Volume 13 Issue 1
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Article Navigation >  Avian Research  > 2022  >  13: 100009.
Wendong Xie, Kai Song, Siegfried Klaus, Jon E. Swenson, Yue-Hua Sun. 2022: The past, present, and future of the Siberian Grouse (Falcipennis falcipennis) under glacial oscillations and global warming. Avian Research, 13(1): 100009. doi: 10.1016/j.avrs.2022.100009
Citation: Wendong Xie, Kai Song, Siegfried Klaus, Jon E. Swenson, Yue-Hua Sun. 2022: The past, present, and future of the Siberian Grouse (Falcipennis falcipennis) under glacial oscillations and global warming. Avian Research, 13(1): 100009. doi: 10.1016/j.avrs.2022.100009
shu

The past, present, and future of the Siberian Grouse (Falcipennis falcipennis) under glacial oscillations and global warming

doi: 10.1016/j.avrs.2022.100009
  • a.

    Key Laboratory of Animal Ecology and Conservation Biology, Institute of Zoology, Chinese Academy of Sciences, Beijing, 100101, China

  • b.

    College of Life Science, Hebei University, Baoding, 071002, China

  • c.

    Lindenhöhe 5, D-07749, Jena, Germany

  • d.

    Faculty of Environmental Sciences and Natural Resource Management, Norwegian University of Life Sciences, PO Box 5003, NO-1432, Ås, Norway

More Information
  • Corresponding author: E-mail address: songkai2014@sina.com (K. Song); E-mail address: sunyh@ioz.ac.cn (Y.-H. Sun)
  • Received Date: 05 Jul 2021
  • Accepted Date: 07 Dec 2021
  • Publish Date: 24 Feb 2022
    Abstract
  • Global climate change has a significant effect on species, as environment conditions change, causing many species' distributions to shift. During the last three million years, the earth has experienced glacial oscillations, forcing some species to survive in ice-free refugia during glacial periods and then disperse postglacially. In this study, by assessing the potential distribution of Siberian Grouse (Falcipennis falcipennis), we used Global Circular Models and Representative Concentration Pathways to model their pattern of range changes during glacial oscillations and the potential impact of present global warming. We used 158 location records of Siberian Grouse to generate a full climate model using 19 bioclimate variables in MaxEnt. We discarded variables with a correlation coefficient larger than 0.8 and relatively lower modeling contributions between each pair of correlated variables. Using the remaining variables, we created a normally uncorrelated simple climate model to predict the possible distribution of Siberian Grouse from the most recent Ice Age to present and to 2070. Then we added geographical data and the human interference index to construct a multiple factor full model to evaluate which were important in explaining the distribution of Siberian Grouse. The Total Suitability Zone (P ​≥ ​0.33) of Siberian Grouse is about 243,000 ​km2 and the Maximum Suitability Zone (P ​≥ ​0.66) is 36,000 ​km2 and is confined to the Russian Far East. Potential habitat modeling suggested that annual precipitation, annual mean temperature, and the distance from lakes are the most explanatory variables for the current distribution of Siberian Grouse. The distribution center moved to the southeast during the Last Glacial Maximum and spread back to the northwest after the ice melted and temperatures rose. The total area range of Siberian Grouse experienced a dramatic loss during the Last Glacial Maximum. Global warming is presently forcing the Siberian Grouse to migrate northward with a contraction of its range. There is an urgent need to protect its habitat, because little of its Maximum Sustainable Zone is protected, although there are some large reserves in that area.

     

