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    Ecological niche partitioning in a fragmented landscape between two highly specialized avian flush-pursuit foragers in the Andean zone of sympatry

    1.
    Patterson, B. D., Stotz, D. F., Solari, S., Fitzpatrick, J. W. & Pacheco, V. Contrasting patterns of elevational zonation for birds and mammals in the Andes of southeastern Peru. J. Biogeogr. 25, 593–607 (1998).
    Article  Google Scholar 
    2.
    Cadena, C. D. et al. Latitude, elevational climatic zonation and speciation in New World vertebrates. Proc. R. Soc. B 279, 194–201 (2012).
    PubMed  Article  PubMed Central  Google Scholar 

    3.
    Diamond, J. M. Distributional ecology of New Guinea birds: recent ecological and biogeographical theories can be tested on the bird communities of New Guinea. Science 179, 759–769 (1973).
    ADS  CAS  PubMed  Article  PubMed Central  Google Scholar 

    4.
    Terborgh, J. & Weske, J. S. The role of competition in the distribution of Andean birds. Ecology 56, 562–576 (1975).
    Article  Google Scholar 

    5.
    Garcia-Moreno, J., Arctander, P. & Fjeldsa, J. Strong diversification at the treeline among Metallura hummingbirds. Auk 116, 702–711 (1999).
    Article  Google Scholar 

    6.
    Freeman, B. G. Competitive interactions upon secondary contact drive elevational divergence in tropical birds. Am. Nat. 186, 470–479 (2015).
    PubMed  Article  PubMed Central  Google Scholar 

    7.
    Cadena, C. D. Testing the role of interspecific competition in the evolutionary origin of elevational zonation: an example with Buarremon Brush-finches (Aves, Emberizidae) in the neotropical mountains. Evolution 61, 1120–1136 (2007).
    PubMed  Article  PubMed Central  Google Scholar 

    8.
    Curson, J. & de Juana, E. Spectacled redstart (Myioborus melanocephalus), version 1.0. In Birds of the World (eds del Hoyo, J. et al.) (Cornell Lab of Ornithology, Ithaca, 2020). https://doi.org/10.2173/bow.spered1.01.
    Google Scholar 

    9.
    Harrod, W. D. & Mumme, R. L. Slate-throated redstart (Myioborus miniatus), version 1.0. In Birds of the World (ed. Schulenberg, T. S.) (Cornell Lab of Ornithology, Ithaca, 2020). https://doi.org/10.2173/bow.sltred.01.
    Google Scholar 

    10.
    Remsen, J. V. Jr. & Robinson, S. K. A classification scheme for foraging behavior of birds in terrestrial habitats. Stud. Avian Biol. 13, 144–160 (1990).
    Google Scholar 

    11.
    Jimenez, D. A bird forages through a tree. Elevation: 2263 m. Movie clip at https://macaulaylibrary.org/asset/201110671, added to IBC (Internet Bird Collection) on June 23, 2019; accessed on 26 July, 2020 through Slate-throated Redstart (Myioborus miniatus), version 1.0. (Harrod, W. D. & Mumme R. L.) in Birds of the World (ed. Schulenberg, T. S.); https://doi.org/10.2173/bow.sltred.01 (Cornell Lab of Ornithology, 2016)

    12.
    Jimenez, D. Bird looking for food. Elevation: 2663 m. Movie clip at IBC (Internet Bird Collection (https://macaulaylibrary.org/asset/201955691); Added to IBC on 23 June, 2016; accessed on 26 July, 2020 through Slate-throated Redstart (Myioborus miniatus), version 1.0. (Harrod, W. D. & Mumme R. L.) in Birds of the World (ed. Schulenberg, T. S.); https://doi.org/10.2173/bow.sltred.01 (Cornell Lab of Ornithology, 2016).

    13.
    Jablonski, P. G. A rare predator exploits prey escape behavior: the role of tail fanning and plumage contrast in foraging of the painted redstart (Myioborus pictus). Behav. Ecol. 10, 7–14 (1999).
    Article  Google Scholar 

    14.
    Jablonski, P. G. Searching for conspicuous versus cryptic prey: search rates of flush-pursuing versus substrate-gleaning birds. Condor 104, 657–661 (2002).
    Article  Google Scholar 

    15.
    Jablonski, P. G. et al. Habitat-specific sensory-exploitative signals in birds: propensity of dipteran prey to cause evolution of plumage variation in flush-pursuit birds. Evolution 60, 2633–2642 (2006).
    PubMed  Article  PubMed Central  Google Scholar 

    16.
    Jablonski, P. G., Lee, S. D. & Jerzak, L. Innate plasticity of a predatory behavior: nonlearned context dependence of avian flush-displays. Behav. Ecol. 6, 925–932 (2006).
    Article  Google Scholar 

    17.
    Mumme, R. L. Scare tactics in a Neotropical warbler: white tail feathers enhance flush-pursuit foraging performance in the Slate-throated redstart (Myioborus miniatus). Auk 119, 1024–1035 (2002).
    Google Scholar 

    18.
    Mumme, R. L., Galatowitsch, M. L., Jablonski, P. G., Stawarczyk, T. M. & Cygan, J. P. Evolutionary significance of geographic variation in a plumage-based foraging adaptation: an experimental test in the Slate-throated redstart (Myioborus miniatus). Evolution 60, 1086–1097 (2006).
    PubMed  Article  PubMed Central  Google Scholar 

    19.
    Perez-Eman, J. L., Mumme, R. L. & Jablonski, P. G. Phylogeography and adaptive plumage evolution in Central American subspecies of the slate-throated redstart (Myioborus miniatus). Ornithol. Monogr. 67, 90–102 (2010).
    Article  Google Scholar 

    20.
    Dawkins, R. The Extended Phenotype (Oxford University Press, Oxford, 1983).
    Google Scholar 

    21.
    Jablonski, P. G. & Lee, S. D. Effects of visual stimuli, substrate borne vibrations and air current stimuli on escape reactions in insect prey of flush-pursuing birds and their implications for evolution of flush-pursuers. Behaviour 143, 303–324 (2006).
    Article  Google Scholar 

    22.
    Jablonski, P. G. & Strausfeld, N. J. Exploitation by a recent avian predator of an ancient arthropod escape circuit: prey sensitivity and elements of the displays by predators. Brain Behav. Evol. 56, 94–106 (2000).
    CAS  PubMed  Article  PubMed Central  Google Scholar 

    23.
    Jablonski, P. G. & Strausfeld, N. J. Exploitation of an ancient escape circuit by an avian predator: relationships between taxon-specific prey escape circuits and the sensitivity to visual cues from the predator. Brain Behav. Evol. 58, 218–240 (2001).
    CAS  PubMed  Article  PubMed Central  Google Scholar 

    24.
    Boles, W. Black fantail (Rhipidura atra), version 1.0. In Birds of the World (eds del Hoyo, J. et al.) (Cornell Lab of Ornithology, Ithaca, 2020). https://doi.org/10.2173/bow.blafan1.01.
    Google Scholar 

    25.
    Boles, W. Dimorphic fantail (Rhipidura brachyrhyncha), version 1.0. In Birds of the World (eds del Hoyo, J. et al.) (Cornell Lab of Ornithology, Ithaca, 2020). https://doi.org/10.2173/bow.dimfan1.01.
    Google Scholar 

    26.
    Moeliker, K. Blue-headed crested-flycatcher (Trochocercus nitens), version 1.0. In Birds of the World (eds del Hoyo, J. et al.) (Cornell Lab of Ornithology, Ithaca, 2020). https://doi.org/10.2173/bow.bhcfly1.01.
    Google Scholar 

    27.
    Clement, P. African blue flycatcher (Elminia longicauda), version 1.0. In Birds of the World (eds del Hoyo, J. et al.) (Cornell Lab of Ornithology, Ithaca, 2020). https://doi.org/10.2173/bow.afbfly1.01.
    Google Scholar 

    28.
    Horak, D. et al. Forest structure determines spatial changes in avian communities along an elevational gradient in tropical Africa. J. Biogeogr. 46, 2466–2478 (2019).
    Article  Google Scholar 

    29.
    Curson, J., Quinn, D. & Beadle, D. New World Warblers (Christopher Helm, London, 1994).
    Google Scholar 

    30.
    Perez-Eman, J. L. Molecular phylogenetics and biogeography of the Neotropical redstarts (Myioborus, Aves, Parulidae). Mol. Phylogen. Evol. 37, 511–528 (2005).
    CAS  Article  Google Scholar 

    31.
    Ridgely, R. S. & Tudor, G. Birds of South America: Passerines (Christopher Helm, London, 2009).
    Google Scholar 

    32.
    Hilbie, C. & Block, N. L. Collared redstart (Myioborus torquatus), version 1.0. In Birds of the World (ed. Schulenberg, T. S.) (Cornell Lab of Ornithology, Ithaca, 2020). https://doi.org/10.2173/bow.colred1.01.
    Google Scholar 

    33.
    Curson, J., del Hoyo, J., Bonan, A., Collar, N. & Kirwan, G. M. Golden-fronted redstart (Myioborus ornatus), version 1.0. In Birds of the World (eds Billerman, S. M. et al.) (Cornell Lab of Ornithology, Ithaca, 2020). https://doi.org/10.2173/bow.gofred1.01.
    Google Scholar 

    34.
    Curson, J. White-fronted redstart (Myioborus albifrons), version 1.0. In Birds of the World (eds del Hoyo, J. et al.) (Cornell Lab of Ornithology, Ithaca, 2020). https://doi.org/10.2173/bow.whfred2.01.
    Google Scholar 

    35.
    Price, T. Speciation in Birds (Roberts and Company, Greenwood Village, 2008).
    Google Scholar 

    36.
    Cadena, C. D. & Loiselle, B. A. Limits to elevational distributions in two species of emberizine finches: disentangling the role of interspecific competition, autoecology, and geographic variation in the environment. Ecography 30, 491–504 (2007).
    Article  Google Scholar 

    37.
    Bussman, R. W. The montane forests of Reserva Biologica San Francisco (Zamora-Chinchipe, Ecuador) Vegetation zonation and natural regeneration. Erde 132, 9–25 (2001).
    Google Scholar 

    38.
    Bussman, R. W. The vegetation of reserva biologica San Francisco, Zamora-Chinchipe, Southern Ecuador—a phytosociological synthesis. In Conservacion de Bioriversidad an los Andes y la Amazonia. Conservation of Biodiversity in the Andes and the Amazon, Cusco, 24–28.09.2001. Memorias del Congreso—Congress Proceedings (eds Bussmann, R. W. & Lange, S.) 71–175 (INKA Cusco, Cuzco, 2002).
    Google Scholar 

    39.
    Ridgely, R. S. & Greenfield, P. J. The Birds of Ecuador (Cornell Univ. Press, Ithaca, 2001).
    Google Scholar 

    40.
    Google. Cascadas de Nambillo by Brian Driscoll. Google Street View, Jul 2018. Accessed 6 August 2020. https://goo.gl/maps/cTv5Cf34LvZV33yTA (2018).

    41.
    Google. Cabanas San Isidro by Daniel Zurita Arthos. Google Street View, Sep 2018.Accessed 6 August 2020. https://goo.gl/maps/NHqLxRMsngRwDbto8 (2018).

    42.
    Google. Milagrosa Waterfall by Elizabeth Clark. Google Street View, Mar 2018. Accessed 6 August 2020. https://goo.gl/maps/SfTW8J8xDVDnpBCC6 (2018).

    43.
    Shopland, J. M. Facultative following of mixed species flocks by two species of neotropical warbler. PhD Dissertation. University of Chicago (1985).

    44.
    Stiles, F. G. & Skutch, A. F. A Guide to the Birds of Costa Rica (Cornell Univ. Press, Ithaca, 1989).
    Google Scholar 

    45.
    Schulenberg, T. S., Stotz, D. F., Lane, D. F., O’Neill, J. P. & Parker, T. A. Birds of Peru (Princeton Univ. Press, Ithaca, 2010).
    Google Scholar 

    46.
    Sullivan, B. L. et al. eBird: a citizen-based bird observation network in the biological sciences. Biol. Conserv. 142, 2282–2292 (2009).
    Article  Google Scholar 

    47.
    eBird. eBird: An online database of bird distribution and abundance [web application]. eBird, Cornell Lab of Ornithology, Ithaca, New York. Available: http://www.ebird.org. Accessed 24 July 2020 (2017).

    48.
    Greeney, H. F. et al. Nesting ecology of the Spectacled Whitestart in Ecuador. Ornitol. Neotrop. 19, 335–344 (2008).
    Google Scholar 

    49.
    Merkord, C. L. Seasonality and Elevational Migration in an ANDEAN BIRD COMMUNITY. PhD Thesis, University of Missouri-Columbia, pp. 154 (2010)

    50.
    Nitta, B. Altitudinal Distribution and Niche Partitioning of Two Redstart Species in Monteverde (Parulidae). Digital Collections > Tropical Ecology Collection [Monteverde Institute], https://digital.lib.usf.edu/?m39.519 (2009).

    51.
    Shopland, J. M. Facultative following of mixed species flocks by two species of Neotropical warbler. Ph.D. Thesis, University of Chicago, Chicago (1985)

    52.
    Brehm, G., Sussenbach, D. & Fiedler, K. Unique elevational diversity patterns of geometrid moths in an Andean montane forest. Ecography 26, 456–466 (2003).
    Article  Google Scholar 

    53.
    Pyrcz, T. W., Wojtusiak, J. & Garlacz, R. Diversity and distribution patterns of Pronophilina butterflies (Lepidoptera: Nymphaliae: Satyrinae) along an altitudinal transect in North-Western Ecuador. Neotrop. Entomol. 38, 716–726 (2009).
    PubMed  Article  PubMed Central  Google Scholar 

    54.
    Brehm, G. & Fiedler, K. Diversity and community structure of geometrid moths of disturbed habitat in a montane area in the Ecuadorian Andes. J. Res. Lepidoptera 38, 1–14 (2005).
    Google Scholar 

    55.
    Janzen, D. H. Sweep samples of tropical foliage insects: effects of seasons, vegetation types, elevation, time of day, and insularity. Ecology 54, 687–708 (1973).
    Article  Google Scholar 

    56.
    Hilt, N. & Fiedler, K. Diversity and composition of Arctiidae moth ensembles along a successional gradient in the Ecuadorian Andes. Divers. Distrib. 11, 387–398 (2005).
    Article  Google Scholar 

    57.
    Harmackova, L., Remesova, E. & Remes, V. Specialization and niche overlap across spatial scales: revealing ecological factors shaping species richness and coexistence in Australian songbirs. J. Anim. Ecol. 88, 1766–1776 (2019).
    PubMed  Article  PubMed Central  Google Scholar 

    58.
    Freeman, B. G., Class Freeman, A. M. & Hochachka, W. M. Asymmetric interspecific aggression in New Guinean songbirds that replace one another along an elevational gradient. Ibis 158, 726–737 (2016).
    Article  Google Scholar 

    59.
    Pyrcz, T. W. & Wojtusiak, J. The vertical distribution of pronophilinae butterflies (Nymphalidae, Satyrinae) along an elevational transect in Monte Zerpa (Cordillera de Merida, Venezuela) with remarks on their diversity and parapatric distribution. Glob. Ecol. Biogeogr. 11, 211–221 (2002).
    Article  Google Scholar 

    60.
    Brehm, G., Zeuss, D. & Colwell, R. K. Moth body size increases with elevation along a complete tropical elevational gradient for two hyperdiverse clades. Ecography 42, 632–642 (2019).
    Article  Google Scholar 

    61.
    Robbins, M. B. et al. Abra Maruncunca, dpto. Puno, Peru, revisited: vegetation cover and avifauna changes over a 30-year period. Bull. B.O.C 133, 31–51 (2013).
    Google Scholar 

    62.
    Pouds, J. A., Fogden, M. P. L. & Campbell, J. H. Biological response to climate change on a tropical mountain. Nature 398, 611–615 (1999).
    ADS  Article  CAS  Google Scholar 

    63.
    Swenson, J. J. et al. Plant and animal endemism in the eastern Andean slope: challenges to conservation. BMC Ecol. 12, 1. https://doi.org/10.1186/1472-6785-12-1 (2012).
    Article  PubMed  PubMed Central  Google Scholar 

    64.
    Valencia, R. Composition and structure of an Andean forest fragment in eastern Ecuador. In Biodiversity and Conservation of Neotropical Montane Forests (eds Churchill, S. et al.) 239–249 (New York Botanical Garden, New York, 1995).
    Google Scholar 

    65.
    Pollard, J. H. On distance estimators of density in randomly distributed forest. Biometrics 27, 991–1002 (1971).
    Article  Google Scholar 

    66.
    Levins, R. Evolution in Changing Environment (Princeton University Press, Princeton, 1968).
    Google Scholar 

    67.
    Pianka, E. R. Niche overlap and diffuse competition. Proc. Nat. Acad. Sci. U.S.A. 71, 2142–2145 (1974).
    ADS  Article  Google Scholar 

    68.
    Sokal, R. R. & Rohlf, F. J. Biometry (Freeman and Co., New York, 1997).
    Google Scholar 

    69.
    McLachlan, G. Discriminant Analysis and Statistical Pattern Recognition (Wiley, Hobolken, 2004).
    Google Scholar 

    70.
    StatSoft Inc. Electronic Statistics Textbook. http://www.statsoft.com/textbook/ (Tulsa, OK: StatSoft. WEB, 2013).

