montpellier (France) and Rome (Italy). Land Use Policy 70, 535–546 (2018).Article 

    Google Scholar 
    Salvati, L. Monitoring high-quality soil consumption driven by urban pressure in a growing city (Rome, Italy). Cities 31, 349–356 (2013).Article 

    Google Scholar 
    Salvati, L., Ciommi, M. T., Serra, P. & Chelli, F. M. Exploring the spatial structure of housing prices under economic expansion and stagnation: The role of socio-demographic factors in metropolitan Rome, Italy. Land Use Policy 81, 143–152 (2019).Article 

    Google Scholar 
    Ferrara, C., Salvati, L. & Tombolini, I. An integrated evaluation of soil resource depletion from diachronic settlement maps and soil cartography in peri-urban Rome, Italy. Geoderma 232, 394–405 (2014).Article 
    ADS 

    Google Scholar 
    Egidi, G. & Salvati, L. Beyond the suburban-urban divide: Convergence in age structures in metropolitan Rome, Italy. J. Popul. Soc. Stud. 28(2), 130–142 (2020).Article 

    Google Scholar 
    Pili, S., Serra, P. & Salvati, L. Landscape and the city: Agro-forest systems, land fragmentation and the ecological network in Rome, Italy. Urban For. Urban Green. 41, 230–237 (2019).Article 

    Google Scholar 
    European Environment Agency. Urban Sprawl in Europe – The Ignored Challenge. Copenhagen: EEA Report no. 10 (2006).Park, S., Hepcan, Ç. C., Hepcan, Ş & Cook, E. A. Influence of urban form on landscape pattern and connectivity in metropolitan regions: a comparative case study of Phoenix, AZ, USA, and Izmir, Turkey. Environ. Monit. Assess. 186(10), 6301–6318 (2014).Article 

    Google Scholar 
    Luo, F., Liu, Y., Peng, J. & Wu, J. Assessing urban landscape ecological risk through an adaptive cycle framework. Landsc. Urban Plan. 180, 125–134 (2018).Article 

    Google Scholar 
    Ortega, M., Pascual, S., Elena-Rosselló, R. & Rescia, A. J. Land-use and spatial resilience changes in the Spanish olive socio-ecological landscape. Appl. Geogr. 117, 102171 (2020).Article 

    Google Scholar 
    Parcerisas, L. et al. Land use changes, landscape ecology and their socioeconomic driving forces in the Spanish Mediterranean coast (El Maresme County, 1850–2005). Environ. Sci. Policy 23, 120–132 (2012).Article 

    Google Scholar 
    Masini, E. et al. Urban growth, land-use efficiency and local socioeconomic context: A comparative analysis of 417 metropolitan regions in Europe. Environ. Manag. 63(3), 322–337 (2019).Article 
    ADS 

    Google Scholar 
    Luck, M. & Wu, J. A gradient analysis of urban landscape pattern: a case study from the Phoenix metropolitan region, Arizona, USA. Landsc. Ecol. 17(4), 327–339 (2002).Article 

    Google Scholar 
    Pesaresi, M. & Bianchin, A. Recognising settlement structure using mathematical morphology and image texture. Remote Sensing Urban Anal. GISDATA 9, 46–60 (2003).
    Google Scholar 
    Schneider, A. & Woodcock, C. E. Compact, dispersed, fragmented, extensive? A comparison of urban growth in twenty-five global cities using remotely sensed data, pattern metrics and census information. Urban Stud. 45(3), 659–692 (2008).Article 

    Google Scholar 
    Mubareka, S., Koomen, E., Estreguil, C. & Lavalle, C. Development of a composite index of urban compactness for land use modelling applications. Landsc. Urban Plan. 103(3–4), 303–317 (2011).Article 

    Google Scholar 
    Vogt, P. et al. Mapping landscape corridors. Ecol. Ind. 7(2), 481–488 (2007).Article 

    Google Scholar 
    Daya Sagar, B. S. & Murthy, K. S. R. Generation of a fractal landscape using nonlinear mathematical morphological transformations. Fractals 8(03), 267–272 (2000).Article 

    Google Scholar 
    Scott, A. J., Carter, C., Reed, M. R., Stonyer, B. & Coles, R. Disintegrated development at the rural-urban fringe: Re-connecting spatial planning theory and practice. Prog. Plan. 83, 1–52 (2013).Article 

    Google Scholar 
    Zhao, Q., Wen, Z., Chen, S., Ding, S. & Zhang, M. Quantifying land use/land cover and landscape pattern changes and impacts on ecosystem services. Int. J. Environ. Res. Public Health 17(1), 126 (2020).Article 

    Google Scholar 
    Parr, J. The regional economy, spatial structure and regional urban systems. Reg. Stud. 48(12), 1926–1938 (2014).Article 

    Google Scholar 
    Salvati, L., Zambon, I., Chelli, F. M. & Serra, P. Do spatial patterns of urbanisation and land consumption reflect different socioeconomic contexts in Europe?. Sci. Total Environ. 625, 722–730 (2018).Article 
    ADS 
    CAS 

    Google Scholar 
    Coppi, R. & Bolasco, S. Multiway Data Analysis (Elsevier, 1988).MATH 

    Google Scholar 
    Kroonenberg, P. M. Applied Multiway Data Analysis (Wiley, 2008).Book 
    MATH 

    Google Scholar 
    Escofier, B. & Pages, J. Multiple factor analysis (AFMULT Package). Comput. Stat. Data Anal. 18, 121–140 (1994).Article 
    MATH 

