Advertisement

Regional Environmental Change

, Volume 19, Issue 1, pp 27–38 | Cite as

Predicting suitable habitats of four range margin amphibians under climate and land-use changes in southwestern France

  • Clémentine PréauEmail author
  • Francis Isselin-Nondedeu
  • Yann Sellier
  • Romain Bertrand
  • Frédéric Grandjean
Original Article

Abstract

Whereas the effects of climate change on the potential range shifts of amphibians have been modeled at various scales, exploring the future impacts of land-use changes has rarely been considered. We modeled the potential distribution of Bombina variegata, Hyla arborea, Hyla meridionalis, and Triturus cristatus using ecological niche modeling, in the French administrative region Nouvelle-Aquitaine. The species are all at range margin in the study area which makes them interesting model species to study range shifts because of their potential local adaptations to their marginal habitat. We used Dyna-CLUE modeling framework to produce land-use change scenarios and combined them with RCP 2.6 and RCP 8.5 climate change scenarios to predict their impact on local amphibian distributions. B. variegata, H. arborea, and T. cristatus were predicted to shift eastward and northward. H. meridionalis was predicted to increase its probability of presence in the study area. The effect of climate determined the general pattern of distribution and was modulated depending on the scenarios of land-use change.

Keywords

Ecological niche modeling Global change Landscape change Range margin amphibians 

Notes

Acknowledgments

We thank Vienne Nature (Miguel Gailledrat), Deux-Sèvres Nature Environnement (Alexandre Boissinot and Florian Doré), Nature Environnement 17 (Olivier Roques), the Ligue de Protection des Oiseaux de Charente-Maritime (Eric Brugel), Charente Nature (Matthieu Dorfiac), the Groupe Mammalogique et Herpétologique du Limousin (Gaëlle Caublot), and Cistude Nature (Matthieu Berroneau, via their program “Sentinelles du climat” coordinated by Fanny Mallard) for their contributions to our work. We thank Julian Reynolds for assistance with the English. We thank the anonymous reviewers and the Editor for their comments and improvements on the manuscript.

Funding information

The study was supported by the Association Nationale de la Recherche et de la Technologie, the Agence de l’eau Loire Bretagne, and the Communauté d’agglomération de Grand Châtellerault. RB’s work was supported by the TULIP Laboratory of Excellence (ANR-10-LABX-41).

Compliance with ethical standards

Conflict of interest

The authors declare that they have no conflict of interest.

Supplementary material

10113_2018_1381_MOESM1_ESM.pdf (596 kb)
ESM 1 (PDF 595 kb)
10113_2018_1381_MOESM2_ESM.pdf (114 kb)
ESM 2 (PDF 113 kb)
10113_2018_1381_MOESM3_ESM.pdf (262 kb)
ESM 3 (PDF 262 kb)
10113_2018_1381_MOESM4_ESM.pdf (90 kb)
ESM 4 (PDF 90 kb)
10113_2018_1381_MOESM5_ESM.pdf (183 kb)
ESM 5 (PDF 182 kb)
10113_2018_1381_MOESM6_ESM.pdf (137 kb)
ESM 6 (PDF 137 kb)
10113_2018_1381_MOESM7_ESM.pdf (517 kb)
ESM 7 (PDF 516 kb)
10113_2018_1381_MOESM8_ESM.pdf (3.7 mb)
ESM 8 (PDF 3771 kb)
10113_2018_1381_MOESM9_ESM.pdf (290 kb)
ESM 9 (PDF 290 kb)
10113_2018_1381_MOESM10_ESM.pdf (235 kb)
ESM 10 (PDF 234 kb)