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    • References(66)
  • Acevedo P, Jimenez-Valverde A, Lobo JM, Real R. Delimiting the geographical background in species distribution modelling. J Biogeogr. 2012;39:1383-1390 doi: 10.1111/j.1365-2699.2012.02713.x
    Anderson RP, Raza A. The effect of the extent of the study region on GIS models of species geographic distributions and estimates of niche evolution: preliminary tests with montane rodents (genus Nephelomys) in Venezuela. J Biogeogr. 2010;37:1378-1393 doi: 10.1111/j.1365-2699.2010.02290.x
    Andreev AV, Hafner F, Klaus S, Gossow H. Displaying behaviour and mating system in the Siberian Spruce Grouse (Falcipennis falcipennis Hartlaub 1855). J Ornithol. 2001;142:404-424 doi: 10.1007/BF01651339
    Andreev AV, Hafner F. Winter Biology of the Siberian Grouse Falcipennis falcipennis. Ornithol Sci. 2011;10:101-111 doi: 10.2326/osj.10.101
    Araujo MB, Pearson RG, Rahbek C. Equilibrium of species’ distributions with climate. Ecography. 2005;28:693-695 doi: 10.1111/j.2005.0906-7590.04253.x
    Berry, M.W., Dayal, U., Kamath, C., Skillicorn, D., 2004. Proceedings of the 2004 SIAM International Conference on Data Mining. Society for Industrial and Applied Mathematics, Philadelphia.
    BirdLife International, 2017. Falcipennis falcipennis (amended version of 2016 assessment). The IUCN Red List of Threatened Species 2017: e.T22679446A112117355. https://dx.doi.org/10.2305/IUCN.UK.2017-1.RLTS.T22679446A112117355.en. (Accessed 17 May 2021).
    Bogaert J, Zhou L, Tucker CJ, Myneni RB, Ceulemans R. Evidence for a persistent and extensive greening trend in Eurasia inferred from satellite vegetation index data. J Geophys Res. 2002;107:ACL4-1-ACL4-14
    Brown JL, Bennett JR, French CM. SDMtoolbox 2.0: the next generation Python-based GIS toolkit for landscape genetic, biogeographic and species distribution model analyses. PeerJ. 2017;5:e4095 doi: 10.7717/peerj.4095
    Brown JL, Yoder AD. Shifting ranges and conservation challenges for lemurs in the face of climate change. Ecol Evol. 2015;5:1131-1142 doi: 10.1002/ece3.1418
    Clark PU, Dyke AS, Shakun JD, Carlson AE, Clark J, Wohlfarth B, et al. The Last Glacial Maximum. 2009;325:710-714 doi: 10.1126/science.1172873
    Dai J, Roberts DA, Stow DA, An L, Hall SJ, Yabiku ST, et al. Mapping understory invasive plant species with field and remotely sensed data in Chitwan, Nepal. Rem Sens Environ. 2020;250:112037 doi: 10.1016/j.rse.2020.112037
    Dong F, Hung CM, Li XL, Gao JY, Zhang Q, Wu F, et al. Ice age unfrozen: severe effect of the last interglacial, not glacial, climate change on East Asian avifauna. BMC Evol Biol. 2017;17:244 doi: 10.1186/s12862-017-1100-2
    Duan J, Zhou G. Potential distribution of rice in china and its climate characteristics. Acta Ecol Sinica. 2011;31:6659-6668
    Elith JH, Graham CP, Anderson R, Dudik M, Ferrier S, Guisan A, et al. Novel methods improve prediction of species’ distributions from occurrence data. Ecography. 2006;29:129-151 doi: 10.1111/j.2006.0906-7590.04596.x
    Engler JO, Stiels D, Schidelko K, Strubbe D, Quillfeldt P, Brambilla M. Avian SDMs: current state, challenges, and opportunities. J Avian Biol. 2017;48:1483-1504 doi: 10.1111/jav.01248
    Farias AA, Svensson GL. Ecoregional vulnerability assessment for the functional richness of South American Carnivorans (Mammalia: Carnivora). J Mamm Evol. 2014;21:437-450 doi: 10.1007/s10914-014-9264-7
    Fielding AH, Bell JF. A review of methods for the assessment of prediction errors in conservation presence/absence models. Environ Conserv. 1997;24:38-49 doi: 10.1017/S0376892997000088
    Fitzpatrick MC, Gotelli NJ, Ellison AM. MaxEnt versus MaxLike: empirical comparisons with ant species distributions. Ecosphere. 2013;4:1-15 doi: 10.1890/es13-00066.1
    GBIF, 2021. Global Biodiversity Information Facility. http://www.gbif.org. (Accessed 8 May 2021).
    Guisan A, Thuiller W. Predicting species distribution: offering more than simple habitat models. Ecol Lett. 2005;8:993-1009 doi: 10.1111/j.1461-0248.2005.00792.x
    Herkert JR. An analysis of midwestern breeding bird population trends: 1966-1993. Am Midl Nat. 1995;134:41-50 doi: 10.2307/2426481