    Google Scholar 
    De Rosa, S. & Salvati, L. Beyond a ‘side street story’? Naples from spontaneous centrality to entropic polycentricism, towards a ‘crisis city’. Cities 51, 74–83 (2016).Article 

    Google Scholar 
    Favaro, J.-M. & Pumain, D. Gibrat revisited: An urban growth model incorporating spatial interaction and innovation cycles. Geogr. Anal. 43(3), 261–286 (2011).Article 

    Google Scholar 
    Walker, B. H., Carpenter, S. R., Rockstrom, J., Crepin, A.-S. & Peterson, G. D. “Drivers, “slow” variables, “fast” variables, shocks, and resilience. Ecol. Soc. 17(3), 30 (2012).Article 

    Google Scholar 
    Zhang, Z., Su, S., Xiao, R., Jiang, D. & Wu, J. Identifying determinants of urban growth from a multi-scale perspective: A case study of the urban agglomeration around Hangzhou Bay, China. Appl. Geogr. 45, 193–202 (2013).Article 

    Google Scholar 
    Fratarcangeli, C., Fanelli, G., Franceschini, S., De Sanctis, M. & Travaglini, A. Beyond the urban-rural gradient: Self-organising map detects the nine landscape types of the city of Rome. Urban For. Urban Green. 38, 354–370 (2019).Article 

    Google Scholar 
    Crisci, M., Benassi, F., Rabiei-Dastjerdi, H., McArdle, G. Spatio-temporal variations and contextual factors of the supply of Airbnb in Rome. An initial investigation. Lett. Spat. Resour. Sci. 1–17 (2022).Lelo, K., Monni, S. & Tomassi, F. Socio-spatial inequalities and urban transformation. The case of Rome districts. Socio-Econ. Plann. Sci. 68, 100696 (2019).Article 

    Google Scholar 
    Crisci, M. The impact of the real estate crisis on a south european metropolis: From urban diffusion to Reurbanisation. Appl. Spat. Anal. Policy 15(3), 797–820 (2022).Article 

    Google Scholar 
    Wang, Y. & Zhang, X. A dynamic modeling approach to simulating socioeconomic effects on landscape changes. Ecol. Model. 140(1–2), 141–162 (2001).Article 

    Google Scholar 
    Voghera, A. The River agreement in Italy. Resilient planning for the co-evolution of communities and landscapes. Land Use Policy 91, 104377 (2020).Article 

    Google Scholar 
    Chen, A. & Partridge, M. D. When are cities engines of growth in China? Spread and backwash effects across the urban hierarchy. Reg. Stud. 47(8), 1313–1331 (2013).Article 

    Google Scholar 
    Ciommi, M., Chelli, F. M., Carlucci, M. & Salvati, L. Urban growth and demographic dynamics in southern Europe: Toward a new statistical approach to regional science. Sustainability 10(8), 2765 (2018).Article 

    Google Scholar 
    Jacobs-Crisioni, C., Rietveld, P. & Koomen, E. The impact of spatial aggregation on urban development analyses. Appl. Geogr. 47, 46–56 (2014).Article 

    Google Scholar 
    Kourtit, K., Nijkamp, P. & Reid, N. The new urban world: Challenges and policy. Appl. Geogr. 49, 1–3 (2014).Article 

    Google Scholar 
    Bruegmann, R. Sprawl: A Compact History (University of Chicago Press, 2005).Book 

    Google Scholar 
    Neuman, M. & Hull, A. The Futures of the City Region. Reg. Stud. 43(6), 777–787 (2009).Article 

    Google Scholar 
    Couch, C., Petschel-held, G. & Leontidou, L. Urban Sprawl In Europe: Landscapes, Land-use Change and Policy (Blackwell, 2007).Book 

    Google Scholar 
    Longhi, C. & Musolesi, A. European cities in the process of economic integration: towards structural convergence. Ann. Reg. Sci. 41, 333–351 (2007).Article 

    Google Scholar 
    Tian, G., Ouyang, Y., Quan, Q. & Wu, J. Simulating spatiotemporal dynamics of urbanisation with multi-agent systems—A case study of the Phoenix metropolitan region, USA. Ecol. Model. 222(5), 1129–1138 (2011).Article 

    Google Scholar 
    Tian, L., Chen, J. & Yu, S. X. Coupled dynamics of urban landscape pattern and socioeconomic drivers in Shenzhen, China. Landsc. Ecol. 29(4), 715–727 (2014).Article 

    Google Scholar 
    Fielding, A. J. Counterurbanization in Western Europe. Prog. Plan. 17, 1–52 (1982).Article 

    Google Scholar 
    Oueslati, W., Alvanides, S. & Garrod, G. Determinants of urban sprawl in European cities. Urban Stud. 52(9), 1594–1614 (2015).Article 

    Google Scholar 
    Tress, B., Tress, G., Décamps, H. & d’Hauteserre, A. M. Bridging human and natural sciences in landscape research. Landsc. Urban Plan. 57(3–4), 137–141 (2001).Article 

    Google Scholar 
    Xu, Z., Lv, Z., Li, J., Sun, H. & Sheng, Z. A Novel perspective on travel demand prediction considering natural environmental and socioeconomic factors. IEEE Intell. Transp. Syst. Mag. https://doi.org/10.1109/MITS.2022.3162901 (2022).Article 

