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Regional Environmental Change

, Volume 18, Issue 4, pp 1223–1233 | Cite as

Changes in future potential distributions of apex predator and mesopredator mammals in North America

  • Ranjit Pandey
  • Monica Papeş
Original Article

Abstract

Climate change has determined shifts in distributions of species and changing climates are likely to continue to affect species in the future. In this study, we used Maxent, an ecological niche modeling algorithm, to estimate the potential future distributions of apex predator and mesopredator mammals in boreal forest and tundra biomes of North America. We projected the climatic niche models of apex predators and mesopredators on future climate datasets based on three global circulation models (Beijing Climate Center Climate System Model, Hadley Global Environment Model, and Model for Interdisciplinary Research on Climate Earth System Model) and four greenhouse gas emission scenarios (RCP2.6, RCP4.5, RCP6, and RCP8.5). Under future climate projections, the potential distributions of most of the predators studied increased by 2050 and 2070. The potential distributions of two apex predators (brown bear, Ursus arctos, and polar bear, U. maritimus) and two mesopredator species (Canadian lynx, Lynx canadensis, and Arctic fox, Vulpes lagopus) were predicted to decline under all emission scenarios, by 2050 and 2070. The only apex predator that was predicted to increase its distribution under all greenhouse gas emission scenarios was U. americanus (American black bear). Similarly, distributions of mesopredators like Mephitis mephitis (striped skunk), and Procyon lotor (raccoon) were predicted to increase greatly under future climate conditions of all four emission scenarios. Predicted expansions of distribution ranges of most mesopredators and contractions of distribution ranges of apex predators included in this study may result in changes of species interactions in North American boreal forests and tundras in the future.

Keywords

Boreal forest Carnivores Climate change Ecological niche model Maxent Tundra 

Notes

Acknowledgements

We thank E. Smithwick and two anonymous reviewers for suggestions that improved our manuscript. We also thank K. Baum and M. Bolek for the feedback regarding the study design and for the comments on an earlier version of this manuscript.

Supplementary material

10113_2017_1265_Fig3_ESM.gif (231 kb)
ESM 1

Estimated current distribution ranges of North American apex predator and mesopredator mammal species from boreal forest and tundra biomes. Areas predicted suitable with climate niche models in Maxent algorithm are shown in blue, whereas the areas predicted unsuitable are shown in yellow. The orange dots represent occurrences used to generate the climate niche models. The diagonal line pattern represents the IUCN range map of the species. (GIF 231 kb)

10113_2017_1265_MOESM1_ESM.tif (5.4 mb)
High resolution image (TIFF 5556 kb)
10113_2017_1265_Fig4_ESM.gif (359 kb)
ESM 2

Estimated changes in ranges of North American apex predator and mesopredator mammal species from boreal forest and tundra biomes, form current to 2070. Only mean potential distributions are shown, averaged over five model replicates, three general circulation models, for the two medium stabilization scenarios (RCP4.5 and RCP6). Yellow areas represent no change (suitable or unsuitable), red areas represent loss in potential distribution (predicted as suitable under current climate and unsuitable under climate scenarios for 2070), and blue areas represent gain in potential distribution (predicted unsuitable under current climate and suitable under climate scenarios for 2070). The diagonal line pattern shows the IUCN range map of species. (GIF 359 kb)

10113_2017_1265_MOESM2_ESM.tif (9.6 mb)
High resolution image (TIFF 9787 kb)