    Hijmans RJ, Graham CH. The ability of climate envelope models to predict the effect of climate change on species distributions. Global Change Biol. 2006;12:2272-2281 doi: 10.1111/j.1365-2486.2006.01256.x
    Hof AR, Rodriguez-Castaneda G, Allen AM, Jansson R, Nilsson C. Vulnerability of Subarctic and Arctic breeding birds. Ecol Appl. 2017;27:219-234 doi: 10.1002/eap.1434
    Hu R, Gu Y, Luo M, Lu Z, Wei M, Zhong J. Shifts in bird ranges and conservation priorities in China under climate change. PLoS One. 2020;15:e0240225 doi: 10.1371/journal.pone.0240225
    Hutchinson GE. Homage to Santa Rosalia or Why Are There So Many Kinds of Animals? Am Nat. 1959;93:145-159 doi: 10.1086/282070
    Intergovernmental Panel on Climate, 2014. Climate Change 2013 – the Physical Science Basis: Working Group I Contribution to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change. Cambridge University Press, Cambridge.
    Kimball RT, Hosner PA, Braun EL. A phylogenomic supermatrix of Galliformes (Landfowl) reveals biased branch lengths. Mol Phylogenet Evol. 2021;158:1-10
    Kimball RT, St. Mary CM, Braun EL. A macroevolutionary perspective on multiple sexual traits in the Phasianidae (Galliformes). Int J Evol Biol. 2011;2011:1-16 doi: 10.4061/2011/423938
    Klaus, S., Andreev, A.V., 2003. Falcipennis falcipennis (Hartlaub, 1855) Sichelhuhn. Atlas der Verbreitung Palaearktischer Vogel Akademie Verlag, Berlin € .
    Kondrashov LG. Russian Far East Forest disturbances and socio-economic problems of restoration. Forest Ecol Manag. 2004;201:65-74 doi: 10.1016/j.foreco.2004.06.012
    Konovalenko, K., 2012. The Influence of Forest Cover Changes in Russian Far East on the Population of Siberian Spruce Grouse. Master thesis. Albert-Ludwigs-Universität Freiburg, Freiburg, Germany.
    Lloyd AH, Fastie CL. Recent changes in treeline forest distribution and structure in interior Alaska. Ecoscience. 2003;10:176-185 doi: 10.1080/11956860.2003.11682765
    Lloyd AH, Rupp TS, Fastie CL, Starfield AM. Patterns and dynamics of treeline advance on the Seward Peninsula, Alaska. J Geophys Res. 2002;107:ALT2-1-ALT2-15
    Lucchini V, Hoglund J, Klaus S, Swenson J, Randi E. Historical biogeography and a mitochondrial DNA phylogeny of Grouse and Ptarmigan. Mol Phylogenet Evol. 2001;20:149-162 doi: 10.1006/mpev.2001.0943
    Ludwig T, Konovalenko K. Siberian Grouse in the Russian Far East: data deficient. Grouse News. 2012;43:11-15
    Mottl O, Flantua SGA, Bhatta KP, Felde VA, Giesecke T, Goring S, et al. Global acceleration in rates of vegetation change over the past 18,000 years. Science. 2021;372:860-864 doi: 10.1126/science.abg1685
    Natural Earth. http://www.naturalearthdata.com. (Accessed 8 May 2021).
    Opdam P, Wascher D. Climate change meets habitat fragmentation: linking landscape and biogeographical scale levels in research and conservation. Biol Conserv. 2004;117:285-297 doi: 10.1016/j.biocon.2003.12.008
    Otto-Bliesner BL, Marshall SJ, Overpeck JT, Miller GH, Hu A. Simulating Arctic Climate Warmth and Icefield Retreat in the Last Interglaciation. Science. 2006;311:1751-1753 doi: 10.1126/science.1120808
    Pachauri, R.K., Allen, M.R., Barros, V.R., Broome, J., Cramer, W., Christ, R., et al., 2014. Climate change 2014: synthesis report. In: Pachauri, R.K., Meyer, L. (Eds.), Contribution of Working Groups Ⅰ, Ⅱ and Ⅲ to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change. IPCC, Geneva, p. 151.
    Parmesan C, Yohe G. A globally coherent fingerprint of climate change impacts across natural systems. Nature. 2003;421:37-42 doi: 10.1038/nature01286
    Parmesan C. Ecological and evolutionary responses to recent climate change. Ann Rev Ecol Evol Syst. 2006;37:637-669 doi: 10.1146/annurev.ecolsys.37.091305.110100
    Persons NW, Hosner PA, Meiklejohn KA, Braun EL, Kimball RT. Sorting out relationships among the grouse and ptarmigan using intron, mitochondrial, and ultra-conserved element sequences. Mol Phylogenet Evol. 2016;98:123-132 doi: 10.1016/j.ympev.2016.02.003
    Phillips SJ, Anderson RP, Schapire RE. Maximum entropy modeling of species geographic distributions. Ecol Model. 2006;190:231-259 doi: 10.1504/IJGENVI.2006.010156
    Phillips, S.J., Dudík, M., Schapire, R.E., 2021. Maxent software for modeling species niches and distributions (Version 3.4.1). http://biodiversityinformatics.amnh.org/o pen_source/maxent/. (Accessed 17 May 2021).