    71.
    Molga, M. Meteorologia rolnicza. PWRiL, Warszawa [in Polish; English translation: Agricultural meteorology. Warszawa: Centralny Instytut Informacji Naukowo-Technicznej i Ekonomicznej, translated by M. Widymski and L. Widymski. OCLC Number: 641437878, 1962], (1986).

    72.
    Nowakowski, J. J. Long-term variability of phenotypic traits in the Sedge Warbler (Acrocephalus schoenobaenus) population in the Biebrza Marshes—Adaptation to the changing environment [in Polish]. Dissertation and Monographs 168, 1–294 (Publishing House of the University of Warmia and Mazury, Olsztyn, 2011).

    73.
    Holm, S. A simple sequential rejective method procedure. Scand. J. Stat. 6, 65–70 (1979).
    MATH  Google Scholar 

    74.
    Nakagawa, S. A farewell to Bonferroni: the problems of low statistical power and publication bias. Behav. Ecol. 15, 1044–1045 (2004).
    Article  Google Scholar 

    75.
    Akaike, H. Information theory and an extension of the maximum likelihood principle. In 2nd Int Symposium on Information Theory (eds Petrov, B. N. & Csaki, F.) 267–281 (Akademia Kiado, Budapest, 1973).
    Google Scholar 

    76.
    Burnham, K. P. & Anderson, D. R. Model Selection and Inference: A Practical Information-Theoretic Approach (Springer, New York, 1998).
    Google Scholar  More

  • in

    Improving climate suitability for Bemisia tabaci in East Africa is correlated with increased prevalence of whiteflies and cassava diseases

    1.
    Kriticos, D. J., Sutherst, R. W., Brown, J. R., Adkins, S. A. & Maywald, G. F. Climate change and the potential distribution of an invasive alien plant: Acacia nilotica ssp. indica in Australia. J. Appl. Ecol. 40(1), 111–124 (2003).
    Article  Google Scholar 
    2.
    Sutherst, R. W. et al. Pests under global change—meeting your future landlords? In Terrestrial Ecosystems in a Changing World (eds Canadell, J. G. et al.) 211–223 (Springer, Berlin, 2007).
    Google Scholar 

    3.
    Sutherst, R.W., Arthropods as disease vectors in a changing environment. In Ciba Foundation Symposium 175—Environmental Change and Human Health (Wiley, 2007), pp. 124–145.

    4.
    Vogl, G. et al. Modelling the spread of ragweed: Effects of habitat, climate change and diffusion. Eur. Phys. J. Spec. Top. 161, 167–173 (2008).
    Article  Google Scholar 

    5.
    Scherm, H., Climate change: can we predict the impacts on plant pathology and pest management?  Presented at the Annual Meeting of the Canadian-Phytopathological-Society, Montreal, Canada, 2003 (unpublished), pp. 267–273.

    6.
    Kocmankova, E. et al. Estimating the impact of climate change on the occurrence of selected pests at a high spatial resolution: A novel approach. J. Agric. Sci. 149, 185–195 (2011).
    Article  Google Scholar 

    7.
    Mardulyn, P. et al. Climate change and the spread of vector-borne diseases: Using approximate Bayesian computation to compare invasion scenarios for the bluetongue virus vector Culicoides imicola in Italy. Mol. Ecol. 22(9), 2456–2466 (2013).
    PubMed  Article  Google Scholar 

    8.
    Ziter, C., Robinson, E. A. & Newman, J. A. Climate change and voltinism in Californian insect pest species: Sensitivity to location, scenario and climate model choice. Glob. Change Biol. 18(9), 2771–2780 (2012).
    ADS  Article  Google Scholar 

    9.
    Estay, S. A., Lima, M. & Labra, F. A. Predicting insect pest status under climate change scenarios: Combining experimental data and population dynamics modelling. J. Appl. Entomol. 113, 491–499 (2009).
    Article  Google Scholar 

    10.
    Parmesan, C. et al. Poleward shifts in geographical ranges of butterfly species associated with regional warming. Nature 399, 579–583 (1999).
    ADS  CAS  Article  Google Scholar 

    11.
    Parmesan, C. et al. Empirical perspectives on species borders: From traditional biogeography to global change. Oikos 108(1), 58–75 (2005).
    Article  Google Scholar 

    12.
    Kerdelhue, C. et al. Quaternary history and contemporary patterns in a currently expanding species. BMC Evol. Biol. 9, 5 (2009).
    Article  CAS  Google Scholar 

    13.
    Battisti, A. et al. Expansion of geographic range in the pine processionary moth caused by increased winter temperature. Ecol. Appl. 15(6), 2084–2096 (2005).
    Article  Google Scholar 

    14.
    Rahmstorf, S. et al. Recent climate observations compared to projections. Science 316(5825), 709 (2007).
    ADS  CAS  PubMed  Article  Google Scholar 

    15.
    Mann, M. E. & Lees, J. M. Robust estimation of background noise and signal detection in climatic time series. Clim. Change 33(3), 409–445 (1996).
    ADS  Article  Google Scholar 

    16.
    Parmesan, C. Ecological and evolutionary responses to recent climate change. Annu. Rev. Ecol. Evol. Syst. 37, 637–669 (2006).
    Article  Google Scholar 

    17.
    Bloomfield, P. & Nychka, D. Climate spectra and detecting climate change. Clim. Change 21(3), 275–287 (1992).
    ADS  Article  Google Scholar 

    18.
    Rosenzweig, C. et al. Attributing physical and biological impacts to anthropogenic climate change. Nature 453(7193), 353–357 (2008).
    ADS  CAS  PubMed  Article  Google Scholar 

    19.
    Sutherst, R. W. et al. Adapting to crop pest and pathogen risks under a changing climate. Wiley Interdiscip. Rev. Clim. Change 2(2), 220–237 (2011).
    Article  Google Scholar 

    20.
    FAOSTAT. Crop Production (Food and Agriculture Organization, Rome, 2015).
    Google Scholar 

    21.
    Nweke, F. I. New Challenges in the Cassava Transformation in Nigeria and Ghana (International Food Policy Research Institute, Washington, 2004).
    Google Scholar 

    22.
    Godfray, H. C. J. et al. Food security: The challenge of feeding 9 billion people. Science 327(5967), 812–818 (2010).
    ADS  CAS  PubMed  PubMed Central  Article  Google Scholar 

    23.
    El-Sharkawy, M. A. Cassava biology and physiology. Plant Mol. Biol. 56(4), 481–501 (2004).
    CAS  PubMed  Article  Google Scholar 

    24.
    Jarvis, A., Ramirez-Villegas, J., Herrera Campo, B. & Navarro-Racines, C. Is Cassava the answer to African climate change adaptation?. Trop. Plant Biol. 5(1), 9–29 (2012).
    Article  Google Scholar 

    25.
    Howeler, R., Lutaladio, N. & Thomas, G. Save and Grow: Cassava. A Guide to Sustainable Production Intensification (FAO, Rome, 2013).
    Google Scholar 

    26.
    Alicai, T. et al. Re-emergence of Cassava Brown Streak Disease in Uganda. Plant Dis. 91(1), 24–29 (2007).
    CAS  PubMed  Article  Google Scholar 

    27.
    Colvin, J., Omongo, C. A., Maruthi, M. N., Otim-Nape, G. W. & Thresh, J. M. Dual begomovirus infections and high Bemisia tabaci populations: Two factors driving the spread of a cassava mosaic disease pandemic. Plant. Pathol. 53, 577–584 (2004).
    Article  Google Scholar 

    28.
    Legg, J. P. et al. Spatio-temporal patterns of genetic change amongst populations of cassava Bemisia tabaci whiteflies driving virus pandemics in East and Central Africa. Virus Res. 186, 61–75 (2014).
    CAS  PubMed  Article  Google Scholar 

    29.
    Thresh, J. et al. African cassava mosaic virus disease: The magnitude of the problem. Afr. J. Root Tuber Crops 2(1/2), 13–19 (1997).
    Google Scholar 

    30.
    Tajebe, L. S. et al. Abundance, diversity and geographic distribution of cassava mosaic disease pandemic-associated Bemisia tabaci in Tanzania. J. Appl. Entomol. 5, 20 (2014).
    Google Scholar 

    31.
    Ndunguru, J. et al. Analyses of twelve new whole genome sequences of Cassava Brown Streak Viruses and Ugandan Cassava Brown Streak Viruses from East Africa: Diversity, supercomputing and evidence for further speciation. PLoS One 10(10), e0139321 (2015).
    PubMed  PubMed Central  Article  CAS  Google Scholar 

    32.
    Basavaprabhu, L. P., Legg, J. P., Kanju, E. & Fauquet, C. M. Cassava brown streak disease: A threat to food security in Africa. J. Gen. Virol. 96(5), 956–968 (2015).
    Article  Google Scholar 

    33.
    Jeremiah, S. C. et al. The dynamics and environmental influence on interactions between Cassava Brown Streak Disease and the whitefly,. Phytopathology 105(5), 646–655 (2015).
    CAS  PubMed  Article  Google Scholar 

    34.
    Legg, J., Owor, B., Sseruwagi, P. & Ndunguru, J. Cassava mosaic virus disease in East and Central Africa: Epidemiology and management of a regional pandemic. Adv. Virus Res. 67, 355–418 (2006).
    CAS  PubMed  Article  Google Scholar 

    35.
    FAO, Cassava Diseases in central, eastern and southern Africa: Strategic programme framework 2010–2015. (2009).

    36.
    Zhou, X. et al. Evidence that DNA-A of a geminivirus associated with severe cassava mosaic disease in Uganda has arisen by interspecific recombination. J. Gen. Virol. 78(8), 2101–2111 (1997).
    CAS  PubMed  Article  Google Scholar 

    37.
    Legg, J. P., French, R., Rogan, D., Okao-Okuja, G. & Brown, J. K. A distinct Bemisia tabaci (Gennadius) (Hemiptera: Sternorrhyncha: Aleyrodidae) genotype cluster is associated with the epidemic of severe cassava mosaic virus disease in Uganda. Mol. Ecol. 11(7), 1219–1229 (2002).
    CAS  PubMed  Article  Google Scholar 

    38.
    Garrett, K.A., Thomas-Sharma, S., Forbes, G.A., & Nopsa, J.H., Climate change and plant pathogen invasions. In Invasive Species and Global Climate Change (eds Ziska, L. H., Dukes, J. S.) 22 (2014).

    39.
    Tay, W. T. et al. The trouble with MEAM2: Implications of pseudogenes on species delimitation in the globally invasive Bemisia tabaci (Hemiptera: Aleyrodidae) cryptic species complex. Genome Biol. Evol. 9(10), 2732–2738 (2017).
    CAS  PubMed  PubMed Central  Article  Google Scholar 

    40.
    Macfadyen, S. et al. Cassava whitefly, Bemisia tabaci (Gennadius) (Hemiptera: Aleyrodidae) in East African farming landscapes: A review of the factors determining abundance. Bull. Entomol. Res. 20, 1–18 (2018).
    Google Scholar 

    41.
    Sseruwagi, P., Sserubombwe, W., Legg, J., Ndunguru, J. & Thresh, J. Methods of surveying the incidence and severity of cassava mosaic disease and whitefly vector populations on cassava in Africa: A review. Virus Res. 100(1), 129–142 (2004).
    CAS  PubMed  Article  Google Scholar 

    42.
    Boykin, L. M. et al. Review and guide to a future naming system of African Bemisia tabaci species. Syst. Entomol. 20, 20 (2018).
    Google Scholar 

    43.
    Mugerwa, H. et al. African ancestry of New World, Bemisia tabaci-whitefly species. Sci. Rep. 8(1), 2734 (2018).
    ADS  PubMed  PubMed Central  Article  CAS  Google Scholar 

    44.
    Boykin, L. M., Armstrong, K. F., Kubatko, L. & De Barro, P. J. Species delimitation and global biosecurity. Evol. Bioinform. 8(2), 1–37 (2011).
    Google Scholar 

    45.
    De Barro, P. J., Liu, S.-S., Boykin, L. M. & Dinsdale, A. B. Bemisia tabaci: A statement of species status. Annu. Rev. Entomol. 56, 1–19 (2011).
    PubMed  Article  CAS  Google Scholar 

    46.
    Boykin, L. M. Bemisia tabaci nomenclature: Lessons learned. Pest Manag. Sci. 70(10), 1454–1459 (2014).
    CAS  PubMed  Article  Google Scholar 

    47.
    Kalyebi, A. et al. African cassava whitefly, Bemisia tabaci, cassava colonization preferences and control implications. PLoS One 13(10), e0204862 (2018).
    PubMed  PubMed Central  Article  CAS  Google Scholar 

    48.
    Herrera Campo, B., Hyman, G. & Bellotti, A. Threats to cassava production: Known and potential geographic distribution of four key biotic constraints. Food Secur. 3(3), 329–345 (2011).
    Article  Google Scholar 

    49.
    Webber, B. L. et al. Modelling horses for novel climate courses: Insights from projecting potential distributions of native and alien Australian acacias with correlative and mechanistic models. Div. Distrib. 17(5), 978–1000 (2011).
    Article  Google Scholar 

    50.
    Sutherst, R. W. & Bourne, A. S. Modelling non-equilibrium distributions of invasive species: A tale of two modelling paradigms. Biol. Invas. 11(6), 1231–1237 (2009).
    Article  Google Scholar 

    51.
    Ramos, R. S., Kumar, L., Shabani, F. & Picanço, M. C. Mapping global risk levels of Bemisia tabaci in areas of suitability for open field tomato cultivation under current and future climates. PLoS One 13(6), e0198925 (2018).
    PubMed  PubMed Central  Article  CAS  Google Scholar 

    52.
    Lobo, J. M., Jiménez-Valverde, A. & Real, R. AUC: A misleading measure of the performance of predictive distribution models. Glob. Ecol. Biogeogr. 17, 145–151 (2008).
    Article  Google Scholar 

    53.
    Kriticos, D. J. et al. CLIMEX Version 4: Exploring the Effects of Climate on Plants, Animals and Diseases (CSIRO, Canberra, 2015).
    Google Scholar 

    54.
    Sutherst, R. W. & Maywald, G. F. A computerised system for matching climates in ecology. Agric. Ecosyst. Environ. 13, 281–299 (1985).
    Article  Google Scholar 

    55.
    Yonow, T., Hattingh, V. & de Villiers, M. CLIMEX modelling of the potential global distribution of the citrus black spot disease caused by Guignardia citricarpa and the risk posed to Europe. Crop Prot. 44, 18–28 (2013).
    Article  Google Scholar 

    56.
    Ireland, K. B., Hardy, G. E. S. J. & Kriticos, D. J. Combining inferential and deductive approaches to estimate the potential geographical range of the invasive plant pathogen, Phytophthora ramorum. PLoS One 8, 5 (2013).
    Google Scholar 

    57.
    Kriticos, D. J. et al. The potential global distribution of the brown marmorated stink bug, Halyomorpha halys, a critical threat to plant biosecurity. J. Pest Sci. 20, 20 (2017).
    Google Scholar 

    58.
    Macfadyen, S. & Kriticos, D. J. Modelling the geographical range of a species with a variable life-history. PLoS One 7(7), e40313 (2012).
    ADS  CAS  PubMed  PubMed Central  Article  Google Scholar 

    59.
    Yonow, T. & Sutherst, R. W. The geographical distribution of the Queensland fruit fly, Bactrocera (Dacus) tryoni, in relation to climate. Aust. J. Agric. Res. 49, 935–953 (1998).
    Article  Google Scholar 

    60.
    De Villiers, M. et al. The potential distribution of Bactrocera dorsalis: Considering phenology and irrigation patterns. Bull. Entomol. Res. 106, 19–33 (2016).
    PubMed  Article  Google Scholar 

    61.
    De Villiers, M., Hattingh, V. & Kriticos, D. J. Combining field phenological observations with distribution data to model the potential range distribution of the fruit fly Ceratitis rosa Karsch (Diptera: Tephritidae). Bull. Entomol. Res. 103, 60–73 (2012).
    PubMed  Article  Google Scholar 

    62.
    Zalucki, M. P. & Furlong, M. J. Forecasting Helicoverpa populations in Australia: A comparison of regression based models and a bio-climatic based modelling approach. Insect Sci. 12(1), 45–56 (2005).
    Article  Google Scholar 

    63.
    Zalucki, M. P. & Van Klinken, R. D. Predicting population dynamics of weed biological control agents: Science or gazing into crystal balls?. Aust. J. Entomol. 45, 331–344 (2006).
    Article  Google Scholar 

    64.
    Kriticos, D. J., De Barro, P. J., Yonow, T., Ota, N. & Sutherst, R. W. The potential geographical distribution and phenology of Bemisia tabaci Middle East Asia Minor 1, considering irrigation and glasshouse production. Bull. Entomol. Res. 110(5), 567–576 (2020).
    CAS  PubMed  Article  Google Scholar 

    65.
    Kriticos, D. J. et al. CliMond: Global high resolution historical and future scenario climate surfaces for bioclimatic modelling. Methods Ecol. Evol. 3, 53–64 (2012).
    Article  Google Scholar 

    66.
    Hutchinson, G.E., Presented at the Cold Spring Symposium on Quantitative Biology, Yale University, New Haven, Connecticutt, USA, 1957 (unpublished).