    Google Scholar 
    Xu, Z., Lv, Z., Li, J. & Shi, A. A novel approach for predicting water demand with complex patterns based on ensemble learning. Water Resour. Manag. 36(11), 4293–4312 (2022).Article 

    Google Scholar 
    Lv, Z., Li, J., Dong, C., Li, H. & Xu, Z. Deep learning in the COVID-19 epidemic: A deep model for urban traffic revitalisation index. Data Knowl. Eng. 135, 101912 (2021).Article 

    Google Scholar  More

  • in

    Genetic basis of thiaminase I activity in a vertebrate, zebrafish Danio rerio

    Sequence analysisProtein sequence searches were conducted in the GenBank nr database with BLASTP42 using default parameters, including automatically adjusting parameters for short input sequences (Table S1). Conserved domain searches were run against the GenBank Conserved Domain Database (CDD)43. Sequence alignments were conducted in CLC Main Workbench 20.0.4 (Qiagen) with the fast alignment algorithm, gap open cost = 10, and gap extension cost = 1. Biochemical properties of the fish putative thiaminase I protein sequences were predicted with the Create Sequence Statistics function in CLC Main Workbench 20.0.4 (Qiagen, Hilden, Germany). The molecular weights were calculated from the sum of the amino acids in the sequence, and the isoelectric points (pIs) were calculated from the pKa values for the individual amino acids in the sequence.Bacteria culturePure cultures of P. thiaminolyticus strain 818822 were cultured at 37 °C in Terrific Broth (MO BIO Laboratories, Carlsbad, CA) in either a shaking incubator or in a beveled flask with a stir bar and were harvested after 48–80 h of culture. Upon harvest, cultures were processed immediately or frozen whole in 50 mL Falcon tubes at − 80 °C. Fresh or thawed cultures were spun at 14,000×g, and culture supernatant was concentrated using Amicon-ultra 10 kDa molecular weight cut-off (MWCO) filters (EMD Millipore, Billerica, MA).The zebrafish and alewife candidate thiaminase I genes were cloned and overexpressed in E. coli to determine whether they produced functional thiaminases. The recombinant thiaminase I gene from P. thiaminolyticus was overexpressed in E. coli as a positive control. Candidate and control genes were synthesized (Integrated DNA Technologies, Inc., Coralville, Iowa) and placed into the pET52b vector (EMD Millipore). Insert sequences are provided in Supplementary Figs. S10–S13. The empty pET52b vector was used as a negative control. The plasmid was transformed into E. coli (Rosetta 2(DE3)pLysS Singles Competent Cells, EMD Millipore) according to the manufacturer’s instructions, and expression of candidate genes was induced by the addition of IPTG. Cells were lysed in 1X BugBuster (Millipore) according to the manufacturer’s instructions in the presence of benzonase nuclease, and soluble and insoluble fractions were separated by centrifugation.Tissue collectionsAdult common carp were captured from Lake Erie using short-set gill nets. Adult alewife and quagga mussels (Dreissena bugensis) were collected from Sturgeon Bay, Lake Michigan using bottom trawls. Fish collections were completed during July 2007. Sex of sampled fish was not identified. Upon collection, unanesthetized animals were immediately euthanized by flash freezing between slabs of dry ice and stored at − 80 °C. Fish were harvested by the Great Lakes Science Center, U.S. Geological Survey (USGS). Laboratory use of frozen animal tissues and wild type and recombinant bacteria was in accordance with institutional guidelines and biosafety procedures at Oregon State University and USGS. Animal care and use procedures were approved by the Great Lakes Science Center, USGS. All USGS sampling and handling of fish during research are carried out in accordance with guidelines for the care and use of fishes by the American Fisheries Society44. All methods are reported in accordance with applicable ARRIVE guidelines (https://arriveguidelines.org). Zebrafish from OSU’s zebrafish facility were anesthetized and euthanized by overdose with waterborne 200 ppm ethyl 3-aminobenzoate methanesulfonate (MS-222, Sigma-Aldrich, St. Louis, MO) following protocols approved by the OSU Animal Institutional Care and Use Committee and were frozen at − 80 °C after euthanization. Gills, liver, spleen, and the intestinal tract were dissected, and gill tissue was homogenized separately from liver, spleen, and gut, which were homogenized together and designated “viscera.” Homogenization and protein preparation procedures were the same as that for alewife. Zebrafish from Columbia Environmental Research Center (CERC), USGS cultures were anesthetized and euthanized by overdose with 200 ppm ethyl 3-aminobenzoate methanesulfonate (MS-222, Sigma-Aldrich, St. Louis, MO) in water following protocols approved by CERC Institutional Animal Care and Use Committee (IACUC). Whole fish (0.2–0.6 g) were homogenized in 10 mL cold phosphate buffer, pH 6.5. Whole common carp and alewife were thawed until they could just be dissected. Preliminary trial extractions on alewife stomach and intestines, spleen, and gills revealed similar results and revealed that gills and spleen tissue produced the cleanest protein preparations. Therefore, subsequent extractions for common carp and alewife used gill tissue. Samples were pooled from 3 to 5 individual fish, haphazardly chosen from the sampled fish without exclusions. Quagga mussels were thawed just sufficiently to be husked from their shell and were used whole. Researchers were aware of the species and tissue designation of each sample throughout the experiments. Animal tissues were placed in ice-cold (4 °C) beakers containing cold extraction buffer (16 mM K3HPO4, 84 mM KH2PO4, 100 mM NaCl, pH 6.5 with 1 mM DTT, 2 mM EDTA, 3 mM Pepstatin, 1X Protease inhibitor cocktail (Sigma), and 1 mM AEBSF). All extractions were carried out at 4 °C in pre-chilled glassware. Samples were mechanically homogenized using a rotor–stator tissue grinder. Samples were stirred gently for several hours to overnight at 4 °C, centrifuged at 14,000×g to remove debris, and strained through cheesecloth to remove any insoluble lipids. Extracts were then subjected to 30–75% ammonium sulfate precipitation. Pellets from the precipitation were resuspended in buffer (83 mM KH2PO4, 17 mM K2HPO4, and 100 mM NaCl), centrifuged to remove any remaining debris, and stored in 30% glycerol at − 20 °C.Protein electrophoresisNative PAGE was run using either pre-cast TGX gels (BioRad, Hercules, California) of varying percentage (7.5% to 12% or 8–16% gradient gels) or