References

  1. Aguirre-Gutiérrez J, Kissling WD, Biesmeijer JC, Wallis De Vries MF, Reemer M, Carvalheiro LG (2017) Historical changes in the importance of climate and land use as determinants of Dutch pollinator distributions. J Biogeogr 44:696–707.  https://doi.org/10.1111/jbi.12937 CrossRefGoogle Scholar
  2. 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 43:1223–1232.  https://doi.org/10.1111/j.1365-2664.2006.01214.x CrossRefGoogle Scholar
  3. Anderson RP, Raza A (2010) 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 37:1378–1393.  https://doi.org/10.1111/j.1365-2699.2010.02290.x CrossRefGoogle Scholar
  4. Anputhas M, Janmaat JA, Nichol CF, Wei X (2016) Modelling spatial association in pattern based land use simulation models. J Environ Manag 181:465–476.  https://doi.org/10.1016/j.jenvman.2016.06.034 CrossRefGoogle Scholar
  5. Araújo MB, Thuiller W, Pearson RG (2006) Climate warming and the decline of amphibians and reptiles in Europe. J Biogeogr 33:1712–1728.  https://doi.org/10.1111/j.1365-2699.2006.01482.x CrossRefGoogle Scholar
  6. Araújo MB, Aldagador D, Cabeza M, Nogués-Bravo D, Thuiller W (2011) Climate change threatens European conservation areas. Ecol Lett 14:484–492.  https://doi.org/10.1111/j.1461-0248.2011.01610.x CrossRefGoogle Scholar
  7. Arntzen JW, Abrahams C, Meilink WRM, Iosif R, Zuiderwijk A (2017) Amphibian decline, pond loss and reduced population connectivity under agricultural intensification over a 38 year period. Biodivers Conserv 26:1411–1430.  https://doi.org/10.1007/s10531-017-1307-y CrossRefGoogle Scholar
  8. Barbet-Massin M, Jiguet F, Albert CH, Thuiller W (2012) Selecting pseudo-absences for species distribution models: how, where and how many? Methods Ecol Evol 3:327–338.  https://doi.org/10.1111/j.2041-210X.2011.00172.x CrossRefGoogle Scholar
  9. Barbosa AM, Real R, Vargas JM (2010) Use of coarse-resolution models of species’ distributions to guide local conservation inferences. Conserv Biol 24:1378–1387.  https://doi.org/10.1111/j.1523-1739.2010.01517.x CrossRefGoogle Scholar
  10. Berroneau M (2014) Atlas des Amphibiens et Reptiles d’Aquitaine. Ed Association Cistude Nature, Le HaillanGoogle Scholar
  11. Bertrand R, Riofrio-Dillon G, Lenoir J, Drapier J, de Ruffray P, Gegout JC, Loreau M (2016) Ecological constraints increase the climatic debt in forests. Nat Commun 7:12643.  https://doi.org/10.1038/ncomms12643 CrossRefGoogle Scholar
  12. Bonino MF, Moreno Azócar DL, Schulte JA, Cruz FB (2015) Climate change and lizards: changing species’ geographic ranges in Patagonia. Reg Environ Chang 15:1121–1132.  https://doi.org/10.1007/s10113-014-0693-x CrossRefGoogle Scholar
  13. Booth TH, Henry AN, John RB, Hutchinson MF (2014) BIOCLIM: the first species distribution modelling package, its early applications and relevance to most current MaxEnt studies. Divers Distrib 20:1–9.  https://doi.org/10.1111/ddi.12144 CrossRefGoogle Scholar
  14. Braunisch V, Bollmann K, Graf RF, Hirzel AH (2008) Living on the edge—modelling habitat suitability for species at the edge of their fundamental niche. Ecol Model 214:153–167.  https://doi.org/10.1016/j.ecolmodel.2008.02.001 CrossRefGoogle Scholar
  15. Cayuela H, Lambrey J, Vacher JP, Miaud C (2015) Highlighting the effects of land-use change on a threatened amphibian in a human-dominated landscape. Popul Ecol 57:433–443.  https://doi.org/10.1007/s10144-015-0483-4 CrossRefGoogle Scholar