    67.
    Brown, J. H., Stevens, G. C. & Kaufman, D. M. The geographic range: Size, shape, boundaries, and internal structure. Annu. Rev. Ecol. Syst. 27, 597–623 (1996).
    Article  Google Scholar 

    68.
    Peterson, A. T., Soberon, J., Pearson, R. G. & Martinez-Meyer, E. Ecological Niches and Geographic Distributions (Princeton University Press, Princeton, 2011).
    Google Scholar 

    69.
    Davis, A. J., Jenkinson, L. S., Lawton, J. H., Shorrocks, B. & Wood, S. Making mistakes when predicting shifts in species range in response to global warming. Nature 391, 783–786 (1998).
    ADS  CAS  PubMed  Article  Google Scholar 

    70.
    Carter, R. N. & Prince, S. D. Epidemic models used to explain biogeographical distribution limits. Nature 293, 644–645 (1981).
    ADS  Article  Google Scholar 

    71.
    Alicai, T. et al. Expansion of the cassava brown streak pandemic in Uganda revealed by annual field survey data for 2004 to 2017. Sci. Data 6(1), 327 (2019).
    PubMed  PubMed Central  Article  Google Scholar 

    72.
    Macfadyen, S. et al. Landscape factors and how they influence whitefly pests in cassava fields across East Africa. Landsc. Ecol. 20, 20 (2020).
    Google Scholar 

    73.
    Shelford, V. E. The Ecology of North America (University of Illinois Press, Urbana, 1963).
    Google Scholar 

    74.
    Shelford, V. E. A comparison of the responses of animals in gradients of environmental factors with particular reference to the method of reaction of representatives of the various groups from protozoa to mammals. Science 48, 225–230 (1918).
    ADS  CAS  PubMed  Article  Google Scholar 

    75.
    Shelford, V. E. & Deere, E. O. The reactions of certain animals to gradients of evaporating power of air: A study in experimental ecology. Biol. Bull. 25, 79–120 (1913).
    Article  Google Scholar 

    76.
    van der Ploeg, R. R., Böhm, W. & Kirkham, M. B. On the origin of the theory of mineral nutrition of plants and the law of the minimum. Soil Sci. Soc. Am. J. 63, 1055–1062 (1999).
    Article  Google Scholar 

    77.
    Mitchell, T. D. & Jones, P. D. An improved method of constructing a database of monthly climate observations and associated high-resolution grids. Int. J. Climatol. 25(6), 693–712 (2005).
    Article  Google Scholar 

    78.
    New, M., Hulme, M. & Jones, P. Representing twentieth-century space-time climate variability. Part II: Development of 1901–96 monthly grids of terrestrial surface climate. J. Clim. 13(13), 2217–2238 (2000).
    ADS  Article  Google Scholar 

    79.
    Sseruwagi, P. et al. Colonization of non-cassava plant species by cassava whiteflies (Bemisia tabaci) in Uganda. Entomol. Exp. Appl. 119(2), 145–153 (2006).
    CAS  Article  Google Scholar 

    80.
    Otim-Nape, G., Alicai, T. & Thresh, J. Changes in the incidence and severity of cassava mosaic virus disease, varietal diversity and cassava production in Uganda. Ann. Appl. Biol. 138(3), 313–327 (2001).
    Article  Google Scholar 

    81.
    R Core Team. R: A Language and Environment for Statistical Computing (R Foundation for Statistical Computing, Vienna, 2013).
    Google Scholar 

    82.
    Castle, S., Henneberry, T. & Toscano, N. Suppression of Bemisia tabaci (Homoptera: Aleyrodidae) infestations in cantaloupe and cotton with sprinkler irrigation. Crop Prot. 15(7), 657–663 (1996).
    Article  Google Scholar 

    83.
    Alemandri, V. et al. Three members of the Bemisia tabaci (Hemiptera: Aleyrodidae) cryptic species complex occur sympatrically in Argentine horticultural crops. J. Econ. Entomol. 108(2), 405–413 (2015).
    CAS  PubMed  Article  Google Scholar 

    84.
    Mitchell, J. et al., Detection of climate change and attribution of causes in IPCC 2001: Climate Change 2001. The Climate change Contribution of Working Group I to the Third Assessment Report of the Intergovemmental Panel on Climate Change, edited by J Houghton et al. (2001), Vol. 159.

    85.
    McQuaid, C. F. et al. Spatial dynamics and control of a crop pathogen with mixed-mode transmission. PLoS Comput. Biol. 13(7), e1005654 (2017).
    PubMed  PubMed Central  Article  CAS  Google Scholar 

    86.
    Fauquet, C. & Fargette, D. African cassava mosaic virus: Etiology, epidemiology and control. Plant Dis. 74(6), 404–411 (1990).
    Article  Google Scholar 

    87.
    Aregbesola, O. Z., Legg, J. P., Sigsgaard, L., Lund, O. S. & Rapisarda, C. Potential impact of climate change on whiteflies and implications for the spread of vectored viruses. J. Pest. Sci. 20, 20 (2018).
    Google Scholar 

    88.
    Hilje, L., Costa, H. S. & Stansly, P. A. Cultural practices for managing Bemisia tabaci and associated viral diseases. Crop Prot. 20(9), 801–812 (2001).
    Article  Google Scholar 

    89.
    Anderson, P. K. et al. Emerging infectious diseases of plants: Pathogen pollution, climate change and agrotechnology drivers. Trends Ecol. Evol. 19(10), 535–544 (2004).
    PubMed  Article  Google Scholar 

    90.
    Chakraborty, S., Tiedemann, A. V. & Teng, P. S. Climate change: Potential impact on plant diseases. Environ. Pollut. 108(3), 317–326 (2000).
    CAS  PubMed  Article  Google Scholar 

    91.
    Jones, R. A. Plant virus emergence and evolution: Origins, new encounter scenarios, factors driving emergence, effects of changing world conditions, and prospects for control. Virus Res. 141(2), 113–130 (2009).
    CAS  PubMed  Article  Google Scholar 

    92.
    Canto, T., Aranda, M. A. & Fereres, A. Climate change effects on physiology and population processes of hosts and vectors that influence the spread of hemipteran-borne plant viruses. Glob. Change Biol. 15(8), 1884–1894 (2009).
    ADS  Article  Google Scholar 

    93.
    Fargette, D., Jeger, M., Fauquet, C. & Fishpool, L. Analysis of temporal disease progress of African cassava mosaic virus. Phytopathology 84(1), 91–98 (1994).
    Article  Google Scholar 

    94.
    Pardey, P. G. et al. Right-sizing stem rust research. Science 340, 147–148 (2013).
    ADS  CAS  PubMed  Article  Google Scholar 

    95.
    Dodson, B. Porous borders: Gender and migration in Southern Africa. S. Afr. Geogr. J. 82(1), 40–46 (2000).
    Article  Google Scholar 

    96.
    Ikome, F.N., Africa’s international borders as potential sources of conflict and future threats to peace and security (2012). More

  • in

    Multiple forms of hotspots of tetrapod biodiversity and the challenges of open-access data scarcity

    1.
    Gaston, K. J. & Blackburn, T. Pattern and Process in Macroecology (Blackwell Science, London, 2000).
    Google Scholar 
    2.
    Gaston, K. J. Global patterns in biodiversity. Nature 405, 220–227. https://doi.org/10.1038/35012228 (2000).
    CAS  Article  PubMed  Google Scholar 

    3.
    Parmesan, C. Ecological and evolutionary responses to recent climate change. Annu. Rev. Ecol. Evol. Syst. 37, 637–669. https://doi.org/10.1146/annurev.ecolsys.37.091305.110100 (2006).
    Article  Google Scholar 

    4.
    Lovejoy, T. E. & Hannah, L. E. E. Biodiversity and Climate Change: Transforming the Biosphere (Yale University Press, New Haven, 2019).
    Google Scholar 

    5.
    Grenyer, R. et al. Global distribution and conservation of rare and threatened vertebrates. Nature 444, 93–96. https://doi.org/10.1038/nature05237 (2006).
    ADS  CAS  Article  PubMed  Google Scholar 

    6.
    Rodrigues, A. S. L. et al. Spatially explicit trends in the global conservation status of vertebrates. PLoS ONE 9, e113934. https://doi.org/10.1371/journal.pone.0113934 (2014).
    ADS  CAS  Article  PubMed  PubMed Central  Google Scholar 

    7.
    Butchart, S. H. et al. Global biodiversity: indicators of recent declines. Science 328, 1164–1168. https://doi.org/10.1126/science.1187512 (2010).
    ADS  CAS  Article  PubMed  Google Scholar 

    8.
    Dirzo, R. et al. Defaunation in the anthropocene. Science 345, 401–406. https://doi.org/10.1126/science.1251817 (2014).
    ADS  CAS  Article  Google Scholar 

    9.
    Urban, M. C. Accelerating extinction risk from climate change. Science 348, 571–573. https://doi.org/10.1126/science.aaa4984 (2015).
    ADS  CAS  Article  PubMed  Google Scholar 

    10.
    Cardinale, B. J. et al. Biodiversity loss and its impact on humanity. Nature 486, 59–67. https://doi.org/10.1038/nature11148 (2012).
    ADS  CAS  Article  PubMed  Google Scholar 

    11.
    Mora, C., Tittensor, D. P., Adl, S., Simpson, A. G. & Worm, B. How many species are there on earth and in the ocean?. PLoS Biol. 9, e1001127. https://doi.org/10.1371/journal.pbio.1001127 (2011).
    CAS  Article  PubMed  PubMed Central  Google Scholar 

    12.
    Brooks, T. M. et al. Global biodiversity conservation priorities. Science 313, 58–61. https://doi.org/10.1126/science.1127609 (2006).
    ADS  CAS  Article  PubMed  Google Scholar 

    13.
    Margules, C. R. & Pressey, R. L. Systematic conservation planning. Nature 405, 243–253. https://doi.org/10.1038/35012251 (2000).
    CAS  Article  PubMed  PubMed Central  Google Scholar 

    14.
    Myers, N., Mittermeier, R. A., Mittermeier, C. G., Da Fonseca, G. A. & Kent, J. Biodiversity hotspots for conservation priorities. Nature 403, 853–858. https://doi.org/10.1038/35002501 (2000).
    ADS  CAS  Article  PubMed  PubMed Central  Google Scholar 

    15.
    Reid, W. V. Biodiversity hotspots. Trends Ecol. Evol. 13, 275–280. https://doi.org/10.1016/S0169-5347(98)01363-9 (1998).
    CAS  Article  PubMed  Google Scholar 

    16.
    Myers, N. Biodiversity hotspots revisited. Bioscience 53, 916–917. https://doi.org/10.1641/0006-3568(2003)053[0916:BHR]2.0.CO;2 (2003).
    Article  Google Scholar 

    17.
    Mittermeier, R. A., Turner, W. R., Larsen, F. W., Brooks, T. M. & Gascon, C. in Biodiversity Hotspots (eds F. Zachos & J. Habel) 3–22 (Springer, Berlin, 2011).

    18.
    Böhm, M. et al. The conservation status of the world’s reptiles. Biol. Conserv. 157, 372–385. https://doi.org/10.1016/j.biocon.2012.07.015 (2013).
    Article  Google Scholar 

    19.
    Marchese, C. Biodiversity hotspots: a shortcut for a more complicated concept. Glob. Ecol. Conserv. 3, 297–309. https://doi.org/10.1016/j.gecco.2014.12.008 (2015).
    Article  Google Scholar 

    20.
    Crossman, N. D., Bryan, B. A. & Summers, D. M. Identifying priority areas for reducing species vulnerability to climate change. Divers. Distrib. 18, 60–72. https://doi.org/10.1111/j.1472-4642.2011.00851.x (2012).
    Article  Google Scholar 

    21.
    Fagundes, C. K., Vogt, R. C., de Souza, R. A. & De Marco Jr, P. Vulnerability of turtles to deforestation in the Brazilian Amazon: indicating priority areas for conservation. Biol. Conserv. 226, 300–310. https://doi.org/10.1016/j.biocon.2018.08.009 (2018).
    Article  Google Scholar 

    22.
    Trombulak, S. C. in Landscape-scale Conservation Planning (eds Stephen C. Trombulak & Robert F. Baldwin) 303–324 (Springer Netherlands, 2010).

    23.
    Reddy, C. S., Faseela, V. S., Unnikrishnan, A. & Jha, C. S. Earth observation data for assessing biodiversity conservation priorities in South Asia. Biodivers. Conserv. 28, 2197–2219. https://doi.org/10.1007/s10531-018-1681-0 (2019).
    Article  Google Scholar 

    24.
    Schmitt, C. B. in Biodiversity Hotspots: Distribution and Protection of Conservation Priority Areas (eds Frank E. Zachos & Jan Christian Habel) 23–42 (Springer Berlin Heidelberg, 2011).