References

  1. Allouche O, Tsoar A, Kadmon R (2006) Assessing the accuracy of species distribution models: prevalence, kappa and the true skill statistic (TSS). J Appl Ecol 43(6):1223–1232.  https://doi.org/10.1111/j.1365-2664.2006.01214.x CrossRefGoogle Scholar
  2. Araújo MB, Alagador D, Cabeza M, Nogués-Bravo D, Thuiller W (2011) Climate change threatens European conservation areas. Ecol Lett 14(5):484–492.  https://doi.org/10.1111/j.1461-0248.2011.01610.x CrossRefGoogle Scholar
  3. Bagchi R, Crosby M, Huntley B, Hole DG, Butchart SH, Collingham Y, Kalra M, Rajkumar J, Rahmani A, Pandey M (2013) Evaluating the effectiveness of conservation site networks under climate change: accounting for uncertainty. Glob Chang Biol 19(4):1236–1248.  https://doi.org/10.1111/gcb.12123 CrossRefGoogle Scholar
  4. Baltensperger A, Huettmann F (2015) Predicted shifts in small mammal distributions and biodiversity in the altered future environment of Alaska: an open access data and machine learning perspective. PLoS One 10(7):e0132054.  https://doi.org/10.1371/journal.pone.0132054 CrossRefGoogle Scholar
  5. Bardeleben C, Moore RL, Wayne RK (2005) A molecular phylogeny of the Canidae based on six nuclear loci. Mol Phylogenet Evol 37:815–831.  https://doi.org/10.1016/j.ympev.2005.07.019 CrossRefGoogle Scholar
  6. Barve N, Barve V, Jiménez-Valverde A, Lira-Noriega A, Maher SP, Peterson AT, Soberón J, Villalobos F (2011) The crucial role of the accessible area in ecological niche modeling and species distribution modeling. Ecol Model 222(11):1810–1819.  https://doi.org/10.1016/j.ecolmodel.2011.02.011 CrossRefGoogle Scholar
  7. Bateman BL, VanDerWal J, Williams SE, Johnson CN (2012) Biotic interactions influence the projected distribution of a specialist mammal under climate change. Divers Distrib 18(9):861–872.  https://doi.org/10.1111/j.1472-4642.2012.00922.x CrossRefGoogle Scholar
  8. Bellard C, Bertelsmeier C, Leadley P, Thuiller W, Courchamp F (2012) Impacts of climate change on the future of biodiversity. Ecol Lett 15(4):365–377.  https://doi.org/10.1111/j.1461-0248.2011.01736.x CrossRefGoogle Scholar
  9. Breed GA, Stichter S, Crone EE (2013) Climate-driven changes in northeastern US butterfly communities. Nat Clim Chang 3(2):142–145.  https://doi.org/10.1038/NCLIMATE1663 CrossRefGoogle Scholar
  10. Brook BW, Sodhi NS, Bradshaw CJ (2008) Synergies among extinction drivers under global change. Trends Ecol Evol 23:453–460.  https://doi.org/10.1016/j.tree.2008.03.011 CrossRefGoogle Scholar
  11. Burns CE, Johnston KM, Schmitz OJ (2003) Global climate change and mammalian species diversity in US national parks. Proc Natl Acad Sci U S A 100(20):11474–11477.  https://doi.org/10.1073/pnas.1635115100 CrossRefGoogle Scholar
  12. Cahill AE, Aiello-Lammens ME, Fisher-Reid MC, Hua X, Karanewsky CJ, Ryu HY, Sbeglia GC, Spagnolo F, Waldron JB, Warsi O (2012) How does climate change cause extinction? Proc R Soc Lond 280(1750):20121890.  https://doi.org/10.1098/rspb.2012.1890 CrossRefGoogle Scholar
  13. Carroll C (2007) Interacting effects of climate change, landscape conversion, and harvest on carnivore populations at the range margin: marten and lynx in the northern Appalachians. Conserv Biol 21(4):1092–1104.  https://doi.org/10.1111/j.1523-1739.2007.00719.x CrossRefGoogle Scholar