  16. Cruz MJ, Robert EMR, Costa T, Avelar D, Rebelo R, Pulquério M (2016) Assessing biodiversity vulnerability to climate change: testing different methodologies for Portuguese herpetofauna. Reg Environ Chang 16:1293–1304.  https://doi.org/10.1007/s10113-015-0858-2 CrossRefGoogle Scholar
  17. Cushman SA (2006) Effects of habitat loss and fragmentation on amphibians: a review and prospectus. Biol Conserv 128:231–240.  https://doi.org/10.1016/j.biocon.2005.09.031 CrossRefGoogle Scholar
  18. de Pous P, Montori A, Amat F, Sanuy D (2016) Range contraction and loss of genetic variation of the Pyrenean endemic newt Calotriton asper due to climate change. Reg Environ Chang 16:995–1009.  https://doi.org/10.1007/s10113-015-0804-3 CrossRefGoogle Scholar
  19. Dolgener N, Freudenberger L, Schneeweiss N, Ibisch PL, Tiedemann R (2013) Projecting current and potential future distribution of the fire-bellied toad Bombina bombina under climate change in North-Eastern Germany. Reg Environ Chang 14:1063–1072.  https://doi.org/10.1007/s10113-013-0468-9 CrossRefGoogle Scholar
  20. Early R, Sax DF (2011) Analysis of climate paths reveals potential limitations on species range shifts. Ecol Lett 14:1125–1133.  https://doi.org/10.1111/j.1461-0248.2011.01681.x CrossRefGoogle Scholar
  21. Fahrig L (2003) Effects of habitat fragmentation on biodiversity. Annu Rev Ecol Evol Syst 34:487–515.  https://doi.org/10.1146/annurev.ecolsys.34.011802.132419 CrossRefGoogle Scholar
  22. Fjeldsa J, Tushabe H (2005) Complementarity of species distributions as a tool for prioritising conservation actions in Africa: testing the efficiency of using coarse-scale distribution data. In: African Biodiversity. Springer, Boston, MA, pp 1–24Google Scholar
  23. Gago-Silva A, Ray N, Lehmann A (2017) Spatial dynamic modelling of future scenarios of land use change in Vaud and Valais, western Switzerland. Int J Geo-Information 6:115.  https://doi.org/10.3390/ijgi6040115 CrossRefGoogle Scholar
  24. Gonçalves J, Honrado JP, Vicente JR, Civantos E (2016) A model-based framework for assessing the vulnerability of low dispersal vertebrates to landscape fragmentation under environmental change. Ecol Complex 28:174–186.  https://doi.org/10.1016/j.ecocom.2016.05.003 CrossRefGoogle Scholar
  25. Guisan A, Graham CH, Elith J, Huettmann F, Distri NS (2007) Sensitivity of predictive species distribution models to change in grain size. Divers Distrib 13:332–340.  https://doi.org/10.1111/j.1472-4642.2007.00342.x CrossRefGoogle Scholar
  26. Hanley JA, McNeil BJ (1982) The meaning and use of the area under a receiver operating characteristic (ROC) curve. Radiology 143:29–36.  https://doi.org/10.1148/radiology.143.1.7063747 CrossRefGoogle Scholar
  27. Haylock MR, Hofstra N, Klein Tank AMG, Klok EJ, Jones PD, New M (2008) A European daily high-resolution gridded data set of surface temperature and precipitation for 1950–2006. J Geophys Res Atmos 113:D20.  https://doi.org/10.1029/2008JD010201 CrossRefGoogle Scholar
  28. Hernandez PA, Graham CH, Master LL, Albert DL (2006) The effect of sample size and species characteristics on performance of different species distribution modeling methods. Ecography 29:773–785.  https://doi.org/10.1111/j.0906-7590.2006.04700.x CrossRefGoogle Scholar
  29. Hof C, Araujo MB, Jetz W, Rahbek C (2011) Additive threats from pathogens, climate and land-use change for global amphibian diversity. Nature 480:516–519.  https://doi.org/10.1038/nature10650 CrossRefGoogle Scholar