    25.
    Asaad, I., Lundquist, C. J., Erdmann, M. V. & Costello, M. J. Ecological criteria to identify areas for biodiversity conservation. Biol. Conserv. 213, 309–316. https://doi.org/10.1016/j.biocon.2016.10.007 (2017).
    Article  Google Scholar 

    26.
    McRae, L., Deinet, S. & Freeman, R. The diversity-weighted living planet index: controlling for taxonomic bias in a global biodiversity indicator. PLoS ONE 12, e0169156. https://doi.org/10.1371/journal.pone.0169156 (2017).
    CAS  Article  PubMed  PubMed Central  Google Scholar 

    27.
    Whittaker, R. J. et al. Conservation biogeography: assessment and prospect. Divers. Distrib. 11, 3–23. https://doi.org/10.1111/j.1366-9516.2005.00143.x (2005).
    Article  Google Scholar 

    28.
    Hortal, J. et al. Seven shortfalls that beset large-scale knowledge of biodiversity. Annu. Rev. Ecol. Evol. Syst. 46, 523–549. https://doi.org/10.1146/annurev-ecolsys-112414-054400 (2015).
    Article  Google Scholar 

    29.
    Ondei, S., Brook, B. W. & Buettel, J. C. Nature’s untold stories: an overview on the availability and type of on-line data on long-term biodiversity monitoring. Biodivers. Conserv. 27, 2971–2987. https://doi.org/10.1007/s10531-018-1582-2 (2018).
    Article  Google Scholar 

    30.
    Schmeller, D. S. et al. Building capacity in biodiversity monitoring at the global scale. Biodivers. Conserv. 26, 2765–2790. https://doi.org/10.1007/s10531-017-1388-7 (2017).
    Article  Google Scholar 

    31.
    Amano, T. & Sutherland, W. J. Four barriers to the global understanding of biodiversity conservation: wealth, language, geographical location and security. Proc. R. Soc. B Biol. Sci. 280, 20122649. https://doi.org/10.1098/rspb.2012.2649 (2013).
    Article  Google Scholar 

    32.
    Roll, U. et al. The global distribution of tetrapods reveals a need for targeted reptile conservation. Nat. Ecol. Evol. 1, 1677–1682. https://doi.org/10.1038/s41559-017-0332-2 (2017).
    Article  PubMed  Google Scholar 

    33.
    Hoffmann, M. et al. The impact of conservation on the status of the world’s vertebrates. Science 330, 1503–1509. https://doi.org/10.1126/science.1194442 (2010).
    ADS  CAS  Article  Google Scholar 

    34.
    Meiri, S. et al. Extinct, obscure or imaginary: the lizard species with the smallest ranges. Divers. Distrib. 24, 262–273. https://doi.org/10.1111/ddi.12678 (2018).
    Article  Google Scholar 

    35.
    Hudson, L. N. et al. The PREDICTS database: a global database of how local terrestrial biodiversity responds to human impacts. Ecol. Evol. 4, 4701–4735. https://doi.org/10.1002/ece3.1303 (2014).
    Article  PubMed  PubMed Central  Google Scholar 

    36.
    Gaston, K. J. Biodiversity-congruence. Prog. Phys. Geogr. 20, 105–112 (1996).
    Article  Google Scholar 

    37.
    Orme, C. D. et al. Global hotspots of species richness are not congruent with endemism or threat. Nature 436, 1016–1019. https://doi.org/10.1038/nature03850 (2005).
    ADS  CAS  Article  PubMed  Google Scholar 

    38.
    Stark, G., Pincheira-Donoso, D. & Meiri, S. No evidence for the ‘rate-of-living’ theory across the tetrapod tree of life. Glob. Ecol. Biogeogr. 29, 857–884. https://doi.org/10.1111/geb.13069 (2020).
    Article  Google Scholar 

    39.
    Fletcher, R. & Fortin, M. Spatial Ecology and Conservation Modeling (Springer, Berlin, 2018).
    Google Scholar 

    40.
    Zhao, L., Li, J., Liu, H. & Qin, H. Distribution, congruence and hotspots of higher plants in China. Sci. Rep. 6, 19080. https://doi.org/10.1038/srep19080 (2016).
    ADS  CAS  Article  PubMed  PubMed Central  Google Scholar 

    41.
    Soberón, J. & Peterson, T. Biodiversity informatics: managing and applying primary biodiversity data. Philos. Trans. R. Soc. Lond. B Biol. Sci. 359, 689–698. https://doi.org/10.1098/rstb.2003.1439 (2004).
    Article  PubMed  PubMed Central  Google Scholar 

    42.
    Neves, I. Q., da LuzMathias, M. & Bastos-Silveira, C. Mapping knowledge gaps of Mozambique’s terrestrial mammals. Sci. Rep. 9, 1–14. https://doi.org/10.1038/s41598-019-54590-4 (2019).
    CAS  Article  Google Scholar 

    43.
    Soriano, A. in Ecosystems of the world 8A. Natural grasslands. Introduction and Western Hemisphere (ed R Coupland) 367–407 (Elsevier: Amsterdam, 1991).

    44.
    Andrade, B. O. et al. Vascular plant species richness and distribution in the Río de la Plata grasslands. Bot. J. Linn. Soc. 188, 6. https://doi.org/10.1093/botlinnean/boy063 (2018).
    Article  Google Scholar 

    45.
    Grela, I. Geografía florística de las especies arbóreas de Uruguay: propuesta para la delimitación de dendrofloras, Universidad de la República. Facultad de Ciencias – PEDECIBA, (2004).

    46.
    Arballo, E. & Cravino, J. Aves del Uruguay, Manual Ornitológico. Editorial Hemisferio Sur, Montevideo 1 (1999).

    47.
    González, E. M. & Martínez-Lanfranco, J. A. in Mamíferos de Uruguay. Guía de campo e introducción a su estudio y conservación 321–327 (Banda Oriental, MNHN y Vida Silvestre Uruguay, 2010).

    48.
    Pincheira-Donoso, D. The untold story on the ecological and phylogenetic complexity of the Uruguayan reptile fauna. Zootaxa 2354, 67–68. https://doi.org/10.11646/zootaxa.2354.1.6 (2010).
    Article  Google Scholar 

    49.
    Núñez, D., Maneyro, R., Langone, J. & de Sa, R. O. Distribución geográfica de la fauna de anfibios del Uruguay. Smithsonian Herpetol. Inf. Serv. https://doi.org/10.5479/si.23317515.134.1 (2004).
    Article  Google Scholar 

    50.
    Grattarola, F. & Rodríguez-Tricot, L. Mammals of Paso Centurión, an area with relicts of Atlantic Forest in Uruguay. Neotrop. Biol. Conserv. 15, 267–283. https://doi.org/10.3897/neotropical.15.e53062 (2020).
    Article  Google Scholar 

    51.
    SISNAP. SNAP Information System. http://www.snap.gub.uy/sisnap (2020).

    52.
    Soutullo, A. & Gudynas, E. How effective is the MERCOSUR’s network of protected areas in representing South America’s ecoregions?. Oryx 40, 112–116. https://doi.org/10.1017/S0030605306000020 (2006).
    Article  Google Scholar 

    53.
    Baldi, G. et al. Nature representation in South American protected areas: country contrasts and conservation priorities. PeerJ 7, e7155. https://doi.org/10.7717/peerj.7155 (2019).
    Article  PubMed  PubMed Central  Google Scholar 

    54.
    Brazeiro, A. Eco-regiones de Uruguay: biodiversidad, presiones y conservación : aportes a la Estrategia Nacional de Biodiversidad. (Facultad de Ciencias, UDELAR, 2015).

    55.
    Canavero, A. et al. Amphibian diversity of Uruguay: Background knowledge, inventory completeness and sampling coverage. Boletín de la Sociedad Zoológica de Uruguay 19, 2–19 (2010).
    Google Scholar 

    56.
    Carreira, S. et al. Diversity of reptiles of Uruguay: knowledge and information gaps. Boletín de la Sociedad Zoológica de Uruguay 21, 9–29 (2012).
    Google Scholar 

    57.
    Soutullo, A., Clavijo, C. & Martínez-Lanfranco, J. Especies prioritarias para la conservación en Uruguay. Vertebrados, moluscos continentales y plantas vasculares. (SNAP/DINAMA/MVOTMA and DICYT/MEC, 2013).

    58.
    Grattarola, F. et al. Biodiversidata: An open-access biodiversity database for Uruguay. Biodivers. Data J. https://doi.org/10.3897/BDJ.7.e36226 (2019).
    Article  PubMed  PubMed Central  Google Scholar 

    59.
    Grattarola, F. et al. Biodiversidata: A novel dataset for the vascular plant species diversity in Uruguay. Biodivers. Data J. https://doi.org/10.3897/BDJ.8.e56850 (2020).
    Article  PubMed  PubMed Central  Google Scholar 

    60.
    Luck, G. W. A review of the relationships between human population density and biodiversity. Biol. Rev. 82, 607–645. https://doi.org/10.1111/j.1469-185X.2007.00028.x (2007).
    Article  PubMed  Google Scholar 

    61.
    Luck, G. W. & Smallbone, L. T. in Urban Ecology Ecological Reviews (ed Kevin J. Gaston) 88–119 (Cambridge University Press, Cambridge, 2010).

    62.
    Pardo, I. et al. Spatial congruence between taxonomic, phylogenetic and functional hotspots: true pattern or methodological artefact?. Divers. Distrib. 23, 209–220. https://doi.org/10.1111/ddi.12511 (2017).
    Article  Google Scholar 

    63.
    Peterson, A. T., Asase, A., Canhos, D. A. L., de Souza, S. & Wieczorek, J. Data leakage and loss in biodiversity informatics. Biodivers. Data J. https://doi.org/10.3897/BDJ.6.e26826 (2018).
    Article  PubMed  PubMed Central  Google Scholar 

    64.
    Lamoreux, J. F. et al. Global tests of biodiversity concordance and the importance of endemism. Nature 440, 212–214. https://doi.org/10.1038/nature04291 (2006).
    ADS  CAS  Article  PubMed  Google Scholar 

    65.
    Feng, J.-M., Zhang, Z. & Nan, R.-Y. Non-congruence among hotspots based on three common diversity measures in Yunnan, south-west China. Plant Ecol. Divers. 4, 353–361. https://doi.org/10.1080/17550874.2012.697204 (2011).
    Article  Google Scholar 

    66.
    Westgate, M. J., Barton, P. S., Lane, P. W. & Lindenmayer, D. B. Global meta-analysis reveals low consistency of biodiversity congruence relationships. Nat. Commun. 5, 3899. https://doi.org/10.1038/ncomms4899 (2014).
    ADS  CAS  Article  PubMed  Google Scholar 

    67.
    Xu, H. et al. Biodiversity congruence and conservation strategies: a national test. Bioscience 58, 632–639. https://doi.org/10.1641/b580710 (2008).
    Article  Google Scholar 

    68.
    Brazeiro, A. et al. Prioridades Geográficas para la Conservación de la Biodiversidad Terrestre (Resumen Ejecutivo) (Facultad de Ciencias, Universidad de la República, Montevideo, Montevideo, 2008).
    Google Scholar 

    69.
    Oliveira, U. et al. The strong influence of collection bias on biodiversity knowledge shortfalls of Brazilian terrestrial biodiversity. Divers. Distrib. 22, 1232–1244. https://doi.org/10.1111/ddi.12489 (2016).
    Article  Google Scholar 

    70.
    Hurlbert, A. H. & Jetz, W. Species richness, hotspots, and the scale dependence of range maps in ecology and conservation. Proc. Natl. Acad. Sci. 104, 13384–13389. https://doi.org/10.1073/pnas.0704469104 (2007).
    ADS  CAS  Article  PubMed  Google Scholar 

    71.
    Boakes, E. H., Fuller, R. A., McGowan, P. J. K. & Mace, G. M. Uncertainty in identifying local extinctions: the distribution of missing data and its effects on biodiversity measures. Biol. Lett. https://doi.org/10.1098/rsbl.2015.0824 (2016).
    Article  PubMed  PubMed Central  Google Scholar 

    72.
    Stropp, J. et al. Mapping ignorance: 300 years of collecting flowering plants in Africa. Glob. Ecol. Biogeogr. 25, 1085–1096. https://doi.org/10.1111/geb.12468 (2016).
    Article  Google Scholar 

    73.
    Di Minin, E. & Toivonen, T. Global protected area expansion: creating more than paper parks. Bioscience 65, 637–638. https://doi.org/10.1093/biosci/biv064 (2015).
    Article  PubMed  PubMed Central  Google Scholar 

    74.
    Guisan, A. et al. Predicting species distributions for conservation decisions. Ecol. Lett. 16, 1424–1435. https://doi.org/10.1111/ele.12189 (2013).
    Article  PubMed  PubMed Central  Google Scholar 

    75.
    Ahrends, A. et al. Funding begets biodiversity. Divers. Distrib. 17, 191–200. https://doi.org/10.1111/j.1472-4642.2010.00737.x (2011).
    Article  Google Scholar 

    76.
    Hochkirch, A. et al. A strategy for the next decade to address data deficiency in neglected biodiversity. Conserv. Biol. https://doi.org/10.1111/cobi.13589 (2020).
    Article  PubMed  Google Scholar 

    77.
    Cabrera, M. R. & Carreira, S. A new, but probably extinct, species of Cnemidophorus (Squamata, Teiidae) from Uruguay. Herpetol. J. 19, 97–105 (2009).
    Google Scholar 

    78.
    Verrastro, L., Maneyro, R., Da Silva, C. M. & Farias, I. A new species of lizard of the L. wiegmannii group (Iguania: Liolaemidae) from the Uruguayan Savanna. Zootaxa 4294, 443–461. https://doi.org/10.11646/zootaxa.4294.4.4 (2017).
    Article  Google Scholar 

    79.
    Maneyro, R., Arrieta, D. & de Sá, R. O. A new toad (Anura: Bufonidae) from Uruguay. J. Herpetol. 38, 161–165. https://doi.org/10.1670/54-03A (2004).
    Article  Google Scholar 

    80.
    Maneyro, R., Naya, D. E. & Baldo, D. A new species of Melanophryniscus (Anura, Bufonidae) from Uruguay. Iheringia. Série Zoologia 98, 189–192. https://doi.org/10.1590/S0073-47212008000200003 (2008).
    Article  Google Scholar 

    81.
    Rosset, S. D. New Species of Odontophrynus Reinhardt and Lütken 1862 (Anura: Neobatrachia) from Brazil and Uruguay. J. Herpetol. 42, 134–144. https://doi.org/10.1670/07-088R1.1 (2008).
    Article  Google Scholar 

    82.
    Grattarola, F. et al. Primer registro de yaguarundí (Puma yagouaroundi) (Mammalia: Carnivora: Felidae) en Uruguay, con comentarios sobre monitoreo participativo. Boletín de la Sociedad Zoológica del Uruguay 25, 85–91 (2016).
    Google Scholar 

    83.
    Prigioni, C. M., Villalba, J. S., Sappa, A. & González, J. C. Confirmación de la presencia del mono aullador negro (Alouatta caraya) (Mammalia, Primates, Atelidae) en el Uruguay. Acta Zoológica Platense 1 (2018).

    84.
    Canavero, A., Naya, D. & Maneyro, R. Leptodactylus furnarius Sazima & Bokermann, 1978 (Anura: leptodactylidae). Cuadernos de Herpetología 15, 89 (2001).
    Google Scholar 

    85.
    Kwet, A. et al. First record of Hyla albopunctata Spix, 1824 (Anura: Hylidae) in Uruguay, with comments on the advertisement call. Boletín de la Asociación Herpetológica Española 13, 15–19 (2002).
    Google Scholar 

    86.
    Maneyro, R. & Beheregaray, M. First record of Physalaemus cuvieri Fitzinger, 1826 (Anura, Leiuperidae) in Uruguay, with comments on the anuran fauna along the borderline Uruguay-Brazil. Boletín de la Sociedad Zoológica del Uruguay 16, 36–41 (2007).
    Google Scholar 

    87.
    Azpiroz, A. B. & Menéndez, J. L. Three new species and novel distributional data for birds in Uruguay. Bull. Br. Ornithol. Club 128, 38–56 (2008).
    Google Scholar 

    88.
    Hernández, D. et al. Confirmación de la presencia del Tucán Grande Ramphastos toco (Piciformes: Ramphastidae) en Uruguay. Boletín de la Sociedad Zoológica del Uruguay 18, 35–38 (2009).
    Google Scholar 

    89.
    Rodríguez-Cajarville, M., Arballo, E. & Gambarotta, J. First documented records of Eastern Kingbird, Tyrannus tyrannus Linnaeus, 1758 (Aves: Tyrannidae) in Uruguay. Check List 13, 169–172. https://doi.org/10.15560/13.4.169 (2017).
    Article  Google Scholar 

    90.
    Meyer, C., Kreft, H., Guralnick, R. & Jetz, W. Global priorities for an effective information basis of biodiversity distributions. Nat. Commun. 6, 8221. https://doi.org/10.1038/ncomms9221 (2015).
    ADS  Article  PubMed  PubMed Central  Google Scholar 

    91.
    Sousa-Baena, M. S., Garcia, L. C. & Peterson, A. T. Completeness of digital accessible knowledge of the plants of Brazil and priorities for survey and inventory. Divers. Distrib. 20, 369–381. https://doi.org/10.1111/ddi.12136 (2014).
    Article  Google Scholar 

    92.
    Faith, D. et al. Bridging the biodiversity data gaps: recommendations to meet users’ data needs. Biodivers. Inf. https://doi.org/10.17161/bi.v8i2.4126 (2013).
    Article  Google Scholar 

    93.
    Grattarola, F. & Pincheira-Donoso, D. Biodiversidata: a collaborative initiative towards open data availability in Uruguay. Biodivers. Inf. Sci. Stand. 3, e37715. https://doi.org/10.3897/biss.3.37715 (2019).
    Article  Google Scholar 

    94.
    Grattarola, F. & Pincheira-Donoso, D. Data-sharing en Uruguay, la visión de los colectores y usuarios de datos. Boletín de la Sociedad Zoológica del Uruguay 28, 1–14. https://doi.org/10.26462/28.1.1 (2019).
    Article  Google Scholar 

    95.
    Griffin, E. in Data Science Landscape. Studies in Big Data Vol. 38 (eds U. Munshi & N. Verma) 183–198 (Springer, 2018).

    96.
    Freeman, B. & Peterson, A. T. Completeness of digital accessible knowledge of the birds of western Africa: priorities for survey. Condor https://doi.org/10.1093/condor/duz035 (2019).
    Article  Google Scholar 

    97.
    Amano, T., Lamming, J. D. L. & Sutherland, W. J. Spatial gaps in blobal biodiversity information and the role of citizen science. Bioscience 66, 393–400. https://doi.org/10.1093/biosci/biw022 (2016).
    Article  Google Scholar 

    98.
    Chandler, M. et al. Contribution of citizen science towards international biodiversity monitoring. Biol. Conserv. 213, 280–294. https://doi.org/10.1016/j.biocon.2016.09.004 (2017).
    Article  Google Scholar 

    99.
    Grattarola, F. et al. Biodiversidata: An open-access biodiversity database for Uruguay. Zenodo https://doi.org/10.5281/zenodo.3685897 (2019).