  14. Cobben M, Van Treuren R, Castañeda-Álvarez NP, Khoury C, Kik C, Van Hintum TJ (2015) Robustness and accuracy of Maxent niche modelling for Lactuca species distributions in light of collecting expeditions. Plant Genet Resour 13(02):1–9.  https://doi.org/10.1017/S1479262114000847 CrossRefGoogle Scholar
  15. Collins M, Knutti R, Arblaster J, Dufresne J, Fichefet T, Friedlingstein P, Gao X, Gutowski WJ, Johns T, Krinner G, Shongwe M, Tebaldi C, Weaver AJ, Wehner M (2013) Long-term climate change: projections, commitments and irreversibility. In: Stocker TF, Qin D, Plattner GK, Tignor M, Allen SK, Boschung J, Nauels A, Xia Y, Bex V, Midgley PM (eds) Climate change 2013: the physical science basis. Contribution of Working Group I to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change. Cambridge University Press, Cambridge and New York, pp 1029–1136.  https://doi.org/10.1017/CBO9781107415324.024
  16. Crooks KR, Soulé ME (1999) Mesopredator release and avifaunal extinctions in a fragmented system. Nature 400(6744):563–566.  https://doi.org/10.1038/23028 CrossRefGoogle Scholar
  17. DeVault TL, Olson ZH, Beasley JC, Rhodes OE (2011) Mesopredators dominate competition for carrion in an agricultural landscape. Basic Appl Ecol 12(3):268–274.  https://doi.org/10.1016/j.baae.2011.02.008 CrossRefGoogle Scholar
  18. Dormann CF, Elith J, Bacher S, Buchmann C, Carl G, Carré G, Marquéz JRG, Gruber B, Lafourcade B, Leitão PJ (2013) Collinearity: a review of methods to deal with it and a simulation study evaluating their performance. Ecography 36(1):27–46.  https://doi.org/10.1111/j.1600-0587.2012.07348.x CrossRefGoogle Scholar
  19. Durner GM, Douglas DC, Nielson RM, Amstrup SC, McDonald TL, Stirling I, Mauritzen M, Born EW, Wiig Ø, DeWeaver E (2009) Predicting 21st-century polar bear habitat distribution from global climate models. Ecol Monogr 79(1):25–58.  https://doi.org/10.1890/07-2089.1 CrossRefGoogle Scholar
  20. Elith J, Phillips SJ, Hastie T, Dudík M, Chee YE, Yates CJ (2011) A statistical explanation of MaxEnt for ecologists. Divers Distrib 17(1):43–57.  https://doi.org/10.1111/j.1472-4642.2010.00725.x CrossRefGoogle Scholar
  21. Fourcade Y, Engler JO, Rödder D, Secondi J (2014) Mapping species distributions with MAXENT using a geographically biased sample of presence data: a performance assessment of methods for correcting sampling bias. PLoS One 9(5):e97122.  https://doi.org/10.1371/journal.pone.0097122 CrossRefGoogle Scholar
  22. Frey SN, Conover MR (2006) Habitat use by meso-predators in a corridor environment. J Wildl Manag 70(4):1111–1118.  https://doi.org/10.2193/0022-541X(2006)70[1111:HUBMIA]2.0.CO;2 CrossRefGoogle Scholar
  23. Garcia RA, Burgess ND, Cabeza M, Rahbek C, Araújo MB (2012) Exploring consensus in 21st century projections of climatically suitable areas for African vertebrates. Glob Chang Biol 18(4):1253–1269.  https://doi.org/10.1111/j.1365-2486.2011.02605.x CrossRefGoogle Scholar
  24. Garmestani AS, Percival HF (2005) Raccoon removal reduces sea turtle nest depredation in the Ten Thousand Islands of Florida. Southeast Nat 4(3):469–472.  https://doi.org/10.1656/1528-7092(2005)004[0469:RRRSTN]2.0.CO;2 CrossRefGoogle Scholar
  25. Gaston KJ (2003) The structure and dynamics of geographic ranges. Oxford University Press, LondonGoogle Scholar
  26. Gienapp P, Teplitsky C, Alho J, Mills J, Merilä J (2008) Climate change and evolution: disentangling environmental and genetic responses. Mol Ecol 17(1):167–178.  https://doi.org/10.1111/j.1365-294X.2007.03413.x CrossRefGoogle Scholar