on hand-cast gels (TGX FastCast, BioRad) made according to the manufacturer’s instructions.Blue-native PAGE was used to estimate the mass of thiaminases in their native conformation. Blue-native PAGE45 gels were run using the NativePage Novex Bis–Tris system (Life Technologies) or hand-cast equivalents46. Light blue cathode buffer was used to facilitate visualization of the activity stain.Standard denaturing SDS-PAGE was used to estimate the molecular mass of thiaminases after denaturation. Denaturing SDS-PAGE was run using one of three relatively equivalent methods: pre-cast TGX gels (BioRad) according to the manufacturer’s instructions, hand-cast Tris–HCl gels using standard Laemmli chemistry47 with an operating pH of approximately 9.5, or hand-cast Bis–Tris gels (MOPS buffer) with an operating pH of approximately 7. For all denaturing and non-denaturing SDS-PAGE applications, standard Laemmli sample buffer was used, and samples were heated to 75 °C for 15 min to facilitate denaturation followed by brief centrifugation to eliminate any precipitated debris.Non-denaturing PAGE was used as an alternative to denaturing PAGE for the common carp thiaminase that could not be renatured (i.e., activity could not be recovered) following a denaturing SDS-PAGE. Non-denaturing PAGE was conducted using any of the three aforementioned gel chemistries with SDS-containing running buffers including reductant (DTT), but samples were not heated prior to application to the gel. Samples for non-denaturing PAGE were allowed to incubate in sample buffer at room temperature for 30 min prior to gel loading. This preserves the charge-shift induced by SDS but does not result in protein denaturation, facilitating in-gel analysis of thiaminase I activity after separation.To visualize proteins following electrophoresis, gels were stained with Coomassie stain (CBR-250 at 1 g/L in methanol/acetic acid/water (4:5:1) and destained with methanol/acetic acid/water (1.7:1:11.5). Mini-gels were run on BioRad’s mini-protean gel rigs. Midi-gels (16 cm length) were run on Hoefer’s SE660, and large-format gels (32 cm length) were run on a BioRad’s Protean Slab Cell. Mini-gels were generally run at room temperature, and midi- and large-format gels were run at 4 °C. Blue-native PAGE was always run at 4 °C.Two-dimensional electrophoresis (2DE) separated proteins in the first dimension based on pI and in the second dimension based on mass (either native or denatured). 2DE was performed by combining in-gel IEF with either denaturing SDS-PAGE, non-denaturing SDS-PAGE, or native PAGE. IPG strips were incubated in TRIS-buffered equilibration solution48 either with 6 M urea, SDS, and iodacetamide (denaturing) or without urea, SDS, and iodacetamide (non-denaturing) for 20 min. Low melting point agarose was used to solidify IGP strips in place. Agarose was cooled to just above the gelling temperature, as hot agarose inactivated thiaminase I activity.Isoelectric focusingIsoelectric focusing (IEF) was conducted both in-gel and in-liquid. In-gel IEF was conducted in immobilized pH gradient (IPG) strips using a Multifor II (GE Healthcare Life Sciences). Prior to rehydration, all protein preparations were desalted in low-salt (~ 5 to 10 mM) sodium or potassium phosphate buffer (pH 6.5) using 10 kDA MWCO filters. All samples were applied using sample volumes and protein concentrations recommended by the manufacturer. For standard denaturing in-gel IEF, rehydration solution consisted of 8 M urea, 2% CHAPS, 2% IPG buffer of the appropriate pH-range, 1% bromophenol blue, and 18 mM DTT. The IEF was conducted at maximum of 2 mA total current and 5 W total power, with an EPS3500 XL power supply in gradient mode. Voltage gradients were based on standard protocols recommended by the manufacturer. In-gel IEF was also performed under native conditions to allow thiaminase I activity staining of IPG strips. Protocols were essentially the same as those for denaturing conditions, with the following exceptions: (1) urea was eliminated and the CHAPS concentration was reduced to 0.5% in the rehydration solution; (2) rehydration was conducted at 14 °C; and (3) the water in the cooling tray was cooled to 4 °C.In-liquid IEF was conducted using a Rotofor (BioRad) according to the manufacturer’s instructions. Non-denaturing in-liquid IEF was also conducted using a focusing solution including no urea, 2% pH 3–10 biolyte, 0.5% CHAPS, 20% glycerol, and 5 mM DTT. The addition of glycerol helped retain activity but also increased focusing times. The Rotofor was run at a constant 15 W with a maximum current of 20 mA and voltage set for a maximum of 2000 V. Samples containing 8 M urea were cooled to 14 °C during focusing to avoid urea precipitation, whereas samples lacking urea were cooled to 4 °C during focusing. Protein extracts in salt solutions greater than 10 mM were desalted directly in focusing solution using a 10 kDA MWCO filter. Focusing runs were allowed to proceed until the voltage stabilized and fractions were harvested with the needle array and vacuum pump. Ampholytes were removed by addition of NaCl to 1 M and then samples were desalted into phosphate buffer using a 10kD MWCO filter.Thiaminase I activity measurementsFor quantitative measurements of thiaminase I activity, we conducted a radiometric assay at CERC as previously described49. Zebrafish homogenates were diluted 1:8, 1:16, or 1:32 in cold phosphate buffer, pH 6.5. Two replicates per dilution were assayed. Activity was calculated from the greatest dilution that gave activity within the linear range of the assay and was reported as pmol thiamine consumed per g tissue (wet weight) per minute (pmol/g/min).Thiaminase I activity stainingAfter electrophoresis, gels were stained for thiaminase I activity using a previously described diazo-coupling reaction19,50. Briefly, gels were washed 3 times in water, twice in 25 mM sodium phosphate buffer with 1 mM DTT, and once in 25 mM sodium phosphate buffer without DTT. Gels were then incubated in 0.89 mM thiamine-HCl and co-substrate (1.45 mM pyridoxine, 24 mM nicotinic acid, or 20 mM pyridine) in 25 mM sodium phosphate buffer for 10 min. Gels were briefly rinsed in water and placed in a lidded container and incubated at 37 °C for 30 min to allow thiamine degradation by any thiaminases in the gel. The diazo stain19,50 was then applied to detect remaining thiamine in the gel for five minutes with gentle agitation. Stained gels were rinsed with water and photographed, and further stained with Coomassie to visualize proteins. More