    100.
    Grattarola, F. et al. Tetrápodos de Uruguay. Occurrence dataset. GBIF https://doi.org/10.15468/ozcrpu (2020).
    Article  Google Scholar 

    101.
    IUCN. The IUCN Red List of Threatened Species. http://www.iucnredlist.org (2020).

    102.
    Carreira, S. & Maneyro, R. Libro Rojo de los Anfibios y Reptiles del Uruguay. Biología y conservación de los Anfibios y Reptiles en peligro de extinción a nivel nacional. (DINAMA, 2019).

    103.
    Azpiroz, A. B., Jiménez, S. & Alfaro, M. Libro Rojo de las Aves del Uruguay. Biología y conservación de las aves en peligro de extinción a nivel nacional Categorías “Extinto a Nivel Regional”, “En Peligro Crítico” y “En Peligro”. (DINAMA & DINARA, 2017).

    104.
    Dale, M. R. & Fortin, M.-J. Spatial Analysis: A Guide for Ecologists (Cambridge University Press, Cambridge, 2014).
    Google Scholar 

    105.
    Grattarola, F. GitHub repository https://github.com/bienflorencia/Multiple-forms-of-hotspots-of-tetrapod-biodiversity (2020).

    106.
    Dutilleul, P., Clifford, P., Richardson, S. & Hemon, D. Modifying the t test for assessing the correlation between two spatial processes. Biometrics 49, 305–314. https://doi.org/10.2307/2532625 (1993).
    Article  Google Scholar 

    107.
    Vallejos, R., Osorio, F. & Bevilacqua, M. Spatial Relationships Between Two Georeferenced Variables: with Applications in R (Springer, Berlin, 2018).
    Google Scholar 

    108.
    Chao, A. et al. Rarefaction and extrapolation with Hill numbers: a framework for sampling and estimation in species diversity studies. Ecol. Monogr. 84, 45–67. https://doi.org/10.1890/13-0133.1 (2014).
    Article  Google Scholar 

    109.
    Chao, A. et al. Quantifying sample completeness and comparing diversities among assemblages. Ecol. Res. 35, 292–314. https://doi.org/10.1111/1440-1703.12102 (2020).
    Article  Google Scholar 

    110.
    Hsieh, T. C., Ma, K. H. & Chao, A. iNEXT: an R package for rarefaction and extrapolation of species diversity (Hill numbers). Methods Ecol. Evol. 7, 1451–1456. https://doi.org/10.1111/2041-210x.12613 (2016).
    Article  Google Scholar 

    111.
    Kusumoto, B. et al. Global distribution of coral diversity: biodiversity knowledge gradients related to spatial resolution. Ecol. Res. 35, 315–326. https://doi.org/10.1111/1440-1703.12096 (2020).
    Article  Google Scholar 

    112.
    Yang, W., Ma, K. & Kreft, H. Geographical sampling bias in a large distributional database and its effects on species richness–environment models. J. Biogeogr. 40, 1415–1426. https://doi.org/10.1111/jbi.12108 (2013).
    Article  Google Scholar 

    113.
    Tittensor, D. P. et al. Global patterns and predictors of marine biodiversity across taxa. Nature 466, 1098–1101. https://doi.org/10.1038/nature09329 (2010).
    ADS  CAS  Article  PubMed  Google Scholar 

    114.
    Gotelli, N. J. & Colwell, R. K. Quantifying biodiversity: procedures and pitfalls in the measurement and comparison of species richness. Ecol. Lett. 4, 379–391. https://doi.org/10.1046/j.1461-0248.2001.00230.x (2001).
    Article  Google Scholar 

    115.
    Oksanen, J. et al. Package ‘vegan’. Community ecology package, version 2 (2013). More

  • in

    Temporal changes in reproductive success and optimal breeding decisions in a long-distance migratory bird

    1.
    Siikamäki, P. Limitation of reproductive success by food availability and timing of breeding in pied flycatchers. Ecology 79, 1789–1796. https://doi.org/10.1890/0012-9658(1998)079[1789:LORSBF]2.0.CO;2 (1998).
    Article  Google Scholar 
    2.
    Post, E., Bøving, P. S., Pedersen, C. & MacArthur, M. A. Synchrony between caribou calving and plant phenology in depredated and non-depredated populations. Can. J. Zool. 81, 1709–1714. https://doi.org/10.1139/z03-172 (2003).
    Article  Google Scholar 

    3.
    Both, C. & Visser, M. E. The effect of climate change on the correlation between avian life-history traits. Glob. Change Biol. 11, 1606–1613. https://doi.org/10.1111/j.1365-2486.2005.01038.x (2005).
    ADS  Article  Google Scholar 

    4.
    Visser, M. E., Holleman, L. J. M. & Gienapp, P. Shifts in caterpillar biomass phenology due to climate change and its impact on the breeding biology of an insectivorous bird. Oecologia 147, 164–172. https://doi.org/10.1007/s00442-005-0299-6 (2006).
    ADS  Article  PubMed  Google Scholar 

    5.
    Reed, T. E., Jenouvrier, S. & Visser, M. E. Phenological mismatch strongly affects individual fitness but not population demography in a woodland passerine. J. Anim. Ecol. 82, 131–144. https://doi.org/10.1111/j.1365-2656.2012.02020.x (2013).
    Article  PubMed  Google Scholar 

    6.
    Rowe, L., Ludwig, D. & Schluter, D. Time condition and the seasonal decline of avian clutch size. Am. Nat. 143, 698–722. https://doi.org/10.1086/285627 (1994).
    Article  Google Scholar 

    7.
    Bêty, J., Gauthier, G. & Giroux, J.-F. Body condition, migration and timing of reproduction in snow geese: a test of the condition-dependent model of optimal clutch size. Am. Nat. 162, 110–121. https://doi.org/10.1086/375680 (2003).
    Article  PubMed  Google Scholar 

    8.
    Drent, R. H., Fox, A. D. & Stahl, J. Travelling to breed. J. Ornithol. 147, 122–134. https://doi.org/10.1007/s10336-006-0066-4 (2006).
    Article  Google Scholar 

    9.
    Both, C. et al. Avian population consequences of climate change are most severe for long-distance migrants in seasonal habitats. Proc. R. Soc. B. 277, 1259–1266. https://doi.org/10.1098/rspb.2009.1525 (2010).
    Article  PubMed  Google Scholar 

    10.
    Verhulst, S. & Nilsson, J. -Å. The timing of birds’ breeding seasons: a review of experiments that manipulated timing of breeding. Phil. Trans. R. Soc. B. 363, 399–410. https://doi.org/10.1098/rstb.2007.2146 (2008).
    Article  PubMed  Google Scholar 

    11.
    Descamps, S., Bêty, J., Love, O. P. & Gilchrist, H. G. Individual optimization of reproduction in a long-lived migratory bird: a test of the condition-dependent model of laying date and clutch size. Funct. Ecol. 25, 671–681. https://doi.org/10.1111/j.1365-2435.2010.01824.x (2011).
    Article  Google Scholar 

    12.
    Lepage, D., Gauthier, G. & Menu, S. Reproductive consequences of egg-laying decisions in snow geese. J. Anim. Ecol. 69, 414–427. https://doi.org/10.1046/j.1365-2656.2000.00404.x (2000).
    Article  Google Scholar 

    13.
    Jean-Gagnon, F. et al. The impact of sea ice conditions on breeding decisions is modulated by body condition in an arctic partial capital breeder. Oecologia 186, 1–10. https://doi.org/10.1007/s00442-017-4002-5 (2018).
    ADS  Article  PubMed  Google Scholar 

    14.
    Durant, J. M., Hjermann, D. O., Ottersen, G. & Stenseth, N. C. Climate and the match or mismatch between predator requirements and resource availability. Clim. Res. 33, 271–283. https://doi.org/10.3354/cr033271 (2007).
    Article  Google Scholar 

    15.
    Both, C., Van Asch, M., Bijlsma, R. G., Van Den Burg, A. B. & Visser, M. E. Climate change and unequal phenological changes across four trophic levels: constraints or adaptations?. J. Anim. Ecol. 78, 73–83. https://doi.org/10.1111/j.1365-2656.2008.01458.x (2009).
    Article  PubMed  Google Scholar 

    16.
    Ross, M. V., Alisauskas, R. T., Douglas, D. C. & Kellett, D. K. Decadal declines in avian herbivore reproduction: density-dependent nutrition and phenological mismatch in the Arctic. Ecology 98, 1869–1883. https://doi.org/10.1002/ecy.1856 (2017).
    Article  PubMed  Google Scholar 

    17.
    Charmantier, A. et al. Adaptive phenotypic plasticity in response to climate change in a wild bird population. Science 320, 800–803. https://doi.org/10.1126/science.1157174 (2008).
    ADS  CAS  Article  PubMed  Google Scholar 

    18.
    Gienapp, P., Teplitsky, C., Alho, J. S., Mills, J. A. & Merilä, J. Climate change and evolution: disentangling environmental and genetic responses. Mol. Ecol. 17, 167–178. https://doi.org/10.1111/j.1365-294X.2007.03413.x (2008).
    CAS  Article  PubMed  Google Scholar 

    19.
    Visser, M. E., van Noordwijk, A. J., Tinbergen, J. M. & Lessells, C. M. Warmer springs lead to mistimed reproduction in great tits (Parus major). Proc. R. Soc. Lond. B. 265, 1867–1870. https://doi.org/10.1098/rspb.1998.0514 (1998).
    Article  Google Scholar 

    20.
    Both, C. & Visser, M. E. Adjustment to climate change is constrained by arrival date in a long-distance migrant bird. Nature 411, 296–298. https://doi.org/10.1038/35077063 (2001).
    ADS  CAS  Article  PubMed  Google Scholar 

    21.
    Ross, M. V., Alisauskas, R. T., Douglas, D. C., Kellett, D. K. & Drake, K. L. Density-dependent and phenological mismatch effects on growth and survival in lesser snow and Ross’s goslings. J. Avian Biol. 49, e01748. https://doi.org/10.1111/jav.01748 (2018).
    Article  Google Scholar 

    22.
    Gauthier, G. et al. Long-term monitoring at multiple trophic levels suggests heterogeneity in responses to climate change in the Canadian Arctic tundra. Phil. Trans. R. Soc. B. 368, 20120482–20120482. https://doi.org/10.1098/rstb.2012.0482 (2013).
    Article  PubMed  Google Scholar 

    23.
    Lepage, D., Gauthier, G. & Reed, A. Seasonal variation in growth of greater snow goose goslings: the role of food supply. Oecologia 114, 226–235. https://doi.org/10.1007/s004420050440 (1998).
    ADS  Article  PubMed  Google Scholar 

    24.
    Doiron, M., Gauthier, G. & Lévesque, E. Trophic mismatch and its effects on the growth of young in an Arctic herbivore. Glob. Change Biol. 21, 4364–4376. https://doi.org/10.1111/gcb.13057 (2015).
    ADS  Article  Google Scholar 

    25.
    Reséndiz-Infante, C., Gauthier, G. & Souchay, G. Consequences of a changing environment on the breeding phenology and reproductive success components in a long-distance migratory bird. Pop. Ecol. 62, 284–296. https://doi.org/10.1002/1438-390X.12046 (2020).
    Article  Google Scholar 

    26.
    Lecomte, N., Careau, V., Gauthier, G. & Giroux, J.-F. Predator behaviour and predation risk in the heterogeneous arctic environment. J. Anim. Ecol. 77, 439–447. https://doi.org/10.1111/j.1365-2656.2008.01354.x (2008).
    Article  PubMed  Google Scholar 

    27.
    Findlay, C. & Cooke, F. Synchrony in the lesser snow goose (Anser caerulescens caerulescens) II. The adaptive value of reproductive synchrony. Evolution 36, 786–799. https://doi.org/10.2307/2407892 (1982).
    Article  PubMed  Google Scholar 

    28.
    Bêty, J., Gauthier, G., Giroux, J.-F. & Korpimäki, E. Are goose nesting success and lemming cycles linked? Interplay between nest density and predators. Oikos 93, 388–400. https://doi.org/10.1034/j.1600-0706.2001.930304.x (2001).
    Article  Google Scholar 

    29.
    Dickey, M.-H., Gauthier, G. & Cadieux, M.-C. Climatic effects on the breeding phenology and reproductive success of an arctic-nesting goose species. Glob. Change Biol. 14, 1973–1985. https://doi.org/10.1111/j.1365-2486.2008.01622.x (2008).
    ADS  Article  Google Scholar 

    30.
    Juhasz, C.-C., Shipley, B., Gauthier, G., Berteaux, D. & Lecomte, N. Direct and indirect effects of regional and local climatic factors on trophic interactions in the Arctic tundra. J. Anim. Ecol. 89, 704–715. https://doi.org/10.1111/1365-2656.13104 (2019).
    Article  PubMed  Google Scholar 

    31.
    Bêty, J., Gauthier, G., Korpimaki, E. & Giroux, J.-F. Shared predators and indirect trophic interactions: lemming cycles and arctic-nesting geese. J. Anim. Ecol. 71, 88–98. https://doi.org/10.1046/j.0021-8790.2001.00581.x (2002).
    Article  Google Scholar 

    32.
    Iles, D. T., Rockwell, R. F. & Koons, D. N. Reproductive success of a keystone herbivore is more variable and responsive to climate in habitats with lower resource diversity. J. Anim. Ecol. 87, 1182–1191. https://doi.org/10.1111/1365-2656.12837 (2018).
    Article  PubMed  Google Scholar 

    33.
    Lohman, M. G. et al. Changes in behavior are unable to disrupt a trophic cascade involving a specialist herbivore and its food plant. Ecol. Evol. 9, 5281–5291. https://doi.org/10.1002/ece3.5118 (2019).
    Article  PubMed  PubMed Central  Google Scholar 

    34.
    Aubry, L. M. et al. Climate change, phenology, and habitat degradation: drivers of gosling body condition and juvenile survival in lesser snow geese. Glob. Change Biol. 19, 149–160. https://doi.org/10.1111/gcb.12013 (2013).
    ADS  Article  Google Scholar 

    35.
    Massé, H., Rochefort, L. & Gauthier, G. Carrying capacity of wetland habitats used by breeding greater snow geese. J. Wildl. Manage. 65, 271–281. https://doi.org/10.2307/3802906 (2001).
    Article  Google Scholar 

    36.
    Valéry, L., Cadieux, M.-C. & Gauthier, G. Spatial heterogeneity of primary production as both cause and consequence of foraging patterns of an expanding Greater Snow Goose colony. Ecoscience 17, 9–19. https://doi.org/10.2980/17-1-3279 (2010).
    Article  Google Scholar 

    37.
    Gienapp, P., Postma, E. & Visser, M. E. Why breeding time has not responded to selection for earlier breeding in a songbird population. Evolution 60, 2381–2388. https://doi.org/10.1111/j.0014-3820.2006.tb01872.x (2006).
    Article  PubMed  Google Scholar 

    38.
    Van Wijk, R. E. et al. Individually tracked geese follow peaks of temperature acceleration during spring migration. Oikos 121, 655–664. https://doi.org/10.1111/j.1600-0706.2011.20083.x (2012).
    Article  Google Scholar 

    39.
    Gauthier, G., Bêty, J. & Hobson, K. A. Are greater snow geese capital breeders? New evidence from a stable-isotope model. Ecology 84, 3250–3264. https://doi.org/10.1890/02-0613 (2003).
    Article  Google Scholar 

    40.
    Lameris, T. K. et al. Arctic geese tune migration to a warming climate but still suffer from a phenological mismatch. Curr. Biol. 28, 1–7. https://doi.org/10.1016/j.cub.2018.05.077 (2018).
    CAS  Article  Google Scholar 

    41.
    Shutler, D., Clark, R. G., Fehr, C. & Diamond, A. W. Time and recruitment costs as currencies in manipulation studies on the costs of reproduction. Ecology 87, 2938–2946. https://doi.org/10.1890/0012-9658(2006)87[2938:TARCAC]2.0.CO;2 (2006).
    Article  PubMed  Google Scholar 