  30. Iosif R, Papes M, Samoila C, Cogalniceanu D (2014) Climate-induced shifts in the niche similarity of two related spadefoot toads (genus Pelobates). Org Divers Evol 14:397–408.  https://doi.org/10.1007/s13127-014-0181-7 CrossRefGoogle Scholar
  31. IPCC (2013) 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, CambridgeGoogle Scholar
  32. Isselin-Nondedeu F, Trochet A, Joubin T, Picard D, Etienne R, Le Chevalier H, Legrand D, Ribéron A (2017) Spatial genetic strucutre of Lissotriton helveticus L. following the restoration of a forest ponds network. Conserv Genet 18:853–866.  https://doi.org/10.1007/s10592-017-0932-z CrossRefGoogle Scholar
  33. Keppel G, Anderson S, Williams C, Kleindorfer S, O’Connell C (2017) Microhabitats and canopy cover moderate high summer temperatures in a fragmented Mediterranean landscape. PLoS One 12:18.  https://doi.org/10.1371/journal.pone.0183106 CrossRefGoogle Scholar
  34. Lenoir J, Svenning JC (2015) Climate-related range shifts—a global multidimensional synthesis and new research directions. Ecography 38:15–28.  https://doi.org/10.1111/ecog.00967 CrossRefGoogle Scholar
  35. Marshall L, Biesmeijer JC, Rasmont P, Vereecken NJ, Dvorak L, Fitzpatrick U, Francis F, Neumayer J, Odegaard F, Paukkunen JPT, Pawlikowski T, Reemer M, Roberts SPM, Straka J, Vray S, Dendoncker N (2017) The interplay of climate and land use change affects the distribution of EU bumblebees. Glob Chang Biol 23:1354–1013.  https://doi.org/10.1111/gcb.13867 CrossRefGoogle Scholar
  36. Mestre F, Risk BB, Mira A, Beja P, Pita R (2017) A metapopulation approach to predict species range shifts under different climate change and landscape connectivity scenarios. Ecol Model 359:406–414.  https://doi.org/10.1016/j.ecolmodel.2017.06.013 CrossRefGoogle Scholar
  37. Miro A, O’Brien D, Hall J, Jehle R (2017) Habitat requirements and conservation needs of peripheral populations: the case of the great crested newt (Triturus cristatus) in the Scottish Highlands. Hydrobiologia 792:169–181.  https://doi.org/10.1007/s10750-016-3053-7 CrossRefGoogle Scholar
  38. Mushet DM, Neau JL, Euliss NH (2014) Modeling effects of conservation grassland losses on amphibian habitat. Biol Conserv 174:93–100.  https://doi.org/10.1016/j.biocon.2014.04.001 CrossRefGoogle Scholar
  39. Nakicenovic N, Alcamo J, Grubler A, Riahi K, Roehrl R, Rogner HH, Victor N (2000) Special Report on Emissions Scenarios (SRES), A Special Report of Working Group III of the Intergovernmental Panel on Climate Change. Cambridge University Press. https://www.ipcc.ch/pdf/special-reports/emissions_scenarios.pdf. Accessed 9 November 2017
  40. Nowakowski AJ, Watling JI, Whitfield SM, Todd BD, Kurz DJ, Donnelly MA (2017) Tropical amphibians in shifting thermal landscapes under land-use and climate change. Conserv Biol 31:96–105.  https://doi.org/10.1111/cobi.12769 CrossRefGoogle Scholar
  41. Ochoa-Ochoa LM, Rodriguez P, Mora F, Flores-Villela O, Whittaker RJ (2012) Climate change and amphibian diversity patterns in Mexico. Biol Conserv 150:94–102.  https://doi.org/10.1016/j.biocon.2012.03.010 CrossRefGoogle Scholar
  42. Oldenborgh GJV, Reyes FJD, Drijfhout SS, Hawkins E (2013) Reliability of regional climate model trends. Environ Res Lett 8:014055.  https://doi.org/10.1088/1748-9326/8/1/014055/meta CrossRefGoogle Scholar
  43. R Core Team (2014) R: a language and environment for statistical computing. R Foundation for Statistical Computing, Vienna http://www.R-project.org/. Accessed 8 September 2017Google Scholar