  • in

    Spatial genetic structure of European wild boar, with inferences on late-Pleistocene and Holocene demographic history

    Ai H, Fang X, Yang B, Huang Z, Chen H, Mao L et al. (2015) Adaptation and possible ancient interspecies introgression in pigs identified by whole-genome sequencing. Nat Genet 47:217–225Article 
    CAS 

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

    Google Scholar 
    Alexandri P, Megens HJ, Crooijmans RPMA, Groenen MAM, Goedbloed DJ, Herrero-Medrano JM et al. (2017) Distinguishing migration events of different timing for wild boar in the Balkans. J Biogeogr 44:259–270Article 

    Google Scholar 
    Alexandri P, Triantafyllidis A, Papakostas S, Chatzinikos E, Platis P, Papageorgiou N et al. (2012) The Balkans and the colonization of Europe: the post-glacial range expansion of the wild boar, Sus scrofa. J Biogeogr 39:713–723Article 

    Google Scholar 
    Alves PC, Pinheiro I, Godinho R, Vicente JJ, Gortázar C, Scandura M et al. (2010) Genetic diversity of wild boar populations and domestic pig breeds (Sus scrofa) in South-western Europe. Biol J Linn Soc 101:797–822Article 

    Google Scholar 
    Apollonio M, Andersen R, Putman R (2010) European ungulates and their management in the 21st century (M Apollonio, R Andersen, and R Putman, Eds.) Cambridge University Press: Cambridge, UKAzzaroli A, De Giuli C, Ficcarelli G, Torre D (1988) Late pliocene to early mid-pleistocene mammals in Eurasia: Faunal succession and dispersal events. Palaeogeogr Palaeoclimatol Palaeoecol 66:77–100Article 

    Google Scholar 
    Bérénos C, Ellis PA, Pilkington JG, Pemberton JM (2016) Genomic analysis reveals depression due to both individual and maternal inbreeding in a free‐living mammal population. Mol Ecol 25:3152–3168Article 

    Google Scholar 
    Braga RT, Rodrigues JFM, Diniz-Filho JAF, Rangel TF (2019) Genetic population structure and allele surfing during range expansion in dynamic habitats. An da Academia Brasileira de Ciências 91:e20180179Article 

    Google Scholar 
    Bragina EV, Ives AR, Pidgeon AM, Kuemmerle T, Baskin LM, Gubar YP, Piquer-Rodríguez M, Keuler NS, Petrosyan VG, Radeloff VC (2015) Rapid Declines of Large Mammal Populations after the Collapse of the Soviet Union. Cons Biol 29:844–853Article 

    Google Scholar 
    Brewer S, Cheddadi R, de Beaulieu JL, Reille M, Allen J, Almqvist-Jacobson H et al. (2002) The spread of deciduous Quercus throughout Europe since the last glacial period. For Ecol Manag 156:27–48Article 

    Google Scholar 
    Cahill S, Llimona F, Cabañeros L, Calomardo F (2012) Characteristics of wild boar (Sus scrofa) habituation to urban areas in the Collserola Natural Park (Barcelona) and comparison with other locations. Anim Biodivers Conserv 35:221–233Article 

    Google Scholar 
    Canu A, Costa S, Iacolina L, Piatti P, Apollonio M, Scandura M (2014) Are captive wild boar more introgressed than free-ranging wild boar? Two case studies in Italy. Eur J Wildl Res 60:459–467Article 