    42.
    Rockwell, R. F., Cooch, E. G., Thompson, C. B. & Cooke, F. Age and reproductive success in female lesser snow geese: experience, senescence and the cost of philopatry. J. Anim. Ecol. 62, 323–333. https://doi.org/10.2307/5363 (1993).
    Article  Google Scholar 

    43.
    Souchay, G., Gauthier, G. & Pradel, R. To breed or not: a novel approach to estimate breeding propensity and potential trade-offs in an Arctic-nesting species. Ecology 95, 2745–2756. https://doi.org/10.1890/13-1277.1 (2014).
    Article  Google Scholar 

    44.
    Bêty, J., Giroux, J.-F. & Gauthier, G. Individual variation in timing of migration: causes and reproductive consequences in greater snow geese (Anser caerulescens atlanticus). Behav. Ecol. Sociobiol. 57, 1–8. https://doi.org/10.1007/s00265-004-0840-3 (2004).
    Article  Google Scholar 

    45.
    Gauthier, G., Giroux, J.-F., Reed, A., Béchet, A. & Bélanger, L. Interactions between land use habitat use and population increase in greater snow geese: what are the consequences for natural wetlands?. Glob. Change Biol. 11, 856–868. https://doi.org/10.1111/j.1365-2486.2005.00944.x (2005).
    ADS  Article  Google Scholar 

    46.
    Cooke, F., Rockwell, R. F. & Lank, D. B. The Snow Geese of La Perouse Bay. Natural Selection in the Wild (Oxford University Press, Oxford, 1995).
    Google Scholar 

    47.
    Reed, A., Hughes, R. J. & Boyd, H. Patterns of distribution and abundance of greater snow geese on Bylot Island Nunavut Canada 1983–1998. Wildfowl 53, 53–65 (2002).
    Google Scholar 

    48.
    Mainguy, J., Gauthier, G., Giroux, J.-F. & Bêty, J. Gosling growth and survival in relation to brood movements in greater snow geese (Chen caerulescens atlantica). Auk 123, 1077–1089. https://doi.org/10.2307/25150221 (2006).
    Article  Google Scholar 

    49.
    Menu, S., Gauthier, G. & Reed, A. Survival of juvenile greater snow geese immediately after banding. J. Field Ornithol. 72, 282–290. https://doi.org/10.1648/0273-8570-72.2.282 (2001).
    Article  Google Scholar 

    50.
    Schubert, C. A. & Cooke, F. Egg-laying intervals in the lesser snow goose. Wilson Bull. 105, 414–426 (1993).
    Google Scholar  More

  • in

    Publisher Correction: Social value shift in favour of biodiversity conservation in the United States

    Affiliations

    Human Dimensions of Natural Resources Department, Colorado State University, Fort Collins, CO, USA
    Michael J. Manfredo, Tara L. Teel & Richard E. W. Berl

    School of Environment and Natural Resources, The Ohio State University, Columbus, OH, USA
    Jeremy T. Bruskotter

    Department of Psychology, University of Michigan, Ann Arbor, MI, USA
    Shinobu Kitayama

    Authors
    Michael J. Manfredo

    Tara L. Teel

    Richard E. W. Berl

    Jeremy T. Bruskotter

    Shinobu Kitayama

    Corresponding author
    Correspondence to Michael J. Manfredo. More

  • in

    Soil fungal and bacterial communities in southern boreal forests of the Greater Khingan Mountains and their relationship with soil properties

    1.
    Gattinger, A., Palojärvi, A. & Schloter, M. Soil microbial communities and related Functions. in Perspectives for agroecosystem management (eds. Schröder P., Pfadenhauer J. & Munch J. C.) 279–292 (Elsevier, 2008).
    2.
    Renella, G. et al. Hydrolase activity, microbial biomass and community structure in long-term Cd-contaminated soils. Soil Biol. Biochem. 36, 443–451 (2004).
    CAS  Article  Google Scholar 

    3.
    Ros, M., Pascual, J. A., Garcia, C., Hernandez, M. T. & Insam, H. Hydrolase activities, microbial biomass and bacterial community in a soil after long-term amendment with different composts. Soil Biol. Biochem. 38, 3443–3452 (2006).
    CAS  Article  Google Scholar 

    4.
    Krishnan, A., Alias, S. A., Wong, C. M. V. L., Pang, K. & Convey, P. Extracellular hydrolase enzyme production by soil fungi from King George Island, Antarctica. Polar Biol. 34, 1535–1542 (2011).
    Article  Google Scholar 

    5.
    Bronson, K. F. et al. Carbon and nitrogen pools of southern high plains cropland and grassland soils. Soil Sci. Soc. Am. J. 68, 1695 (2004).
    ADS  CAS  Article  Google Scholar 

    6.
    Liu, S. et al. Estimation of plot-level soil carbon stocks in China’s forests using intensive soil sampling. Geoderma 348, 107–114 (2019).
    ADS  CAS  Article  Google Scholar 

    7.
    Kapusta, P., Sobczyk, A., Rożen, A. & Weiner, J. Species diversity and spatial distribution of enchytraeid communities in forest soils: effects of habitat characteristics and heavy metal contamination. Appl. Soil Ecol. 23, 187–198 (2003).
    Article  Google Scholar 

    8.
    Romanowicz, K. J. et al. Active microorganisms in forest soils differ from the total community yet are shaped by the same environmental factors: the influence of pH and soil moisture. FEMS Microbiol. Ecol. 92, w149 (2016).
    Article  CAS  Google Scholar 

    9.
    Ilstedt, U. & Singh, S. Nitrogen and phosphorus limitations of microbial respiration in a tropical phosphorus-fixing acrisol (ultisol) compared with organic compost. Soil Biol. Biochem. 37, 1407–1410 (2005).
    CAS  Article  Google Scholar 

    10.
    Liu, L., Gundersen, P., Zhang, T. & Mo, J. Effects of phosphorus addition on soil microbial biomass and community composition in three forest types in tropical China. Soil Biol. Biochem. 44, 31–38 (2012).
    Article  CAS  Google Scholar 

    11.
    Turner, B. L. & Wright, S. J. The response of microbial biomass and hydrolytic enzymes to a decade of nitrogen, phosphorus, and potassium addition in a lowland tropical rain forest. Biogeochemistry 117, 115–130 (2014).
    CAS  Article  Google Scholar 

    12.
    Allison, S. D., Hanson, C. A. & Treseder, K. K. Nitrogen fertilization reduces diversity and alters community structure of active fungi in boreal ecosystems. Soil Biol. Biochem. 39, 1878–1887 (2007).
    CAS  Article  Google Scholar 

    13.
    Gadd, G. M. Microorganisms in soils: roles in genesis and functions. Soil Biology. 3, 325–356 (2005).
    CAS  Article  Google Scholar 

    14.
    Johnson, M. J., Lee, K. Y. & Scow, K. M. DNA fingerprinting reveals links among agricultural crops, soil properties, and the composition of soil microbial communities. Geoderma 114, 279–303 (2003).
    ADS  Article  Google Scholar 

    15.
    Pietri, J. A. & Brookes, P. C. Relationships between soil pH and microbial properties in a UK arable soil. Soil Biol. Biochem. 40, 1856–1861 (2008).
    Article  CAS  Google Scholar 

    16.
    Anthony, M. A., Crowther, T. W., Maynard, D. S., van den Hoogen, J. & Averill, C. Distinct assembly processes and microbial communities constrain soil organic carbon formation. One Earth. 2, 349–360 (2020).
    Article  Google Scholar 

    17.
    Schulte-Uebbing, L. & de Vries, W. Global-scale impacts of nitrogen deposition on tree carbon sequestration in tropical, temperate, and boreal forests: A meta-analysis. Global Change Biol. 24, e416–e431 (2018).
    Article  Google Scholar 

    18.
    Juday, G. P. Taiga. (2019) Available at: https://www.britannica.com/science/taiga (Accessed: October 15, 2020.

    19.
    Hu, L. et al. Spatiotemporal dynamics in vegetation GPP over the Great Khingan Mountains using GLASS products from 1982 to 2015. Remote Sens. Basel. 10, 488 (2018).
    ADS  Article  Google Scholar 

    20.
    Jiang, H., Apps, M. J., Peng, C., Zhang, Y. & Liu, J. Modelling the influence of harvesting on Chinese boreal forest carbon dynamics. Forest Ecol. Manag. 169, 65–82 (2002).
    Article  Google Scholar 

    21.
    Tang, H. et al. Variability and climate change trend in vegetation phenology of recent decades in the Greater Khingan Mountain area, Northeastern China. Remote Sens.-Basel. 7, 11914–11932 (2015).

    22.
    Greene, D. F. et al. A review of the regeneration dynamics of North American boreal forest tree species. Can. J. Forest Res. 29, 824–839 (1999).
    ADS  Article  Google Scholar 

    23.
    Yuan, Z. Y. & Chen, H. Y. Fine root biomass, production, turnover rates, and nutrient contents in boreal forest ecosystems in relation to species, climate, fertility, and stand age: literature review and meta-analyses. Crit. Rev. Plant Sci. 29, 204–221 (2010).
    CAS  Article  Google Scholar 

    24.
    Sanderson, L. A., McLaughlin, J. A. & Antunes, P. M. The last great forest: a review of the status of invasive species in the North American boreal forest. Forestry 85, 329–340 (2012).
    Article  Google Scholar 

    25.
    Kreutzweiser, D. P., Hazlett, P. W. & Gunn, J. M. Logging impacts on the biogeochemistry of boreal forest soils and nutrient export to aquatic systems: a review. Environ. Rev. 16, 157–179 (2008).
    CAS  Article  Google Scholar 

    26.
    Dhar, A. et al. Plant community development following reclamation of oil sands mine sites in the boreal forest: a review. Environ. Rev. 26, 286–298 (2018).
    Article  Google Scholar 

    27.
    Simard, D. G., Fyles, J. W., Paré, D. & Nguyen, T. Impacts of clearcut harvesting and wildfire on soil nutrient status in the Quebec boreal forest. Can. J. Soil Sci. 81, 229–237 (2001).
    CAS  Article  Google Scholar 

    28.
    Ohtonen, R. & Väre, H. Vegetation composition determines microbial activities in a boreal forest soil. Microb. Ecol. 36, 328–335 (1998).
    CAS  PubMed  Article  Google Scholar 

    29.
    Nilsson, M., Wardle, D. A. & Dahlberg, A. Effects of plant litter species composition and diversity on the boreal forest plant-soil system. Oikos 86, 16–26 (1999).
    Article  Google Scholar 

    30.
    Dimitriu, P. A. & Grayston, S. J. Relationship between soil properties and patterns of bacterial β-diversity across reclaimed and natural boreal forest soils. Microb. Ecol. 59, 563–573 (2010).
    PubMed  Article  Google Scholar 

    31.
    Buckley, D. H. & Schmidt, T. M. Diversity and dynamics of microbial communities in soils from agro-ecosystems. Environ. Microbiol. 5, 441–452 (2003).
    PubMed  Article  Google Scholar 

    32.
    Jangid, K. Land-use history has a stronger impact on soil microbial community composition than aboveground vegetation and soil properties. Soil Biol. Biochem. 43, 2184–2193 (2011).
    CAS  Article  Google Scholar 

    33.
    Wal, A. V. D. et al. Fungal biomass development in a chronosequence of land abandonment. Soil Biol. Biochem. 38, 51–60 (2006).
    Article  CAS  Google Scholar 

    34.
    Fu, X. et al. Understory vegetation leads to changes in soil acidity and in microbial communities 27 years after reforestation. Sci. Total Environ. 502, 280–286 (2015).
    ADS  CAS  PubMed  Article  Google Scholar 

    35.
    Kalinina, O. et al. Self-restoration of post-agrogenic chernozems of Russia: soil development, carbon stocks, and dynamics of carbon pools. Geoderma 162, 196–206 (2011).
    ADS  CAS  Article  Google Scholar 

    36.
    Gao, Y. et al. Influence of forest type on dark-spored myxomycete community in subtropical forest soil, China. Soil Biol. Biochem. 138, 107606 (2019).
    CAS  Article  Google Scholar 

    37.
    Sheng, Y. et al. Broad-leaved forest types affect soil fungal community structure and soil organic carbon contents. MicrobiologyOpen. 8, e874 (2019).
    CAS  PubMed  PubMed Central  Article  Google Scholar 

    38.
    Vatani, L., Hosseini, S. M., Sarjaz, M. R. & Alavi, S. J. Tree species effects on albedo, soil carbon and nitrogen stocks in a temperate forest in Iran. Aus. J. For. Sci. 136, 283–310 (2019).
    Google Scholar 

    39.
    Bauhus, J., Paré, D. & Co Té, L. Effects of tree species, stand age and soil type on soil microbial biomass and its activity in a southern boreal forest. Soil Biol. Biochem. . 30, 1077–1089 (1998).

    40.
    Dukunde, A., Schneider, D., Schmidt, M., Veldkamp, E. & Daniel, R. Tree species shape soil bacterial community structure and function in temperate deciduous forests. Front. Microbiol. 10, 1–17 (2019).
    Article  Google Scholar 

    41.
    Tajik, S., Ayoubi, S., Khajehali, J. & Shataee, S. Effects of tree species composition on soil properties and invertebrates in a deciduous forest. Arab. J. Geosci. 12, 368 (2019).
    Article  CAS  Google Scholar 

    42.
    Stingl, U. & Giovannoni, S. J. Molecular diversity and ecology of microbial plankton. Nature 437, 343–348 (2005).
    ADS  PubMed  Article  CAS  Google Scholar 

    43.
    Danger, M., Daufresne, T., Lucas, F., Pissard, S. & Lacroix, G. Does Liebig’s law of the minimum scale up from species to communities?. Oikos 117, 1741–1751 (2008).
    Article  Google Scholar 

    44.
    Sakurai, M., Suzuki, K., Onodera, M., Shinano, T. & Osaki, M. Analysis of bacterial communities in soil by PCR–DGGE targeting protease genes. Soil Biol. Biochem. 39, 2777–2784 (2007).
    CAS  Article  Google Scholar 

    45.
    Wang, Y. et al. Carbon input manipulations affecting microbial carbon metabolism in temperate forest soils—a comparative study between broadleaf and coniferous plantations. Geoderma 355, 113914 (2019).
    ADS  CAS  Article  Google Scholar 

    46.
    Wan, X. et al. Soil C: N ratio is the major determinant of soil microbial community structure in subtropical coniferous and broadleaf forest plantations. Plant Soil. 387, 103–116 (2015).
    CAS  Article  Google Scholar 

    47.
    Amtmann, A., Troufflard, S. & Armengaud, P. The effect of potassium nutrition on pest and disease resistance in plants. Physiol. Plantarum. 133, 582–691 (2008).
    Article  CAS  Google Scholar 

    48.
    Pettigrew, W. T. Potassium influences on yield and quality production for maize, wheat, soybean and cotton. Physiol. Plantarum. 133, 670–681 (2008).
    CAS  Article  Google Scholar 

    49.
    Markewitz, D. & Richter, D. D. Long-term soil potassium availability from a Kanhapludult to an aggrading loblolly pine ecosystem. Forest Ecol. Manag. 130, 109–129 (2000).
    Article  Google Scholar 

    50.
    Tripler, C. E., Kaushal, S. S. & Likens, G. E. Patterns in potassium dynamics in forest ecosystems. Ecol. Lett. 9, 451–466 (2006).
    PubMed  Article  Google Scholar 

    51.
    Mori, T. et al. Testing potassium limitation on soil microbial activity in a sub-tropical forest. J. For. Res. 30, 2341–2347 (2019).
    CAS  Article  Google Scholar 

    52.
    Vuong, T. M. D., Zeng, J. Y. & Man, X. L. Spatial distribution andmonthly dynamics of soil carbon/nitrogen and hydrolases in Pinus sylvestris var. mongolica Litv. natural forest. Scientia Silvae Sinicae. 56, 40–47 (2020).

    53.
    Zeng, J. et al. An investigation into whether effect of tree species on soil microbial community is related with deciduous property or leaf shape. CATENA 195, 104699 (2020).
    Article  Google Scholar 

    54.
    Wu, Y. et al. Changes in the soil microbial community structure with latitude in eastern China, based on phospholipid fatty acid analysis. Appl. Soil Ecol. 43, 234–240 (2009).
    Article  Google Scholar 

    55.
    Washburn, C. & Arthur, M. A. Spatial variability in soil nutrient availability in an oak-pine forest: Potential effects of tree species. Can. J. For. Res. 33, 2321–2330 (2003).
    Article  Google Scholar 

    56.
    Azeez, J. O. Recycling organic waste in managed tropical forest ecosystems: effects of arboreal litter types on soil chemical properties in Abeokuta, southwestern Nigeria. J. For. Res. 30, 1903–1911 (2019).
    CAS  Article  Google Scholar 

    57.
    Ha, T. Effectiveness of the Vietnamese Good Agricultural Practice (VietGAP) on Plant Growth and Quality of Choy Sum (Brassica rapa var. parachinensis) in Northern Vietnam. Aceh International Journal of Science and Technology. 3, 80–87 (2014).