  44. Root TL, Price JT, Hall KR, Schneider SH, Rosenzweig C, Pounds JA (2003) Fingerprints of global warming on wild animals and plants. Nature 421:57–60.  https://doi.org/10.1038/nature01333 CrossRefGoogle Scholar
  45. Scheffers BR, Edwards DP, Diesmos A, Williams SE, Evans TA (2014) Microhabitats reduce animal’s exposure to climate extremes. Glob Change Biol 20:495–503.  https://doi.org/10.1111/gcb.12439 CrossRefGoogle Scholar
  46. Sears MW, Raskin E, Angilletta JMJ (2011) The world is not flat: defining relevant thermal landscapes in the context of climate change. Integr Comp Biol 51:666–675.  https://doi.org/10.1093/icb/icr111 CrossRefGoogle Scholar
  47. Seo C, Thorne JH, Hannah L, Thuiller W (2009) Scale effects in species distribution models: implications for conservation planning under climate change. Biol Lett 5:39–43.  https://doi.org/10.1098/rsbl.2008.0476 CrossRefGoogle Scholar
  48. Sillero N (2010) Modelling suitable areas for Hyla meridionalis under current and future hypothetical expansion scenarios. Amphibia-Reptilia 31:37–50.  https://doi.org/10.1163/156853810790457948 CrossRefGoogle Scholar
  49. Sillero N (2011) What does ecological modelling model? A proposed classification of ecological niche models based on their underlying methods. Ecol Model 222:1343–1346.  https://doi.org/10.1016/j.ecolmodel.2011.01.018 CrossRefGoogle Scholar
  50. Sirami C, Caplat P, Popy S, Clamens A, Arlettaz R, Jiguet F, Brotons L, Martin JL (2017) Impacts of global change on species distributions: obstacles and solutions to integrate climate and land use. Glob Ecol Biogeogr 26:385–394.  https://doi.org/10.1111/geb.12555 CrossRefGoogle Scholar
  51. Sohl TL (2014) The relative impacts of climate and land-use change on conterminous United States bird species from 2001 to 2075. PLoS One 9:e112251.  https://doi.org/10.1371/journal.pone.0112251 CrossRefGoogle Scholar
  52. Stanton JC, Pearson RG, Horning N, Ersts P, Akcakaya HR (2012) Combining static and dynamic variables in species distribution models under climate change. Methods Ecol Evol 3:349–357.  https://doi.org/10.1111/j.2041-210X.2011.00157.x CrossRefGoogle Scholar
  53. Stockwell DRB, Peterson AT (2002) Effects of sample size on accuracy of species distribution models. Ecol Model 148:1–13.  https://doi.org/10.1016/s0304-3800(01)00388-x CrossRefGoogle Scholar
  54. Stürck J, Levers C, Zanden vEH, Schulp CJE, Verkerk PJ, Kuemmerle T, Helming J, Lotze-Campen H, Tabeau A, Popp A, Schrammeijer E, Verburg PH (2015) Simulating and delineating future land change trajectories across Europe. Reg Environ Change 18:733–749.  https://doi.org/10.1007/s10113-015-0876-0
  55. Tabor K, Williams JW (2010) Globally downscaled climate projections for assessing the conservation impacts of climate change. Ecol Appl 20:554–565.  https://doi.org/10.1890/09-0173.1 CrossRefGoogle Scholar
  56. Thirion JM, Evrard P (2012) Guide des Reptiles et Amphibiens de France. Editions Belin, ParisGoogle Scholar
  57. Thomas CD (2010) Climate, climate change and range boundaries. Divers Distrib 16:488–495.  https://doi.org/10.1111/j.1472-4642.2010.00642.x CrossRefGoogle Scholar
  58. Thuiller W, Araújo MB, Lavorel S (2004) Do we need land-cover data to model species distributions in Europe? J Biogeogr 31:353–361.  https://doi.org/10.1046/j.0305-0270.2003.00991.x CrossRefGoogle Scholar
  59. Thuiller W, Georges D, Engler R, Georges MD, Thuiller CW (2012) Package ‘biomod2’. https://cran.r-project.org/web/packages/biomod2/biomod2.pdf. Accessed 9 September 2012