  27. Gillings S, Balmer DE, Fuller RJ (2015) Directionality of recent bird distribution shifts and climate change in Great Britain. Glob Chang Biol 21(6):2155–2168.  https://doi.org/10.1111/gcb.12823 CrossRefGoogle Scholar
  28. Heikkinen RK, Luoto M, Araújo MB, Virkkala R, Thuiller W, Sykes MT (2006) Methods and uncertainties in bioclimatic envelope modelling under climate change. Prog Phys Geogr 30(6):751–777.  https://doi.org/10.1177/0309133306071957 CrossRefGoogle Scholar
  29. Hitch AT, Leberg PL (2007) Breeding distributions of North American bird species moving north as a result of climate change. Conserv Biol 21(2):534–539.  https://doi.org/10.1111/j.1523-1739.2006.00609.x CrossRefGoogle Scholar
  30. Hof AR, Jansson R, Nilsson C (2012) Future climate change will favour non-specialist mammals in the (sub) arctics. PLoS One 7(12):e52574.  https://doi.org/10.1371/journal.pone.0052574 CrossRefGoogle Scholar
  31. Hogg E, Brandt JP, Kochtubajda B (2002) Growth and dieback of aspen forests in northwestern Alberta, Canada, in relation to climate and insects. Can J For Res 32(5):823–832.  https://doi.org/10.1139/X01-152 CrossRefGoogle Scholar
  32. Hosmer DW Jr, Lemeshow S, Sturdivant RX (2013) Applied logistic regression. John Wiley & Sons, USA.  https://doi.org/10.1002/9781118548387 CrossRefGoogle Scholar
  33. IPCC (2013) Summary for Policymakers. In 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, pp 1–30.  https://doi.org/10.1017/CBO9781107415324.004 
  34. IUCN (2015) IUCN red list. http://www.iucnredlist.org/. Accessed on 10 Dec 2015
  35. Jiang G, Liu J, Xu L, Yu G, He H, Zhang Z (2013) Climate warming increases biodiversity of small rodents by favoring rare or less abundant species in a grassland ecosystem. Integr Zool 8(2):162–174.  https://doi.org/10.1111/1749-4877.12027 CrossRefGoogle Scholar
  36. Johnston KM, Freund KA, Schmitz OJ (2012) Projected range shifting by montane mammals under climate change: implications for Cascadia’s National Parks. Ecosphere 3(11):1–15.  https://doi.org/10.1890/ES12-00077.1 CrossRefGoogle Scholar
  37. Jones CD, Hughes JK, Bellouin N, Hardiman SC, Jones GS, Knight J, Liddicoat S, O’Connor FM, Andres RJ, Bell C, Boo KO, Bozzo A, Butchart AN, Cadule P, Corbin KD, Doutriaux-Boucher M, Friedlingstein P, Gornall J, Gray L, Halloran PR, Hurtt G, Ingram WJ, Lamarque JF, Law RM, Meinshausen M, Osprey S, Palin EJ, Chini LP, Raddatz T, Sanderson MG, Sellar AA, Schurer A, Valdes P, Wood N, Woodward S, Yoshioka M, Zerroukat M (2011) The HadGEM2-ES implementation of CMIP5 centennial simulations. Geosci Model Dev 4(3):543–570.  https://doi.org/10.5194/gmd-4-543-2011 CrossRefGoogle Scholar
  38. Kerr JT, Pindar A, Galpern P, Packer L, Potts SG, Roberts SM, Rasmont P, Schweiger O, Colla SR, Richardson LL, Wanger DL, Gall LF, Sikes DS, Pantoja A (2015) Climate change impacts on bumblebees converge across continents. Science 349(6244):177–180.  https://doi.org/10.1126/science.aaa7031 CrossRefGoogle Scholar
  39. Khaliq I, Hof C, Prinzinger R, Böhning-Gaese K, Pfenninger M (2014) Global variation in thermal tolerances and vulnerability of endotherms to climate change. Proc R Soc Lond B Biol Sci 281(1789):1471–2954.  https://doi.org/10.1098/rspb.2014.1097 CrossRefGoogle Scholar
  40. Kimball S, Angert AL, Huxman TE, Venable DL (2010) Contemporary climate change in the Sonoran Desert favors cold adapted species. Glob Chang Biol 16(5):1555–1565.  https://doi.org/10.1111/j.1365-2486.2009.02106.x CrossRefGoogle Scholar