    Google Scholar 
    Canu A, Vilaça STT, Iacolina L, Apollonio M, Bertorelle G, Scandura M (2016) Lack of polymorphism at the MC1R wild-type allele and evidence of domestic allele introgression across European wild boar populations. Mamm Biol 81:477–479Article 

    Google Scholar 
    Carranza J, Salinas M, de Andrés D, Pérez-González J (2016) Iberian red deer: paraphyletic nature at mtDNA but nuclear markers support its genetic identity. Ecol Evol 6:905–922Article 

    Google Scholar 
    Chang CC, Chow CC, Tellier LCAM, Vattikuti S, Purcell SM, Lee JJ (2015) Second-generation PLINK: Rising to the challenge of larger and richer datasets. Gigascience 4:1–16Article 

    Google Scholar 
    Cheddadi R, Bar-Hen A (2009) Spatial gradient of temperature and potential vegetation feedback across Europe during the late Quaternary. Clim Dyn 32:371–379Article 

    Google Scholar 
    Clark PU, Dyke AS, Shakun JD, Carlson AE, Clark J, Wohlfarth B et al. (2009) The Last Glacial Maximum. Science 325:710–714Article 
    CAS 

    Google Scholar 
    DeGiorgio M, Rosenberg NA (2013) Geographic sampling scheme as a determinant of the major axis of genetic variation in principal components analysis. Mol Biol Evol 30:480–488Article 
    CAS 

    Google Scholar 
    Deinet S, Ieronymidou C, McRae L, Burfield IJ, Foppen RP, Collen B, et al. (2013) Wildlife comeback in Europe. The recovery of selected mammal and bird species. London, UKEckert CG, Samis KE, Lougheed SC (2008) Genetic variation across species’ geographical ranges: the central–marginal hypothesis and beyond. Mol Ecol 17:1170–1188Article 
    CAS 

    Google Scholar 
    Fang M, Berg F, Ducos A, Andersson L (2006) Mitochondrial haplotypes of European wild boars with 2n = 36 are closely related to those of European domestic pigs with 2n = 38. Anim Genet 37:459–464Article 
    CAS 

    Google Scholar 
    Ferenčaković M, Sölkner J, Curik I (2013) Estimating autozygosity from high-throughput information: Effects of SNP density and genotyping errors. Genet Sel Evol 45:42Article 

    Google Scholar 
    Ferreira E, Souto L, Soares AMVM, Fonseca C (2009) Genetic structure of the wild boar population in Portugal: Evidence of a recent bottleneck. Mamm Biol 74:274–285Article 

    Google Scholar 
    Franois O, Currat M, Ray N, Han E, Excoffier L, Novembre J (2010) Principal component analysis under population genetic models of range expansion and admixture. Mol Biol Evol 27:1257–1268Article 

    Google Scholar 
    Frantz AC, Bertouille S, Eloy MC, Licoppe A, Chaumont F, Flamand MC (2012) Comparative landscape genetic analyses show a Belgian motorway to be a gene flow barrier for red deer (Cervus elaphus), but not wild boars (Sus scrofa). Mol Ecol 21:3445–3457Article 
    CAS 

    Google Scholar 
    Fulgione D, Rippa D, Buglione M, Trapanese M, Petrelli S, Maselli V (2016) Unexpected but welcome. Artificially selected traits may increase fitness in wild boar. Evol Appl 9:769–776Article 
    CAS 

    Google Scholar 
    Goedbloed DJ, Megens HJ, van Hooft P, Herrero-Medrano JM, Lutz W, Alexandri P et al. (2013a) Genome-wide single nucleotide polymorphism analysis reveals recent genetic introgression from domestic pigs into Northwest European wild boar populations. Mol Ecol 22:856–866Article 
    CAS 

    Google Scholar 
    Goedbloed DJ, van Hooft P, Megens HJ, Langenbeck K, Lutz W, Crooijmans RPMA et al. (2013b) Reintroductions and genetic introgression from domestic pigs have shaped the genetic population structure of Northwest European wild boar. BMC Genet 14:2–10Article 

    Google Scholar 
    Groenen MAM, Archibald AL, Uenishi H, Tuggle CK, Takeuchi Y, Rothschild MF et al. (2012) Analyses of pig genomes provide insight into porcine demography and evolution. Nature 491:393–398Article 
    CAS 

    Google Scholar 
    Herrero-Medrano JM, Megens H-J, Groenen MAM, Ramis G, Bosse M, Pérez-Enciso M et al. (2013) Conservation genomic analysis of domestic and wild pig populations from the Iberian Peninsula. BMC Genet 14:1–13Article 

    Google Scholar 
    Hewitt GM (1999) Post-glacial re-colonization of European biota. Biol J Linn Soc 68:87–112Article 

    Google Scholar 
    Hewitt GM (2004) Genetic consequences of climatic oscillations in the Quaternary. Philos Trans R Soc Lond Ser B Biol Sci 359:183–195Article 
    CAS 

    Google Scholar 
    Hiemstra PH, Pebesma EJ, Twenhöfel CJW, Heuvelink GBM (2009) Real-time automatic interpolation of ambient gamma dose rates from the Dutch radioactivity monitoring network. Comput Geosci 35:1711–1721Article 
    CAS 

    Google Scholar 
    Howrigan DP, Simonson MA, Keller MC (2011) Detecting autozygosity through runs of homozygosity: a comparison of three autozygosity detection algorithms. BMC Genomics 12:460Article 
    CAS 

