Biodiversity and Conservation

, Volume 27, Issue 9, pp 2425–2441 | Cite as

Ignoring seasonal changes in the ecological niche of non-migratory species may lead to biases in potential distribution models: lessons from bats

  • Sonia Smeraldo
  • Mirko Di Febbraro
  • Luciano Bosso
  • Carles Flaquer
  • David Guixé
  • Fulgencio Lisón
  • Angelika Meschede
  • Javier Juste
  • Julia Prüger
  • Xavier Puig-Montserrat
  • Danilo Russo
Original Paper


Phenology is a key feature in the description of species niches to capture seasonality in resource use and climate requirements. Species distribution models (SDMs) are widespread tools to evaluate a species’ potential distribution and identify its large-scale habitat preferences. Despite its chief importance, data phenology is often neglected in SDM development. Non-migratory bats of temperate regions are good model species to test the effect of data seasonality on SDM outputs because of their different roosting preferences between hibernation and reproduction. We hypothesized that (1) the output of SDMs developed for six non-migratory European bat species will differ between hibernation and reproduction; (2) models built from datasets encompassing both ecological stages will perform better than seasonal models. We employed a dataset of 470 independent occurrences of bat hibernacula and 400 independent records of nursery roosts of selected species and for each species we developed separate winter, summer and mixed (i.e. generated from both winter and summer occurrences) models. Seasonal and mixed potential ranges differed from each other and the direction of this difference was species-specific. Mixed models outperformed seasonal models in representing species niches. Our work highlights the importance of considering data seasonality in the development of SDMs for bats as well as many other organisms, including non-migratory species, otherwise the analysis will lead to significant biases whose consequences for conservation planning and landscape management may be detrimental.


Biomod2 Hibernation IUCN Reproduction Species distribution models 



We would like to thank the Eurobats Advisory Committee for providing bat occurrence records for many of the countries within the Agreement range. We also thank two anonymous reviewers for the valuable comments made on a previous ms version.

Supplementary material

10531_2018_1545_MOESM1_ESM.docx (87 kb)
Supplementary material 1 (DOCX 87 kb)