  41. Koepfli KP, Deere KA, Slater GJ, Begg C, Begg K, Grassman L, Lucherini M, Veron G, Wayne RK (2008) Multigene phylogeny of the Mustelidae: resolving relationships, tempo and biogeographic history of a mammalian adaptive radiation. BMC Biol 6(1):1–15.  https://doi.org/10.1186/1741-7007-6-10 CrossRefGoogle Scholar
  42. LaPoint S, Belant J, Kays R (2015) Mesopredator release facilitates range expansion in fisher. Anim Conserv 18(1):50–61.  https://doi.org/10.1111/acv.12138 CrossRefGoogle Scholar
  43. Laundré JW, Hernández L, Medina PL, Campanella A, López-Portillo J, González-Romero A, Grajales-Tam KM, Burke AM, Gronemeyer P, Browning DM (2014) The landscape of fear: the missing link to understand top-down and bottom-up controls of prey abundance? Ecology 95(5):1141–1152.  https://doi.org/10.1890/13-1083.1 CrossRefGoogle Scholar
  44. Lenoir J, Svenning JC (2015) Climate-related range shifts—a global multidimensional synthesis and new research directions. Ecography 38(1):15–28.  https://doi.org/10.1111/ecog.00967 CrossRefGoogle Scholar
  45. Liu C, White M, Newell G (2013) Selecting thresholds for the prediction of species occurrence with presence only data. J Biogeogr 40(4):778–789.  https://doi.org/10.1111/jbi.12058 CrossRefGoogle Scholar
  46. Lobo JM, Jiménez Valverde A, Real R (2008) AUC: a misleading measure of the performance of predictive distribution models. Glob Ecol Biogeogr 17(2):145–151.  https://doi.org/10.1111/j.1466-8238.2007.00358.x CrossRefGoogle Scholar
  47. Marcot BG, Jorgenson MT, Lawler JP, Handel CM, DeGange AR (2015) Projected changes in wildlife habitats in Arctic natural areas of northwest Alaska. Clim Chang 130(2):145–154.  https://doi.org/10.1007/s10584-015-1354-x CrossRefGoogle Scholar
  48. Moritz C, Patton JL, Conroy CJ, Parra JL, White GC, Beissinger SR (2008) Impact of a century of climate change on small-mammal communities in Yosemite National Park, USA. Science 322(5899):261–264.  https://doi.org/10.1126/science.1163428 CrossRefGoogle Scholar
  49. Myers P, Lundrigan BL, Hoffman SM, Haraminac AP, Seto SH (2009) Climate induced changes in the small mammal communities of the northern Great Lakes region. Glob Chang Biol 15(6):1434–1454.  https://doi.org/10.1111/j.1365-2486.2009.01846.x CrossRefGoogle Scholar
  50. Overland JE, Wang M (2010) Large-scale atmospheric circulation changes are associated with the recent loss of Arctic sea ice. Tellus Ser A Dyn Meteorol Oceanogr 62(1):1–9.  https://doi.org/10.1111/j.1600-0870.2009.00421.x CrossRefGoogle Scholar
  51. Pages M, Calvignac S, Klein C, Paris M, Hughes S, Hänni C (2008) Combined analysis of fourteen nuclear genes refines the Ursidae phylogeny. Mol Phylogenet Evol 47:73–83.  https://doi.org/10.1016/j.ympev.2007.10.019 CrossRefGoogle Scholar
  52. Papeş M, Gaubert P (2007) Modelling ecological niches from low numbers of occurrences: assessment of the conservation status of poorly known viverrids (Mammalia, Carnivora) across two continents. Divers Distrib 13(6):890–902.  https://doi.org/10.1111/j.1472-4642.2007.00392.x CrossRefGoogle Scholar
  53. Parmesan C, Yohe G (2003) A globally coherent fingerprint of climate change impacts across natural systems. Nature 421(6918):37–42.  https://doi.org/10.1038/nature01286 CrossRefGoogle Scholar