    Google Scholar 
    Huisman J, Kruuk LEB, Ellis PA, Clutton-Brock T, Pemberton JM (2016) Inbreeding depression across the lifespan in a wild mammal population. Proc Natl Acad Sci 113:3585–3590Article 
    CAS 

    Google Scholar 
    Iacolina L, Corlatti L, Buzan E, Safner T, Šprem N (2019) Hybridisation in European ungulates: an overview of the current status, causes, and consequences. Mamm Rev 49:45–59Article 

    Google Scholar 
    Iacolina L, Pertoldi C, Amills M, Kusza S, Megens H-J, Bâlteanu VA et al. (2018) Hotspots of recent hybridization between pigs and wild boars in Europe. Sci Rep. 8:17372Article 
    CAS 

    Google Scholar 
    Iacolina L, Scandura M, Goedbloed DJ, Alexandri P, Crooijmans RPMA, Larson G et al. (2016) Genomic diversity and differentiation of a managed island wild boar population. Heredity 116:60–67Article 
    CAS 

    Google Scholar 
    Jombart T, Ahmed I (2011) adegenet 1.3-1: new tools for the analysis of genome-wide SNP data. Bioinformatics 27:1–2Article 

    Google Scholar 
    de Jong JF, Hooft van P, Megens HJ, Crooijmans RPMA, Groot de GA, Pemberton JM, Huisman J et al. (2020) Fragmentation and translocation distort the genetic landscape of ungulates: red deer in the Netherlands. Front Ecol Evol 8:535715Article 

    Google Scholar 
    Kamvar ZN, Tabima JF, Grünwald NJ (2014) Poppr: an R package for genetic analysis of populations with clonal, partially clonal, and/or sexual reproduction. PeerJ 2:e281Article 

    Google Scholar 
    Kardos M, Åkesson M, Fountain T, Flagstad Ø, Liberg O, Olason P et al. (2018) Genomic consequences of intensive inbreeding in an isolated wolf population. Nat Ecol Evol 2:124–131Article 

    Google Scholar 
    Kaplan JO, Krumhardt KM, Zimmermann N (2009) The prehistoric and preindustrial deforestation of Europe. Quat Sci Rev 28:3016–3034. https://doi.org/10.1016/j.quascirev.2009.09.028Koemle D, Zinngrebe Y, Yu X (2018) Highway construction and wildlife populations: Evidence from Austria. Land use policy 73:447–457Article 

    Google Scholar 
    Krže B (1982) Divji prašič: biologija, gojitev, ekologija. Lovska zveza Slovenije, Ljubljana
    Google Scholar 
    Kusza S, Podgórski T, Scandura M, Borowik T, Jávor A, Sidorovich VE et al. (2014) Contemporary genetic structure, phylogeography and past demographic processes of wild boar Sus scrofa population in central and eastern Europe. PLoS One 9:e91401Article 

    Google Scholar 
    Lorenzini R, Lovari S, Masseti M (2002) The rediscovery of the Italian roe deer: Genetic differentiation and management implications. Ital J Zool 69(4):367–379Article 

    Google Scholar 
    Lorenzini R, San José C, Braza F, Aragón S (2003) Genetic differentiation and phylogeography of roe deer in Spain, as suggested by mitochondrial DNA and microsatellite analysis. Ital J Zool 70(1):89–99Article 
    CAS 

    Google Scholar 
    Magri D (2013) Early to Middle Pleistocene dynamics of plant and mammal communities in South West Europe. Quat Int 288:63–72Article 

    Google Scholar 
    Manunza A, Zidi A, Yeghoyan S, Balteanu VA, Carsai TC, Scherbakov O et al. (2013) A high throughput genotyping approach reveals distinctive autosomal genetic signatures for European and Near Eastern wild boar. PLoS One 8:e55891Article 
    CAS 

    Google Scholar 
    Maselli V, Rippa D, De Luca A, Larson G, Wilkens B, Linderholm A et al. (2016) Southern Italian wild boar population, hotspot of genetic diversity. Hystrix 27:137–144
    Google Scholar 
    McVean G (2009) A genealogical interpretation of principal components analysis. PLoS Genet 5:e1000686Article 

    Google Scholar 
    Megens H-J, Crooijmans RP, Cristobal M, Hui X, Li N, Groenen MA (2008) Biodiversity of pig breeds from China and Europe estimated from pooled DNA samples: differences in microsatellite variation between two areas of domestication. Genet Sel Evol 40:103
    Google Scholar 
    Melis C, Szafrańska PA, Jȩdrzejewska B, Bartoń K (2006) Biogeographical variation in the population density of wild boar (Sus scrofa) in western Eurasia. J Biogeogr 33:803–811Article 

    Google Scholar 
    Mihalik B, Stéger V, Frank K, Szendrei L, Kusza S (2018) Barrier effect of the M3 highway in Hungary on the genetic diversity of wild boar (Sus scrofa) population. Res J Biotechnol 13:32–38
    Google Scholar 
    NCBI (2018) Genome Organism Overview: Sus scrofa (pig). https://www.ncbi.nlm.nih.gov/genome?term=sus%20scrofa%20%5BOrganism%5D&cmd=DetailsSearch&report=OverviewNikolov IS, Gum B, Markov G, Kuehn R (2009) Population genetic structure of wild boar Sus scrofa in Bulgaria as revealed by microsatellite analysis. Acta Theriol (Warsz) 54:193–205Article 