    58.
    Jia, Z. et al. The placental microbiome varies in association with low birth weight in full-term neonates. Nutrients 7, 6924–6937 (2015).
    Article  CAS  Google Scholar 

    59.
    Zhang, Y., Sui, B., Shen, H. & Ouyang, L. Mapping stocks of soil total nitrogen using remote sensing data: a comparison of random forest models with different predictors. Comput. Electron. Agric. 160, 23–30 (2019).
    Article  Google Scholar 

    60.
    Sun, H. et al. Soil organic carbon stabilization mechanisms in a subtropical mangrove and salt marsh ecosystems. Sci. Total Environ. 673, 502–510 (2019).
    ADS  CAS  PubMed  Article  Google Scholar 

    61.
    Ye, C. et al. Spatial and temporal dynamics of nutrients in riparian soils after nine years of operation of the Three Gorges Reservoir, China. Sci. Total Environ. 664, (2019).

    62.
    Li, J., Zhou, L. & Lin, W. Calla lily intercropping in rubber tree plantations changes the nutrient content, microbial abundance, and enzyme activity of both rhizosphere and non-rhizosphere soil and calla lily growth. Ind. Crop. Prod. (2019).

    63.
    Kandeler, E. & Gerber, H. Short-term assay of soil urease activity using colorimetric determination of ammonium. Biol. Fert. Soils. 6, 68–72 (1988).
    CAS  Article  Google Scholar 

    64.
    Ladd, J. N. & Butler, J. H. A. Short-term assays of soil proteolytic enzyme activities using proteins and dipeptide derivatives as substrates. Soil Biol. Biochem. 4, 19–30 (1972).
    CAS  Article  Google Scholar 

    65.
    Ross, D. J. & Roberts, H. S. Enzyme activities and oxygen uptakes of soils under pasture in temperature and rainfall sequences. Eur. J. Soil Sci. 21, 368–381 (1970).
    CAS  Article  Google Scholar 

    66.
    Sharma, N., Bhalla, T. C. & Bhatt, A. K. Partial purification and characterization of extracellular cellulase from a strain of Trichoderma viride isolated from forest soil. Folia Microbiol. 36, 353–359 (1991).
    CAS  Article  Google Scholar 

    67.
    Bolger, A. M., Lohse, M. & Usadel, B. Trimmomatic: a flexible trimmer for Illumina sequence data. Bioinformatics 30, 2114–2120 (2014).
    CAS  PubMed  PubMed Central  Article  Google Scholar 

    68.
    Magoč, T. & Salzberg, S. L. FLASH: fast length adjustment of short reads to improve genome assemblies. Bioinformatics 27, 2957–2963 (2011).
    PubMed  PubMed Central  Article  CAS  Google Scholar 

    69.
    Wang, Q., Garrity, G. M., Tiedje, J. M. & Cole, J. R. Naive Bayesian classifier for rapid assignment of rRNA sequences into the new bacterial taxonomy. Appl. Environ. Microb. 73, 5261–5267 (2007).
    CAS  Article  Google Scholar 

    70.
    Schloss, P. D. et al. Introducing Mothur: open-source, platform-independent, community-supported software for describing and comparing microbial communities. Appl. Environ. Microb. 75, 7537–7541 (2009).
    CAS  Article  Google Scholar 

    71.
    Chen, H. & Boutros, P. C. VennDiagram: a package for the generation of highly-customizable Venn and Euler diagrams in R. BMC Bioinformatics 12, 1–7 (2011).
    CAS  Article  Google Scholar 

    72.
    Wickham, H. Ggplot2: Elegant Graphics for Data Analysis (Springer, Berlin, 2016).
    Google Scholar 

    73.
    Oksanen, J. et al. Package “vegan”. Commun. Ecol. Package, Version 2, 1–295 (2013).
    Google Scholar 

    74.
    Box, J. F. Guinness, Gosset, Fisher, and small samples. Stat. Sci. 2, 45–52 (1987).
    MathSciNet  MATH  Article  Google Scholar 

    75.
    Holland, S. M. Principal Components Analysis (PCA) 30602–32501 (Department of Geology, University of Georgia, Athens, GA, 2008).
    Google Scholar 

    76.
    Vu, V. Q. ggbiplot: A ggplot2 based biplot. R package. 342, (2011).

    77.
    Nguyen, N. H. et al. FUNGuild: an open annotation tool for parsing fungal community datasets by ecological guild. Fungal Ecol. 20, 241–248 (2016).
    Article  Google Scholar 

    78.
    Langille, M. G. et al. Predictive functional profiling of microbial communities using 16S rRNA marker gene sequences. Nat. Biotechnol. 31, 814–821 (2013).
    CAS  PubMed  PubMed Central  Article  Google Scholar  More

  • in

    Morphological function of toe fringe in the sand lizard Phrynocephalus mystaceus

    1.
    Higham, T. E. The integration of locomotion and prey capture in vertebrates: morphology, behavior, and performance. Integr. Comp. Biol. 47, 82–95 (2007).
    PubMed  Article  Google Scholar 
    2.
    Ydenberg, R. C. & Dill, L. M. The economics of fleeing from predators. Adv. Stud. Behav. 16, 229–249 (1986).
    Article  Google Scholar 

    3.
    Cooper, W. E. Jr. & Frederick, W. G. Optimal flight initiation distance. J. Theor. Biol. 244, 59–67 (2007).
    MathSciNet  PubMed  MATH  Article  Google Scholar 

    4.
    Darwin, C. The Voyage of the Beagle (Doubleday and Co, New York, 1962).
    Google Scholar 

    5.
    Arnold, E. N. Identifying the effects of history on adaptation – origins of different sand-diving techniques in lizards. J. Zool. 235, 351–388 (1995).
    Article  Google Scholar 

    6.
    Attum, O., Eason, P. & Cobbs, G. Morphology, niche segregation, and escape tactics in a sand dune lizard community. J. Arid Environ. 68, 564–573 (2007).
    ADS  Article  Google Scholar 

    7.
    Kacoliris, F., Williams, J. & Molinari, A. Selection of key features of vegetation and escape behavior in the sand dune lizard (Liolaemus multimaculatus). Anim. Biol. 60, 157–167 (2010).
    Article  Google Scholar 

    8.
    Arnold, S. J. Morphology, performance and fitness. Am. Zool. 23, 347–361 (1983).
    Article  Google Scholar 

    9.
    Losos, J. B. & Sinervo, B. The effect of morphology and perch diameter on sprint performance of Anolis Lizards. J. Exp. Biol. 145, 23–30 (1989).
    Google Scholar 

    10.
    Losos, J. B. & Irschick, D. J. The effect of perch diameter on escape behavior of Anolis lizards: laboratory predictions and field tests. Anim. Behav. 51, 593–602 (1996).
    Article  Google Scholar 

    11.
    Luke, C. Convergent evolution of lizard toe fringes. Biol. J. Linn. Soc. 27, 1–16 (1986).
    ADS  Article  Google Scholar 

    12.
    Carothers, J. H. An experimental confirmation of morphological adaptation: toe fringes in the sand-dwelling lizard Uma scoparia. Evolution 40, 871–874 (1986).
    PubMed  Article  PubMed Central  Google Scholar 

    13.
    Irschick, D. J. & Jayne, B. C. Effects of incline on speed, acceleration, body posture and hindlimb kinematics in two species of lizard Callisaurus draconoides and Uma scoparia. J. Exp. Biol. 21, 273–287 (1998).
    Google Scholar 

    14.
    Korff, W. L. & McHenry, M. J. Environmental differences in substrate mechanics do not affect sprinting performance in sand lizards (Uma scoparia and Callisaurus draconoides). J. Exp. Biol. 214, 122–130 (2011).
    PubMed  Article  PubMed Central  Google Scholar 

    15.
    Bergmann, P. J. & Irschick, D. J. Alternate pathways of body shape evolution translate into common patterns of locomotor evolution in two clades of lizards. Evolution 64, 1569–1582 (2010).
    PubMed  Article  PubMed Central  Google Scholar 

    16.
    Li, C., Hsieh, S. T. & Goldman, D. I. Multi-functional foot use during running in the zebra-tailed lizard (Callisaurus draconoides). J. Exp. Biol. 215, 3293–3308 (2012).
    PubMed  Article  Google Scholar 

    17.
    Zhao, E. M., Zhao, K. T. & Zhou, K. Y. Fauna Sinica, Reptilian Vol. 2, Squamata (Beijing Science Press, Beijing, Lacertilia, 1999).
    Google Scholar 

    18.
    Solovyeva, E. N. et al. Cenozoic aridization in Central Eurasia shaped diversification of toad-headed agamas (Phrynocephalus; Agamidae, Reptilia). Peer. J. 6, e4543 (2018).
    PubMed  Article  CAS  Google Scholar 

    19.
    Jiang, Z. G. et al. Red List of China’s Vertebrates. Biodivers. Sci. 24, 550–551 (2016).
    Google Scholar 

    20.
    Du, W. G., Lin, C. X., Shou, L. & Ji, X. Morphological correlates of locomotor performance in four species of lizards using different habitats. Zool. Res. 26, 41–46 (2005).
    CAS  Google Scholar 

    21.
    Pérez, A. & Fabré, N. N. Spatial population structure of the Neotropical tiger catfish Pseudoplatystoma metaense: skull and otolith shape variation. J. Fish Biol. 82, 1453–1468 (2013).
    PubMed  Article  PubMed Central  Google Scholar 

    22.
    Higham, T. E. & Russel, A. P. Divergence in locomotor performance, ecology, and morphology between two sympatric sister species of desert-dwelling gecko. Biol. J. Linn. Soc. 101, 860–869 (2010).
    Article  Google Scholar 

    23.
    King, R. B. Analyzing the relationship between clutch size and female body size in reptiles. J. Herpetol. 34, 148–150 (2000).
    Article  Google Scholar 

    24.
    Benjamini, Y. & Hochberg, Y. Controlling the false discovery rate: apractical and powerful approach to multiple testing. J. R. Stat. Soc. B. 57, 289–300 (1995).
    MATH  Google Scholar 

    25.
    Imdadullah, M., Aslam, M. & Altaf, S. mctest: an R package for detection of collinearity among regressors. R. J. 8, 495–505 (2016).
    Article  Google Scholar 

    26.
    Carrascal, L. M., Galván, I. & Gordo, O. Partial least squares regression as an alternative to current regression methods used in ecology. Oikos 118, 681–690 (2009).
    Article  Google Scholar 

    27.
    Garthwaite, P. H. An interpretation of partial least squares. J. Am. Stat. Ass. 89, 122–127 (1994).
    MathSciNet  MATH  Article  Google Scholar 

    28.
    Abdi, H. Partial least squares regression and projection on latent structure regression. Wiley Interdiscip. Rev. Comput. 2, 97–106 (2010).
    Article  Google Scholar 

    29.
    Lesku, J. A., Roth, T. C. II., Amlaner, C. J. & Lima, S. L. A phylogenetic analysis of sleep architecture in mammals: the integration of anatomy, physiology, and ecology. Am. Nat. 168, 441–453 (2006).
    PubMed  Article  PubMed Central  Google Scholar 

    30.
    Mitchell, R. J. Testing evolutionary and ecological hypotheses using path analysis and structural equation modeling. Funct. Ecol. 6, 123–129 (1992).
    Article  Google Scholar 

    31.
    Wootton, J. T. Predicting direct and indirect effects: an integrated approach using experiments and path analysis. Ecology 75, 151–165 (1994).
    Article  Google Scholar 

    32.
    Arnold, S. J. Species densities of predators and their prey. Am. Nat. 106, 220–236 (1972).
    Article  Google Scholar 

    33.
    Team, R. C. A Language and Environment for Statistical Computing. Vienna: the R Foundation for Statistical Computing. http://www.R-project.org/ (2020).

    34.
    Irschick, D. J. & Garland, T. Jr. Integrating function and ecology in studies of adaptation: investigations of locomotor capacity as a model system. Annu. Rev. Ecol. Syst. 32, 367–396 (2001).
    Article  Google Scholar 

    35.
    Damme, R. V. & Vanhooydonck, B. Origins of interspecific variation in lizard sprint capacity. Funct. Ecol. 15, 186–202 (2001).
    Article  Google Scholar 

    36.
    Ballinger, R. E., Nietfeldt, J. W. & Krupa, J. J. An experimental analysis of the role of the tail in a high running speed in Cnemidophorus sexlineatus (Reptilia; Squamata: Lacertilia). Herpetology 35, 114–116 (1979).
    Google Scholar 

    37.
    Downes, S. & Shine, R. Why does tail loss increase a lizard’s later vulnerability to snake predators?. Ecology 82, 1293–1303 (2001).
    Article  Google Scholar 

    38.
    Johnson, T. P., Swoap, S. J., Bennett, A. F. & Josephson, R. K. Body size, muscle power output and limitations on burst locomotor performance in the lizard Dipsosaurus dorsalis. J. Exp. Biol. 174, 185–197 (1993).
    Google Scholar 

    39.
    Punzo, F. Tail Autotomy and running speed in the lizards Cophosaurus texanus and Uma notata. J. Herpetol. 16, 329–331 (1982).
    Article  Google Scholar 

    40.
    Borges-Landáez, P. A. & Shine, R. Influence of toe-clipping on running speed in Eulamprus quoyii, an Australian scincid lizard. J. Herpetol. 37, 592–595 (2003).
    Article  Google Scholar 

    41.
    Vanhooydonck, B., Damme, R. V. & Aerts, P. Variation in speed, gait characteristics and microhabitat use in lacertid lizards. J. Exp. Biol. 205, 1037–1046 (2002).
    PubMed  Google Scholar 

    42.
    Darwin, C. R. On the Origin of Species by Means of Natural Selection (Harvard University Press, Cambridge, 1859).
    Google Scholar 

    43.
    Losos, J. B. Adaptive radiation, ecological opportunity, and evolutionary determinism. Am. Nat. 175, 623–639 (2010).
    PubMed  Article  Google Scholar 

    44.
    Ricklefs, R. E. & Miles, D. B. Ecological and evolutionary inferences from morphology: an ecological perspective. In Ecological Morphology: Integrative and Organismal Biology (eds Wainwright, P. C. & Reilly, S. M.) 13–41 (University of Chicago Press, Chicago, 1994).
    Google Scholar 

    45.
    Dornburg, A., Sidlaukas, B., Santini, F. & Alfaro, N. M. E. The influence of an innovative locomotor strategy on the phenotypic diversifcation of triggerfsh (Family: Balistidae). Evolution 65, 1912–1926 (2011).
    PubMed  Article  Google Scholar 

    46.
    Vermeij, G. J. Historical contingency and the purported uniqueness of evolutionary innovations. Proc. Natl. Acad. Sci. USA 103, 1804–1809 (2006).
    ADS  CAS  PubMed  Article  Google Scholar 

    47.
    Collins, C. E. & Higham, T. E. Individuals of the common Namib Day Gecko vary in how adaptive simplification alters sprint biomechanics. Sci. Rep. 7, 15595 (2017).
    ADS  PubMed  PubMed Central  Article  CAS  Google Scholar 

    48.
    Cameron, S. F., Wynn, M. L. & Wilson, R. S. Sex-specific trade-offs and compensatory mechanisms: bite force and sprint speed pose conflicting demands on the design of geckos (Hemidactylus frenatus). J. Exp. Biol. 216, 3781–3789 (2013).
    CAS  PubMed  Article  Google Scholar 

    49.
    Stebbins, R. C. Some aspects of the ecology of the iguanid genus Uma. Ecol. Monogr. 14, 311–332 (1944).
    Article  Google Scholar 

    50.
    Evans, J. S., Eifler, D. A. & Eifler, M. A. Sand-diving as an escape tactic in the lizard Meroles anchietae. J. Arid Environ. 140, 1–5 (2017).
    ADS  Article  Google Scholar 

    51.
    Halloy, M., Etheridge, R. & Burghardt, G. M. To bury in sand: Phylogenetic relationships among lizard species of the boulengeri group, Liolaemus (Reptilia: Squamata: Tropiduridae), based on behavioral characters. Herpetol. Monogr. 12, 1–37 (1998).
    Article  Google Scholar 