  54. Parmesan C, Ryrholm N, Stefanescu C, Hill JK, Thomas CD, Descimon H, Huntley B, Kaila L, Kullberg J, Tammaru T (1999) Poleward shifts in geographical ranges of butterfly species associated with regional warming. Nature 399(6736):579–583.  https://doi.org/10.1038/21181 CrossRefGoogle Scholar
  55. Pearson RG, Raxworthy CJ, Nakamura M, Peterson AT (2007) Predicting species distributions from small numbers of occurrence records: a test case using cryptic geckos in Madagascar. J Biogeogr 34(1):102–117.  https://doi.org/10.1111/j.1365-2699.2006.01594.x CrossRefGoogle Scholar
  56. Peterson AT, Soberón J (2012) Integrating fundamental concepts of ecology, biogeography, and sampling into effective ecological niche modeling and species distribution modeling. Plant Biosyst 146(4):789–796.  https://doi.org/10.1080/11263504.2012.740083 CrossRefGoogle Scholar
  57. Phillips SJ, Dudík M, Schapire RE (2004) A maximum entropy approach to species distribution modeling, Proceedings of the 21st international conference on Machine learning. ACM Press, pp 655–662.  https://doi.org/10.1145/1015330.1015412
  58. Phillips SJ, Anderson RP, Schapire RE (2006) Maximum entropy modeling of species geographic distributions. Ecol Model 190(3-4):231–259.  https://doi.org/10.1016/j.ecolmodel.2005.03.026 CrossRefGoogle Scholar
  59. Pokallus JW, Pauli JN (2015) Population dynamics of a northern-adapted mammal: disentangling the influence of predation and climate change. Ecol Appl 25(6):1546–1556.  https://doi.org/10.1890/14-2214.1 CrossRefGoogle Scholar
  60. Ponder W, Carter G, Flemons P, Chapman R (2001) Evaluation of museum collection data for use in biodiversity assessment. Conserv Biol 15(6):648–657.  https://doi.org/10.1890/14-2214.1 CrossRefGoogle Scholar
  61. Potter KA, Arthur Woods H, Pincebourde S (2013) Microclimatic challenges in global change biology. Glob Chang Biol 19(10):2932–2939.  https://doi.org/10.1111/gcb.12257 CrossRefGoogle Scholar
  62. Pöyry J, Luoto M, Heikkinen RK, Kuussaari M, Saarinen K (2009) Species traits explain recent range shifts of Finnish butterflies. Glob Chang Biol 15(3):732–743.  https://doi.org/10.1111/j.1365-2486.2008.01789.x CrossRefGoogle Scholar
  63. Prugh LR, Stoner CJ, Epps CW, Bean WT, Ripple WJ, Laliberte AS, Brashares JS (2009) The rise of the mesopredator. Bioscience 59(9):779–791.  https://doi.org/10.1525/bio.2009.59.9.9 CrossRefGoogle Scholar
  64. Ripple WJ, Estes JA, Beschta RL, Wilmers CC, Ritchie EG, Hebblewhite M, Berger J, Elmhagen B, Letnic M, Nelson MP, Schmitz OJ, Smith DW, Wallach AD, Wirsing AJ (2014) Status and ecological effects of the world’s largest carnivores. Science 343(6167):1241484.  https://doi.org/10.1126/science.1241484 CrossRefGoogle Scholar
  65. Roemer GW, Gompper ME, Van Valkenburgh B (2009) The ecological role of the mammalian mesocarnivore. Bioscience 59(2):165–173.  https://doi.org/10.1525/bio.2009.59.2.9 CrossRefGoogle Scholar
  66. Rondinini C, Visconti P (2015) Scenarios of large mammal loss in Europe for the 21st century. Conserv Biol 29(4):1028–1036.  https://doi.org/10.1111/cobi.12532 CrossRefGoogle Scholar
  67. Rowe KC, Rowe KM, Tingley MW, Koo MS, Patton JL, Conroy CJ, Perrine JD, Beissinger SR, Moritz C (2015) Spatially heterogeneous impact of climate change on small mammals of montane California. Proc R Soc Lond 282(1799):20141857.  https://doi.org/10.1098/rspb.2014.1857 CrossRefGoogle Scholar
  68. Rupp TS, Olson M, Adams LG, Dale BW, Joly K, Henkelman J, Collins WB, Starfield AM (2006) Simulating the influences of various fire regimes on caribou winter habitat. Ecol Appl 16(5):1730–1743.  https://doi.org/10.1890/1051-0761(2006)016[1730:STIOVF]2.0.CO;2 CrossRefGoogle Scholar