    Prates L, Perez SI. Late Pleistocene South American megafaunal extinctions associated with rise of Fishtail points and human population. Nature Commun. 2021;12:2175 doi: 10.1038/s41467-021-22506-4
    Prieto-Torres, D.A., Rojas-Soto, O., Lira-Noriega, A., 2020. Ecological niche modeling and other tools for the study of avian malaria distribution in the Neotropics: a short literature review. In: Santiago-Alarcon, D., Marzal, A. (Eds.), Avian Malaria and Related Parasites in the Tropics: Ecology, Evolution and Systematics. Springer International Publishing, Chambridge, pp. 251–280.
    Rodriguez JP, Brotons L, Bustamante J, Seoane J. The application of predictive modelling of species distribution to biodiversity conservation. Divers Distrib. 2007;13:243-251 doi: 10.1111/j.1472-4642.2007.00356.x
    Root TL, Price JT, Hall KR, Schneider SH, Rosenzweig C, Pounds JA. Fingerprints of global warming on wild animals and plants. Nature. 2003;421:57-60 doi: 10.1038/nature01333
    Scheper J, Holzschuh A, Kuussaari M, Potts SG, Rundlof M, Smith HG, et al. Environmental factors driving the effectiveness of European agri-environmental measures in mitigating pollinator loss - a meta-analysis. Ecol Lett. 2013;16:912-920 doi: 10.1111/ele.12128
    Singh PB, Mainali K, Jiang Z, Thapa A, Subedi N, Awan MN, et al. Projected distribution and climate refugia of endangered Kashmir musk deer Moschus cupreus in greater Himalaya, South Asia. Sci Rep. 2020;10:1511 doi: 10.1038/s41598-020-58111-6
    Storch, I., 2007. Grouse: Status Survey and Conservation Action Plan 2006–2010. IUCN, Gland, Switzerland and World Pheasant Association, Fordingbridge, UK.
    Summers DM, Bryan BA, Crossman ND, Meyer WS. Species vulnerability to climate change: impacts on spatial conservation priorities and species representation. Global Change Biol. 2012;18:2335-2348 doi: 10.1111/j.1365-2486.2012.02700.x
    Tang CQ, Dong Y-F, Herrando-Moraira S, Matsui T, Ohashi H, He L-Y, et al. Potential effects of climate change on geographic distribution of the Tertiary relict tree species Davidia involucrata in China. Sci Rep. 2017;7:43822 doi: 10.1038/srep43822
    UNEP-WCMC, 2021. IUCN. Protected Planet: the World Database on Protected Areas (WDPA) and World Database on Other Effective Area-Based Conservation Measures (WD-OECM). UNEP-WCMC and IUCN, Cambridge.
    Urvois T, Auger-Rozenberg MA, Roques A, Rossi JP, Kerdelhue C. Climate change impact on the potential geographical distribution of two invading Xylosandrus ambrosia beetles. Sci Rep. 2021;11:1339 doi: 10.1038/s41598-020-80157-9
    Vladimir S, Svetlana K. Experiment on creatiing west Siberian Grouse reserve population (Falcipennis falcipennis). Tomsk State Univ J Biol. 2010;12:60-67
    Wang T, Overgaard J. The heartbreak of adapting to global warming. Science. 2007;315:49-50 doi: 10.1126/science.1137359
    Wildlife Conservation Society (WCS), Center for International Earth Science Information Network (CCU), 2005. Last of the Wild Project, Version 2, 2005 (LWP-2): Global Human Influence Index (HII) Dataset (IGHP). NASA Socioeconomic Data and Applications Center (SEDAC), Palisades, NY.
    WorldClim Version 1, 2021. Global Climate Data. https://www.worldclim.com/version1. (Accessed 8 May 2021).
    Zacarias D, Loyola R. Distribution modelling and multi-scale landscape connectivity highlight important areas for the conservation of savannah elephants. Biol Conserv. 2018;224:1-8 doi: 10.1016/j.biocon.2018.05.014
    Zhao H, Zhang H, Xu C. Study on Taiwania cryptomerioides under climate change: MaxEnt modeling for predicting the potential geographical distribution. Global Ecol Conserv. 2020;24:e01313 doi: 10.1016/j.gecco.2020.e01313
    Zhao M, Chang Y, Kimball RT, Zhao J, Lei F, Qu Y. Pleistocene glaciation explains the disjunct distribution of the Chestnut-vented Nuthatch (Aves, Sittidae). Zool Scripta. 2019;48:33-45 doi: 10.1111/zsc.12327
    Zheng, G.M., Wang, Q.S., 1998. China Red Data Book of Endangered Animals: Aves. Science Press, Beijing.
    Zhou L, Tucker CJ, Kaufmann RK, Slayback D, Shabanov NV, Myneni RB. Variations in northern vegetation activity inferred from satellite data of vegetation index during 1981 to 1999. J Geophys Res. 2001;106:20069-20083 doi: 10.1029/2000JD000115
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