    52.
    Bauwens, D., Garland, T., Castilla, A. M. & Van Damme, R. Evolution of sprint speed in lacertid lizards: morphological, physiological, and behavioral covariation. Evolution 49, 848–863 (1995).
    PubMed  PubMed Central  Google Scholar 

    53.
    Bonine, K. E. & Garland, T. J. Sprint performance of phrynosomatid lizards, measured on a high-speed treadmill, correlates with hindlimb length. J. Zool. 248, 255–265 (1999).
    Article  Google Scholar 

    54.
    Shimada, T., Kadau, D., Shinbrot, T. & Herrmann, H. J. Swimming in granular media. Phys. Rev. E. 80, 020301 (2009).
    ADS  Article  CAS  Google Scholar 

    55.
    Maladen, R. D., Ding, Y., Li, C. & Goldman, D. I. Undulatory swimming in sand: subsurface locomotion of the sandfish lizard. Sci. 325, 314–318 (2009).
    ADS  CAS  Article  Google Scholar 

    56.
    Sharpe, S. S., Ding, Y. & Goldman, D. I. Environmental interaction influences muscle activation strategy during sand-swimming in the sandfish lizard Scincus scincus. J. Exp. Biol. 216, 260–274 (2013).
    PubMed  Article  PubMed Central  Google Scholar 

    57.
    Edwards, S., Herrel, A., Vanhooydonck, B., Measey, G. J. & Tolley, K. A. Diving in head first: morphology and performance is linked to predator escape strategy in desert lizards (Meroles, Lacertidae, Squamata). Biol. J. Linn. Soc. 119, 919–931 (2016).
    Article  Google Scholar 

    58.
    Bergmann, P. J., Pettinelli, K. J., Crockett, M. E. & Schaper, E. G. It’s just sand between the toes: how particle size and shape variation affect running performance and kinematics in a generalist lizard. J. Exp. Biol. 220, 3706–3716 (2017).
    PubMed  Article  PubMed Central  Google Scholar 

    59.
    Arnold, E. N. Why do morphological phylogenies vary in quality—an investigation based on the comparative history of lizard clades. Proc. R. Soc. B. 240, 135–172 (1990).
    ADS  CAS  Google Scholar 

    60.
    Stellatelli, O. A., Block, C., Vega, L. E. & Cruz, F. B. Nonnative vegetation induces changes in predation pressure and escape behavior of two sand lizards (Liolaemidae: Liolaemus). Herpetology 71, 136–142 (2015).
    Article  Google Scholar 

    61.
    Etheridge, R. & de Queiroz, K. A phylogeny of Iguanidae. In Phylogenetic relationships of the lizard families, essays commemorating Charles L. Camp (eds Estes, R. & Pregill, G.) 283–368 (Stanford University Press, Stanford, 1988).
    Google Scholar 

    62.
    Pang, J. F. et al. A phylogeny of Chinese species in the genus Phrynocephalus (Agamidae) inferred from mitochondrial DNA sequences. Mol. Phylogenet. Evol. 27, 398–409 (2003).
    CAS  PubMed  Article  PubMed Central  Google Scholar 

    63.
    Guo, X. & Wang, Y. Partitioned Bayesian analyses, dispersal—vicariance analysis, and the biogeography of Chinese toad-headed lizards (Agamidae: Phrynocephalus): a reevaluation. Mol. Phylogenet. Evol. 45, 643–662 (2007).
    CAS  PubMed  Article  PubMed Central  Google Scholar  More

  • in

    Acid resistance of Masson pine (Pinus massoniana Lamb.) families and their root morphology and physiological response to simulated acid deposition

    1.
    Reis, S. et al. From acid rain to climate change. Science 338, 1153–1154 (2012).
    ADS  CAS  PubMed  Article  Google Scholar 
    2.
    Wang, L., Chen, Z., Shang, H., Wang, J. & Zhang, P. Y. Impact of simulated acid rain on soil microbial community function in Masson pine seedlings. Electron. J. Biotechnol. 17, 199–203 (2014).
    CAS  Article  Google Scholar 

    3.
    Wang, W. X. & Xu, P. J. Research progress in precipitation chemistry in China. Prog. Chem. 21, 266–281 (2010).
    Google Scholar 

    4.
    Meng, Y. et al. Characterization of inorganic ions in rainwater in the megacity of Shanghai: Spatiotemporal variations and source apportionment. Atmos. Res. 222, 12–24 (2019).
    CAS  Article  Google Scholar 

    5.
    Busch, G. et al. Forest ecosystems and the changing patterns of nitrogen input and acid deposition today and in the future based on a scenario. Environ. Sci. Pollut. Res. 8, 95–102 (2001).
    CAS  Article  Google Scholar 

    6.
    Wang, Y. et al. Phenotypic response of tobacco leaves to simulated acid rain and its impact on photosynthesis. Int. J. Agric. Biol. 21, 391–398 (2019).
    CAS  Google Scholar 

    7.
    Ramlall, C. et al. Effects of simulated acid rain on germination, seedling growth and oxidative metabolism of recalcitrant-seeded Trichilia dregeana grown in its natural seed bank. Physiol. Plant. 153, 149–160 (2015).
    CAS  PubMed  Article  Google Scholar 

    8.
    Wang, X. Q., Liu, Z., Niu, L. & Fu, B. Long-term effects of simulated acid rain stress on a staple forest plant, Pinus massoniana Lamb: A proteomic analysis. Trees Struct. Funct. 27, 297–309 (2013).
    Article  CAS  Google Scholar 

    9.
    Tong, S. M. & Zhang, L. Q. Differential sensitivity of growth and net photosynthetic rates in five tree species seedlings under simulated acid rain stress. Pol. J. Environ. Stud. 23, 2259–2264 (2014).
    CAS  Article  Google Scholar 

    10.
    Wu, X. & Liang, C. J. Enhancing tolerance of rice (Oryza sativa) to simulated acid rain by exogenous abscisic acid. Environ. Sci. Pollut. Res. 24, 4860–4870 (2017).
    CAS  Article  Google Scholar 

    11.
    Hu, W. J. et al. Proteome and calcium-related gene expression in Pinus massoniana needles in response to acid rain under different calcium levels. Plant Soil 380, 285–303 (2014).
    CAS  Article  Google Scholar 

    12.
    Luo, S. P., He, B. H., Zeng, Q. P., Li, N. J. & Yang, L. Effects of seasonal variation on soil microbial community structure and enzyme activity in a Masson pine forest in Southwest China. J. Mt. Sci. 17, 1398–1409 (2020).
    Article  Google Scholar 

    13.
    Zhang, M. Y., Wang, S. J., Wu, F. C., Yuan, X. H. & Zhang, Y. Chemical compositions of wet precipitation and anthropogenic influences at a developing urban site in southeastern China. Atmos. Res. 84, 311–322 (2007).
    CAS  Article  Google Scholar 

    14.
    Li, Y. F., Wang, Y. J., Wang, B. & Wang, Y. Q. Response of soil respiration and its components to simulated acid rain in a typical forest stand in the three gorges reservoir area. Environ. Sci. 40, 1457–1467. https://doi.org/10.13227/j.hjkx.201803170 (2019).
    Article  Google Scholar 

    15.
    Wu, G. Effect of acidic deposition on productivity of forest ecosystem and estimation of its economic losses in southern suburbs of Chongqing China. J. Environ. Sci-China 10, 83–88. http://kns.cnki.net/kns/detail/detail.aspx?FileName=HJKB802.010&DbName=CJFQ1998 (1998).

    16.
    Quan, W. X. & Ding, G. J. Root tip structure and volatile organic compound responses to drought stress in Masson pine (Pinusmassoniana Lamb.). Acta. Physiol. Plant. 39, 258 (2017).
    Article  CAS  Google Scholar 

    17.
    He, Y. L. et al. Physiological responses of needles of Pinus massoniana elite families to phosphorus stress in acid soil. J. For. Res. 24, 325–332 (2013).
    CAS  Article  Google Scholar 

    18.
    DeHayes, D. H., Schaberg, P. G., Hawley, G. J. & Strimbeck, G. R. Acid rain impacts on calcium nutrition and forest health. Bioscience 49, 789–800 (1999).
    Article  Google Scholar 

    19.
    Ju, S. M., Wang, L. P. & Chen, J. Y. Effects of silicon on the growth, photosynthesis and chloroplast ultrastructure of Oryzasativa L. seedlings under acid rain stress. Silicon 12, 655–664 (2020).
    CAS  Article  Google Scholar 

    20.
    Ma, Y., Guo, L. Q., Wang, H. X., Bai, B. & Shi, D. C. Accumulation, distribution, and physiological contribution of oxalic acid and other solutes in an alkali-resistant forage plant, Kochiasieversiana, during adaptation to saline and alkaline conditions. J. Plant Nutr. Soil Sci. 174, 655–663 (2011).
    CAS  Article  Google Scholar 

    21.
    Rajniak, J. et al. Biosynthesis of redox-active metabolites in response to iron deficiency in plants. Nat. Chem. Biol. 14, 442–450 (2018).
    CAS  PubMed  PubMed Central  Article  Google Scholar 

    22.
    Zhang, H. et al. Colonization on cucumber root and enhancement of chlorimuron-ethyl degradation in rhizosphere by Hansschlegelia zhihuaiae S113 and root exudates. J. Agric. Food Chem. 66, 4584–4591 (2018).
    CAS  PubMed  Article  Google Scholar 

    23.
    Chen, Y. T., Wang, Y. & Yeh, K. C. Role of root exudates in metal acquisition and tolerance. Curr. Opin. Plant Biol. 39, 66–72 (2017).
    CAS  PubMed  Article  Google Scholar 

    24.
    Yan, F., Schubert, S. & Mengel, K. Effect of low root medium pH on net proton release, root respiration, and root growth of corn (Zeamays L.) and broad bean (Viciafaba L.). Plant Physiol. 99, 415–421 (1992).
    CAS  PubMed  PubMed Central  Article  Google Scholar 

    25.
    Hu, X. F., Wu, A. Q., Wang, F. C. & Chen, F. S. The effects of simulated acid rain on internal nutrient cycling and the ratios of Mg, Al, Ca, N, and P in tea plants of a subtropical plantation. Environ. Monit. Assess. 191, 99 (2019).
    PubMed  Article  CAS  Google Scholar 

    26.
    Ericsson, T. Growth and shoot: root ratio of seedlings in relation to nutrient availability. Plant Soil 168–169, 205–214 (1995).
    Article  Google Scholar 

    27.
    Liu, J. X., Zhou, G. Y., Yang, C. W., Ou, Z. Y. & Peng, C. L. Responses of chlorophyll fluorescence and xanthophyll cycle in leaves of Schimasuperba Gardn. & Champ. and Pinusmassoniana Lamb. to simulated acid rain at Dinghushan biosphere reserve, china. Acta Physiol. Plant. 29, 33–38 (2007).
    Article  CAS  Google Scholar 

    28.
    Liang, C. J. & Zhang, B. J. Effect of exogenous calcium on growth, nutrients uptake and plasma membrane H+-ATPase and Ca2+-ATPase activities in soybean (Glycine max) seedlings under simulated acid rain stress. Ecotoxicol. Environ. Safe 165, 261–269 (2018).
    CAS  Article  Google Scholar 

    29.
    Li, X. W. et al. Boron alleviates aluminum toxicity by promoting root alkalization in transition zone via polar auxin transport. Plant Physiol. 177, 1254–1266 (2018).
    CAS  PubMed  PubMed Central  Article  Google Scholar 

    30.
    Wagatsuma, T. The membrane lipid bilayer as a regulated barrier to cope with detrimental ionic conditions: making new tolerant plant lines with altered membrane lipid bilayer. Soil Sci. Plant Nutr. 63, 507–516 (2017).
    CAS  Article  Google Scholar 

    31.
    Liang, C. J., Ma, Y. J. & Li, L. R. Comparison of plasma membrane H+-ATPase response to acid rain stress between rice and soybean. Environ. Sci. Pollut. Res. 27, 6389–6400 (2020).
    CAS  Article  Google Scholar 

    32.
    Guo, Q., Liu, L. & Barkla, B. J. Membrane lipid remodeling in response to salinity. Int. J. Mol. Sci. 20, 4264 (2019).
    CAS  PubMed Central  Article  PubMed  Google Scholar 

    33.
    Pellet, D. M., Grunes, D. L. & Kochian, L. V. Organic acid exudation as an aluminum-tolerance mechanism in maize (Zeamays L.). Planta 196, 788–795 (1995).
    CAS  Article  Google Scholar 

    34.
    Wang, H. H. et al. Organic acids enhance the uptake of lead by wheat roots. Planta 225, 1483–1494 (2007).
    CAS  PubMed  Article  Google Scholar 

    35.
    Li, Z. R. et al. Effect of root exudates of intercropping vicia faba and arabis alpina on accumulation and sub-cellular distribution of lead and cadmium. Int. J. Phytoremediat. 21, 4–13 (2019).
    CAS  Article  Google Scholar 

    36.
    Jia, H., Hou, D. Y., Dai, Y., Lu, H. L. & Yan, C. L. Effects of root exudates on the mobility of pyrene in mangrove sediment water system. CATENA 162, 396–401 (2018).
    CAS  Article  Google Scholar 

    37.
    Ahmed, I. M. et al. Physiological and molecular analysis on root growth associated with the tolerance to aluminumand drought individual and combined in Tibetan wild and cultivated barley. Planta 243, 973–985 (2016).
    CAS  PubMed  Article  Google Scholar 

    38.
    Wang, P., Bi, S. P., Wang, S. & Ding, Q. Y. Variation of wheat root exudates under aluminum stress. J. Agric. Food Chem. 54, 10040–10046 (2006).
    CAS  PubMed  Article  Google Scholar 

    39.
    Yao, Y. et al. Thallium-induced oxalate secretion from rice (Oryzasativa L.) root contributes to the reduction of Tl(III) to Tl(I). Environ. Exp. Bot. 155, 387–393 (2018).
    CAS  Article  Google Scholar 

    40.
    Javed, M. et al. Deciphering the growth, organic acid exudations, and ionic homeostasis of Amaranthusviridis L. and Portulacaoleracea L. under lead chloride stress. Environ. Sci. Pollut. Res. 25, 2958–2971 (2017).
    Article  CAS  Google Scholar 

    41.
    Wang, P., Bi, S. P., Ma, L. P. & Han, W. Y. Aluminum tolerance of two wheat cultivars (Brevor and Atlas66) in relation to the irrhizosphere pH and organic acids exuded from roots. J. Agric. Food. Chem. 54, 10033–10039 (2006).
    ADS  CAS  PubMed  Article  Google Scholar 

    42.
    Tu, J., Wang, H. S., Zhang, Z. F., Jin, X. & Li, W. Q. Trends in chemical composition of precipitation in Nanjing, China, during 1992–2003. Atmos. Res. 73, 283–298 (2005).
    CAS  Article  Google Scholar 

    43.
    Liang, C. J. & Wang, W. M. Antioxidant response of soybean seedlings to joint stress of lanthanum and acid rain. Environ. Sci. Pollut. Res. 20, 8182–8191 (2013).
    CAS  Article  Google Scholar 

    44.
    Tang, X. R., Li, W. P., Zuo, H. S. & Yin, Y. L. Study on the growth stability of Pinus Massoniana. J. Hunan For. Sci. Technol. 29, 20–24, http://kns.cnki.net/kns/detail/detail.aspx?FileName=HLKJ200204005&DbName=CJFQ2002 (2002) (in Chinese).

    45.
    Jia, X. M. et al. Comparative physiological responses and adaptive strategies of apple Malushalliana to salt, alkali and saline-alkali stress. Sci. Hortic. Amsterdam 245, 154–162 (2019).
    CAS  Article  Google Scholar 

    46.
    Inoue, S. & Kinoshita, T. Blue light regulation of stomatal opening and the plasma membrane H+-ATPase. Plant Physiol. 174, 531–538 (2017).
    CAS  PubMed  PubMed Central  Article  Google Scholar 

    47.
    Wang, S. L., Fan, C. N. Q. & Wang, P. Determination of ultra-trace organic acid in Masson pine (Pinusmassoniana L.) by accelerated solvent extraction and liquid chromatography-tandem mass spectrometry. J. Chromatogr. B 981–982, 1–8 (2015).
    Google Scholar 

    48.
    Yao, Y. W., Ren, B. L., Yang, Y., Huang, C. J. & Li, M. Y. Preparation and electrochemical treatment application of Ce-PbO2/ZrO2 composite electrode in the degradation of acridine orange by electrochemical advanced oxidation process. J. Hazard. Mater. 361, 141–151 (2019).
    CAS  PubMed  Article  Google Scholar  More