  69. Šálek M, Drahníková L, Tkadlec E (2015) Changes in home range sizes and population densities of carnivore species along the natural to urban habitat gradient. Mammal Rev 45:1–14.  https://doi.org/10.1111/mam.12027 Google Scholar
  70. Scheffer M, Hirota M, Holmgren M, Van Nes EH, Chapin FS (2012) Thresholds for boreal biome transitions. Proc Natl Acad Sci U S A 109(52):21384–21389.  https://doi.org/10.1073/pnas.1219844110 CrossRefGoogle Scholar
  71. Schloss CA, Nuñez TA, Lawler JJ (2012) Dispersal will limit ability of mammals to track climate change in the Western Hemisphere. Proc Natl Acad Sci 109(22):8606–8611.  https://doi.org/10.1073/pnas.1116791109 CrossRefGoogle Scholar
  72. Schmidt KA (2003) Nest predation and population declines in Illinois songbirds: a case for mesopredator effects. Conserv Biol 17(4):1141–1150.  https://doi.org/10.1046/j.1523-1739.2003.02316.x CrossRefGoogle Scholar
  73. Schmitz OJ, Post E, Burns CE, Johnston KM (2003) Ecosystem responses to global climate change: moving beyond color mapping. Bioscience 53(12):1199–1205.  https://doi.org/10.1641/0006-3568(2003)053[1199:ERTGCC]2.0.CO;2 CrossRefGoogle Scholar
  74. Sinervo B, Mendez-De-La-Cruz F, Miles DB, Heulin B, Bastiaans E, Villagrán-Santa Cruz M, Lara-Resendiz R, Martínez-Méndez N, Calderón-Espinosa ML, Meza-Lázaro RN (2010) Erosion of lizard diversity by climate change and altered thermal niches. Science 328(5980):894–899.  https://doi.org/10.1126/science.1184695 CrossRefGoogle Scholar
  75. Smith AB (2013) The relative influence of temperature, moisture and their interaction on range limits of mammals over the past century. Glob Ecol Biogeogr 22(3):334–343.  https://doi.org/10.1111/j.1466-8238.2012.00785.x CrossRefGoogle Scholar
  76. Soulé ME, Bolger DT, Alberts AC, Wrights J, Sorice M, Hill S (1988) Reconstructed dynamics of rapid extinctions of chaparral requiring birds in urban habitat islands. Conserv Biol 2(1):75–92.  https://doi.org/10.1111/j.1523-1739.1988.tb00337.x CrossRefGoogle Scholar
  77. Swets JA (1988) Measuring the accuracy of diagnostic systems. Science 240(4857):1285–1293.  https://doi.org/10.1126/science.3287615 CrossRefGoogle Scholar
  78. Thomas CD, Cameron A, Green RE, Bakkenes M, Beaumont LJ, Collingham YC, Erasmus BF, De Siqueira MF, Grainger A, Hannah L (2004) Extinction risk from climate change. Nature 427(6970):145–148.  https://doi.org/10.1038/nature02121 CrossRefGoogle Scholar
  79. Tilley A, López-Angarita J, Turner JR (2013) Diet reconstruction and resource partitioning of a Caribbean marine mesopredator using stable isotope bayesian modelling. PLoS One 8(11):e79560.  https://doi.org/10.1371/journal.pone.0079560 CrossRefGoogle Scholar
  80. Urban MC, Zarnetske PL, Skelly DK (2013) Moving forward: dispersal and species interactions determine biotic responses to climate change. Ann N Y Acad Sci 1297:44–60.  https://doi.org/10.1111/nyas.12184 Google Scholar
  81. USFWS (2015) U. S Fish & Wildlife Service. https://www.fws.gov/endangered/. Accessed on 12 Nov 2015
  82. Valladares F, Matesanz S, Guilhaumon F, Araújo MB, Balaguer L, Benito-Garzón M, Cornwell W, Gianoli E, Kleunen M, Naya DE (2014) The effects of phenotypic plasticity and local adaptation on forecasts of species range shifts under climate change. Ecol Lett 17(11):1351–1364.  https://doi.org/10.1111/ele.12348 CrossRefGoogle Scholar