  1. Algar AC, Kharouba HM, Young ER, Kerr JT (2009) Predicting the future of species diversity: macroecological theory climate change and direct tests of alternative forecasting methods. Ecography 32:22–33CrossRefGoogle 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 43:1223–1232CrossRefGoogle Scholar
  3. Altringham JD (2011) Bats: from evolution to conservation. Oxford University Press, OxfordCrossRefGoogle Scholar
  4. Amorim F, Carvalho SB, Honrado J, Rebelo H (2014) Designing optimized multi-species monitoring networks to detect range shifts driven by climate change: a case study with bats in the North of Portugal. PLoS ONE 9(1):e87291PubMedPubMedCentralCrossRefGoogle Scholar
  5. Ardestani EG, Tarkesh M, Bassiri M, Vahabi MR (2015) Potential habitat modeling for reintroduction of three native plant species in central Iran. J Arid Land 7:381–390CrossRefGoogle Scholar
  6. Arlettaz R, Christe P, Lugon A, Perrin N, Vogel P (2001) Food availability dictates the timing of parturition in insectivorous mouse-eared bats. Oikos 95:105–111CrossRefGoogle Scholar
  7. Austin MP (2002) Spatial prediction of species distribution: an interface between ecological theory and statistical modelling. Ecol Model 157:101–118CrossRefGoogle Scholar
  8. Austin MP, Van Niel KP (2011) Improving species distribution models for climate change studies: variable selection and scale. J Biogeogr 38:1–8CrossRefGoogle Scholar
  9. Balestrieri A, Bogliani G, Boano G, Ruiz-González A, Saino N, Costa S, Milanesi P (2016) Modelling the distribution of forest-dependent species in human-dominated landscapes: patterns for the pine marten in intensively cultivated lowlands. PLoS ONE 11:e0158203PubMedPubMedCentralCrossRefGoogle Scholar
  10. Barbet-Massin M, Thuiller W, Jiguet F (2010) How much do we overestimate future local extinction rates when restricting the range of occurrence data in climate suitability models? Ecography 33:878–886CrossRefGoogle Scholar
  11. 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 3:327–338CrossRefGoogle Scholar
  12. Barve N, Barve V, Jiménez-Valverde A, Lira-Noriega A, Maher SP, Peterson AT, Villalobos F (2011) The crucial role of the accessible area in ecological niche modeling and species distribution modelling. Ecol Model 222:1810–1819CrossRefGoogle Scholar
  13. Bates D, Maechler M, Dai B (2008) Lme4: linear-mixed effects models using S4 classes R package version 099375-27. http://www.lme4rforger-projectorg/
  14. Beaumont LJ, Pitman AJ, Poulsen M, Hughes L (2007) Where will species go? Incorporating new advances in climate modelling into projections of species distributions. Glob Change Biol 13:1368–1385CrossRefGoogle Scholar
  15. Bellamy C, Altringham J (2015) Predicting species distributions using record centre data: multi-scale modelling of habitat suitability for bat roosts. PLoS ONE 10:e0128440PubMedPubMedCentralCrossRefGoogle Scholar
  16. Biscardi S, Russo D, Casciani V, Cesarini D, Mei M, Boitani L (2007) Foraging requirements of the endangered long-fingered bat: the influence of micro-habitat structure water quality and prey type. J Zool 273:372–381CrossRefGoogle Scholar
  17. Bosso L, Mucedda M, Fichera G, Kiefer A, Russo D (2016a) A gap analysis for threatened bat populations on Sardinia. Hystrix 27:212–214Google Scholar
  18. Bosso L, Di Febbraro M, Cristinzio G, Zoina A, Russo D (2016b) Shedding light on the effects of climate change on the potential distribution of Xylella fastidiosa in the Mediterranean basin. Biol Invasions 18:1759–1768CrossRefGoogle Scholar
  19. Bosso L, Luchi N, Maresi G, Cristinzio G, Smeraldo S, Russo D (2017a) Predicting current and future disease outbreaks of Diplodia sapinea shoot blight in Italy: species distribution models as a tool for forest management planning. For Ecol Manag 400:655–664CrossRefGoogle Scholar
  20. Bosso L, De Conno C, Russo D (2017b) Modelling the risk posed by the Zebra Mussel Dreissena polymorpha: Italy as a case study. Environ Manag 60:304–313CrossRefGoogle Scholar
  21. Bosso L, Smeraldo S, Rapuzzi P, Sama G, Garonna AP, Russo D (2018) Nature protection areas of Europe are insufficient to preserve the threatened beetle Rosalia alpina (Coleoptera: Cerambycidae): evidence from species distribution models and conservation gap analysis. Ecol Entomol 43:192–203. CrossRefGoogle Scholar
  22. Breiner FT, Guisan A, Bergamini A, Nobis MP (2015) Overcoming limitations of modelling rare species by using ensembles of small models. Methods Ecol Evol 6:1210–1218CrossRefGoogle Scholar
  23. Breiner FT, Guisan A, Nobis MP, Bergamini A (2017) Including environmental niche information to improve IUCN Red List assessments. Divers Distrib 23:484–495CrossRefGoogle Scholar
  24. Burgman MA, Fox JC (2003) Bias in species range estimates from minimum convex polygons: implications for conservation and options for improved planning. Anim Conserv Forum 6(1):19–28CrossRefGoogle Scholar
  25. Carretero MA, Sillero N (2016) Evaluating how species niche modelling is affected by partial distributions with an empirical case. Acta Oecol 77:207–216CrossRefGoogle Scholar
  26. Carvalho SB, Brito JC, Pressey RL, Crespo E, Possingham HP (2010) Simulating the effects of using different types of species distribution data in reserve selection. Biol Conserv 143:426–438CrossRefGoogle Scholar
  27. Chuine I (2010) Why does phenology drive species distribution? Philos Trans R Soc Lond B Biol Sci 365:3149–3160PubMedPubMedCentralCrossRefGoogle Scholar