  60. Titeux N, Henle K, Mihoub JB, Regos A, Geijzendorffer IR, Cramer W, Verburg PH, Brotons L (2016) Biodiversity scenarios neglect future land-use changes. Glob Change Biol 22:2505–2515.  https://doi.org/10.1111/gcb.13272 CrossRefGoogle Scholar
  61. Toranza C, Maneyro R (2013) Potential effects of climate change on the distribution of an endangered species: Melanophryniscus montevidensis (Anura: Bufonidae). Phyllomedusa 12:97–106.  https://doi.org/10.11606/issn.2316-9079.v12i2p97-106 CrossRefGoogle Scholar
  62. Trivedi MR, Berry PM, Morecroft MD, Dawson TP (2008) Spatial scale affects bioclimate model projections of climate change impacts on mountain plants. Glob Change Biol 14:1089–1103.  https://doi.org/10.1111/j.1365-2486.2008.01553.x CrossRefGoogle Scholar
  63. Trochet A, Dechartre J, Chevalier HL, Baillat B, Calvez O, Blanchet S, Ribéron A (2016) Effects of habitat and fragmented-landscape parameters on amphibian distribution at a large spatial scale. Herpetol J 26:73–84Google Scholar
  64. Vale CG, Tarroso P, Brito JC (2014) Predicting species distribution at range margins: testing the effects of study area extent, resolution and threshold selection in the Sahara-Sahel transition zone. Divers Distrib 20:20–33.  https://doi.org/10.1111/ddi.12115 CrossRefGoogle Scholar
  65. Van Buskirk J (2012) Permeability of the landscape matrix between amphibian breeding sites. Ecol Evol 12:3160–3167.  https://doi.org/10.1002/ece3.424 CrossRefGoogle Scholar
  66. Verburg PH, Overmars KP (2009) Combining top-down and bottom-up dynamics in land use modeling: exploring the future of abandoned farmlands in Europe with the Dyna-CLUE model. Landsc Ecol 24:1167–1181.  https://doi.org/10.1007/s10980-009-9355-7 CrossRefGoogle Scholar
  67. Verburg PH, Soepboer W, Veldkamp A, Limpiada R, Espaldon V, Mastura SS (2002) Modeling the spatial dynamics of regional land use: the CLUE-S model. Environ Manag 30:391–405.  https://doi.org/10.1007/s00267-002-2630-x CrossRefGoogle Scholar
  68. Wiens JA, Bachelet D (2010) Matching the multiple scales of conservation with the multiple scales of climate change. Conserv Biol 24:51–62.  https://doi.org/10.1111/j.1523-1739.2009.01409.x CrossRefGoogle Scholar
  69. Ye X, Yu X, Yu C, Tayibazhaer A, Xu F, Skidmore AK, Wang T (2018) Impacts of future climate and land cover changes on threatened mammals in the semi-arid Chinese Altai Mountains. Sci Total Environ 612:775–787.  https://doi.org/10.1016/j.scitotenv.2017.08.191 CrossRefGoogle Scholar

Copyright information

© Springer-Verlag GmbH Germany, part of Springer Nature 2018

Authors and Affiliations

  1. 1.Réserve Naturelle Nationale du Pinail, GEREPIMoulin de ChitréVouneuil-sur-VienneFrance
  2. 2.Laboratoire Ecologie et Biologie des Interactions – UMR CNRS 7267 Equipe Ecologie Evolution SymbiosePoitiers CedexFrance
  3. 3.Département Aménagement et Environnement Ecole Polytechnique de l’Université de ToursCNRS; UMR CNRS 7324 CITERESToursFrance
  4. 4.Département Aménagement et Environnement Ecole Polytechnique de l’Université François Rabelais de ToursCNRS; UMR CNRS 7324 CITERESToursFrance
  5. 5.Université Avignon, Aix Marseille, UMR 7223-CNRS, IRD, IMBEAvignonFrance
  6. 6.CNRS, Centre for Biodiversity Theory and Modelling, Theoretical and Experimental Ecology StationUMR5321 CNRS-Université Paul Sabatier Toulouse IIIMoulisFrance

Personalised recommendations