  83. Van Vuuren DP, Edmonds J, Kainuma M, Riahi K, Thomson A, Hibbard K, Hurtt GC, Kram T, Krey V, Lamarque JF (2011) The representative concentration pathways: an overview. Clim Chang 109(1-2):5–31.  https://doi.org/10.1007/s10584-011-0148-z CrossRefGoogle Scholar
  84. Virkkala R, Lehikoinen A (2014) Patterns of climate-induced density shifts of species: poleward shifts faster in northern boreal birds than in southern birds. Glob Chang Biol 20(10):2995–3003.  https://doi.org/10.1111/gcb.12573 CrossRefGoogle Scholar
  85. Volney WJA, Fleming RA (2000) Climate change and impacts of boreal forest insects. Agric Ecosyst Environ 82(1-3):283–294.  https://doi.org/10.1016/S0167-8809(00)00232-2 CrossRefGoogle Scholar
  86. Wang M, Overland JE (2009) A sea ice free summer Arctic within 30 years? Geophys Res Lett 36(7):L07502.  https://doi.org/10.1029/2009GL037820 Google Scholar
  87. Warren RJ, Chick L (2013) Upward ant distribution shift corresponds with minimum, not maximum, temperature tolerance. Glob Chang Biol 19(7):2082–2088.  https://doi.org/10.1111/gcb.12169 CrossRefGoogle Scholar
  88. Watanabe S, Hajima T, Sudo K, Nagashima T, Takemura T, Okajima HHH, Nozawa T, Kawase H, Abe M, Yokohata T, Ise T, Sato H, Kato E, Takata K, Emori S, Kawamiya M (2011) MIROC-ESM 2010: model description and basic results of CMIP5-20c3m experiments. Geosci Model Dev 4(4):845–872.  https://doi.org/10.5194/gmd-4-845-2011 CrossRefGoogle Scholar
  89. Williams P, Margules CR, Hilbert DW (2002) Data requirements and data sources for biodiversity priority area selection. J Biosci 27(4):327–338.  https://doi.org/10.1007/BF02704963 CrossRefGoogle Scholar
  90. Wisz MS, Hijmans R, Li J, Peterson AT, Graham C, Guisan A (2008) Effects of sample size on the performance of species distribution models. Divers Distrib 14(5):763–773.  https://doi.org/10.1111/j.1472-4642.2008.00482.x CrossRefGoogle Scholar
  91. Wong MHG, Li R, Xu M, Long Y (2013) An integrative approach to assessing the potential impacts of climate change on the Yunnan snub-nosed monkey. Biol Conserv 158:401–409.  https://doi.org/10.1016/j.biocon.2012.08.030 CrossRefGoogle Scholar
  92. Wu J (2015) Detecting and attributing the effect of climate change on the changes in the distribution of Qinghai-Tibet plateau large mammal species over the past 50 years. Mamm Res 60(4):353–364.  https://doi.org/10.1007/s13364-015-0235-z CrossRefGoogle Scholar
  93. Wu T, Li W, Ji J, Xin X, Li L, Wang Z, Zhang Y, Li J, Zhang F, Wei M, Shi X, Wu F, Zhang L, Chu M, Jie W, Liu Y, Wang F, Liu X, Li Q, Dong M, Liang X, Gao Y, Zhang J (2013) Global carbon budgets simulated by the Beijing Climate Center Climate System Model for the last century. J Geophys Res Atmos 118(10):4326–4347.  https://doi.org/10.1002/jgrd.50320 CrossRefGoogle Scholar
  94. Yesson C, Brewer PW, Sutton T, Caithness N, Pahwa JS, Burgess M, Gray WA, White RJ, Jones AC, Bisby FA (2007) How global is the global biodiversity information facility. PLoS One 2(11):e1124.  https://doi.org/10.1371/journal.pone.0001124 CrossRefGoogle Scholar

Copyright information

© Springer-Verlag GmbH Germany, part of Springer Nature 2017

Authors and Affiliations

  1. 1.Department of Integrative BiologyOklahoma State UniversityStillwaterUSA
  2. 2.Department of Ecology and Evolutionary BiologyUniversity of TennesseeKnoxvilleUSA

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