    Google Scholar 
    Nykänen M, Rogan E, Foote AD, Kaschner K, Dabin W, Louis M et al. (2019) Postglacial colonization of northern coastal habitat by bottlenose dolphins: a marine leading-edge expansion? J Hered 110:662–674Article 

    Google Scholar 
    Palombo M, Romana AV-G (2003) Remarks on the biochronology of mammalian faunal complexes from the Pliocene to the Middle Pleistocene in France. Geol Rom: 145–163Paradis E, Claude J, Strimmer K (2004) APE: analysis of phylogenetics and evolution in R language. Bioinformatics 20:289–290Article 
    CAS 

    Google Scholar 
    Purcell S, Neale B, Todd-Brown K, Thomas L, Ferreira MAR, Bender D et al (2007) PLINK: A tool Set for whole-genome association and population-based linkage analyses. Am J Hum Genet 81:559–575. www.cog-genomics.org/plink/1.9/Putman R, Apollonio M, Andersen R (2011) Ungulate management in Europe: problems and practices. Cambridge University Press, Cambridge, UKBook 

    Google Scholar 
    R Core Team (2018) R: A language and environment for statistical computing. Vienna, AustriaRejduch B, Sota E, Ró M, Ko M (2003) Chromosome number polymorphism in a litter of European wild boar (Sus scrofa scrofa L.). Anim Sci Pap Rep. 21:57–62
    Google Scholar 
    Scandura M, Iacolina L, Apollonio M (2011a) Genetic diversity in the European wild boar Sus scrofa: phylogeography, population structure and wild x domestic hybridization: Genetic variation in European wild boar. Mamm Rev 41:125–137Article 

    Google Scholar 
    Scandura M, Iacolina L, Cossu A, Apollonio M (2011b) Effects of human perturbation on the genetic make-up of an island population: The case of the Sardinian wild boar. Heredity 106:1012–1020Article 
    CAS 

    Google Scholar 
    Scandura M, Iacolina L, Crestanello B, Pecchioli E, Di Benedetto MF, Russo V et al. (2008) Ancient vs. recent processes as factors shaping the genetic variation of the European wild boar: Are the effects of the last glaciation still detectable? Mol Ecol 17:1745–1762Article 
    CAS 

    Google Scholar 
    Scandura M, Fabbri G, Caniglia R, Iacolina L, Mattucci F, Mengoni C, Pante G, Apollonio M, Mucci N (2022) Resilience to Historical Human Manipulations in the Genomic Variation of Italian Wild Boar Populations. Front Ecol Evol 10:833081Article 

    Google Scholar 
    Schmitt T, Varga Z (2012) Extra-Mediterranean refugia: the rule and not the exception. Front Zool 9:22Article 

    Google Scholar 
    Sommer RS, Fahlke JM, Schmölcke U, Benecke N, Zachos FE (2009) Quaternary history of the European roe deer Capreolus capreolus. Mamm Rev 39:1–16Article 

    Google Scholar 
    Sommer RS, Nadachowski A (2006) Glacial refugia of mammals in Europe: evidence from fossil records. Mamm Rev 36:251–265Article 

    Google Scholar 
    Sommer RS, Zachos FE (2009) Fossil evidence and phylogeography of temperate species: ‘glacial refugia’ and post-glacial recolonization. J Biogeogr 36:2013–2020Article 

    Google Scholar 
    Sommer RS, Zachos FE, Street M, Jöris O, Skog A, Benecke N (2008) Late Quaternary distribution dynamics and phylogeography of the red deer (Cervus elaphus) in Europe. Quat Sci Rev 27:714–733Article 

    Google Scholar 
    Stillfried M, Fickel J, Börner K, Wittstatt U, Heddergott M, Ortmann S et al. (2017) Do cities represent sources, sinks or isolated islands for urban wild boar population structure? J Appl Ecol 54:272–281Article 

    Google Scholar 
    Taberlet P, Fumagalli L, Wust-Saucy AG, Cossons JF (1998) Comparative phylogeography and post-glacial colonization routes in Europe. Mol Ecol 7:453–461.Article 
    CAS 

    Google Scholar 
    Veličković N, Djan M, Ferreira E, Stergar M, Obreht D, Maletić V et al. (2015) From north to south and back: the role of the Balkans and other southern peninsulas in the recolonization of Europe by wild boar. J Biogeogr 42:716–728Article 

    Google Scholar 
    Veličković N, Ferreira E, Djan M, Ernst M, Obreht Vidaković D, Monaco A et al. (2016) Demographic history, current expansion and future management challenges of wild boar populations in the Balkans and Europe. Heredity 117:348–357Article 

    Google Scholar 
    Vernesi C, Crestanello B, Pecchioli E, Tartari D, Caramelli D, Hauffe H et al. (2003) The genetic impact of demographic decline and reintroduction in the wild boar (Sus scrofa): A microsatellite analysis. Mol Ecol 12:585–595Article 
    CAS 

    Google Scholar 
    Vilaça ST, Biosa D, Zachos F, Iacolina L, Kirschning J, Alves PC et al. (2014) Mitochondrial phylogeography of the European wild boar: The effect of climate on genetic diversity and spatial lineage sorting across Europe. J Biogeogr 41:987–998Article 

    Google Scholar 
    Zachos FE, Frantz AC, Kuehn R, Bertouille S, Colyn M, Niedziałkowska M et al. (2016) Genetic structure and effective population sizes in European red deer (Cervus elaphus) at a continental scale: insights from microsatellite DNA. J Hered 107:318–326 More