  28. Cooper-Bohannon R, Rebelo H, Jones G, Monadiem A, Schoeman MC, Taylor P, Park K (2016) Predicting bat distributions and diversity hotspots in southern Africa. Hystrix. CrossRefGoogle Scholar
  29. de Castro Pena JC, Kamino LHY, Rodrigues M, Mariano-Neto E, de Siqueira MF (2014) Assessing the conservation status of species with limited available data and disjunct distribution. Biol Conserv 170:130–136CrossRefGoogle Scholar
  30. Di Febbraro M, Roscioni F, Frate L, Carranza ML, De Lisio L, De Rosa D, Loy A (2015) Long-term effects of traditional and conservation-oriented forest management on the distribution of vertebrates in Mediterranean forests: a hierarchical hybrid modelling approach. Divers Distrib 21:1141–1154CrossRefGoogle Scholar
  31. Di Febbraro M, Martinoli A, Russo D, Preatoni D, Bertolino S (2016) Modelling the effects of climate change on the risk of invasion by alien squirrels. Hystrix 27(1):1–8Google Scholar
  32. Doko T, Fukui H, Kooiman A, Toxopeus AG, Ichinose T, Chen W, Skidmore AK (2011) Identifying habitat patches and potential ecological corridors for remnant Asiatic black bear (Ursus thibetanus japonicus) populations in Japan. Ecol Model 222:748–761CrossRefGoogle Scholar
  33. Domíguez-Vega H, Monroy-Vilchis O, Balderas-Valdivia CJ, Gienger CM, Ariano-Sánchez D (2012) Predicting the potential distribution of the beaded lizard and identification of priority areas for conservation. J Nat Conserv 20:247–253CrossRefGoogle Scholar
  34. Dormann CF, McPherson JM, Araújo MB, Bivand R, Bolliger J, Carl G, Davies RG, Hirzel A, Jetz W, Kissling WD, Kühn I, Ohlemüller R, Peres-Neto PR, Reineking B, Schröder B, Schurr FM, Wilson R (2007) Methods to account for spatial autocorrelation in the analysis of species distributional data: a review. Ecography 30:609–628CrossRefGoogle Scholar
  35. Dubuis A, Pottier J, Rion V, Pellissier L, Theurillat JP, Guisan A (2011) Predicting spatial patterns of plant species richness: a comparison of direct macroecological and species stacking modelling approaches. Divers Distrib 17:1122–1131CrossRefGoogle Scholar
  36. Ducci L, Agnelli P, Di Febbraro M, Frate L, Russo D, Loy A, Roscioni F (2015) Different bat guilds perceive their habitat in different ways: a multiscale landscape approach for variable selection in species distribution modelling. Landsc Ecol 30:2147–2159CrossRefGoogle Scholar
  37. Engler JO, Rödder D, Stiels D, Förschler MI (2014) Suitable reachable but not colonised: seasonal niche duality in an endemic mountainous songbird. J Ornithol 155:657–669CrossRefGoogle Scholar
  38. Erickson JL, West SD (2002) The influence of regional climate and nightly weather conditions on activity patterns of insectivorous bats. Acta Chiropterol 4:17–24CrossRefGoogle Scholar
  39. Feng X, Papeş M (2017) Can incomplete knowledge of species’ physiology facilitate ecological niche modelling? A case study with virtual species. Divers Distrib 23:1157–1168CrossRefGoogle Scholar
  40. Feuda R, Bannikova AA, Zemlemerova ED, Di Febbraro M, Loy A, Hutterer R, Colangelo P (2015) Tracing the evolutionary history of the mole Talpa europaea through mitochondrial DNA phylogeography and species distribution modelling. Biol J Linn Soc 114:495–512CrossRefGoogle Scholar
  41. Fielding AH, Bell JF (1997) A review of methods for the assessment of prediction errors in conservation presence/absence models. Environ Conserv 24:38–49CrossRefGoogle Scholar
  42. 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:e97122PubMedPubMedCentralCrossRefGoogle Scholar
  43. Fourcade Y, Besnard AG, Secondi J (2018) Paintings predict the distribution of species, or the challenge of selecting environmental predictors and evaluation statistics. Glob Ecol Biogeogr 27:245–256CrossRefGoogle Scholar
  44. Franklin J (2010) Mapping species distributions: spatial inference and prediction. Cambridge University Press, CambridgeCrossRefGoogle Scholar
  45. Frick WF, Reynolds DS, Kunz TH (2010) Influence of climate and reproductive timing on demography of little brown myotis Myotis lucifugus. J Anim Ecol 79:128–136PubMedCrossRefGoogle Scholar
  46. Grindal SD, Collard TS, Brigham RM, Barclay RM (1992) The influence of precipitation on reproduction by Myotis bats in British Columbia. Am Midl Nat 128:339–344CrossRefGoogle Scholar
  47. Guisan A, Thuiller W (2005) Predicting species distribution: offering more than simple habitat models. Ecol Lett 8:993–1009CrossRefGoogle Scholar
  48. Guisan A, Zimmermann NE (2000) Predictive habitat distribution models in ecology. Ecol Model 135:147–186CrossRefGoogle Scholar
  49. Guisan A, Tingley R, Baumgartner JB, Naujokaitis-Lewis I, Sutcliffe PR, Tulloch AI, Martin TG (2013) Predicting species distributions for conservation decisions. Ecol Lett 16:1424–1435PubMedPubMedCentralCrossRefGoogle Scholar
  50. Haghani A, Aliabadian M, Sarhangzadeh J, Setoodeh A (2016) Seasonal habitat suitability modeling and factors affecting the distribution of Asian Houbara in East Iran. Heliyon 2:e00142PubMedCentralCrossRefGoogle Scholar
  51. Hanley JA, McNeil BJ (1982) The meaning and use of the area under a receiver operating characteristic (ROC) curve. Radiology 143:29–36PubMedCrossRefGoogle Scholar
  52. Hayes MA, Cryan PM, Wunder MB (2015) Seasonally-dynamic presence-only species distribution models for a cryptic migratory bat impacted by wind energy development. PLoS ONE 10:e0132599PubMedPubMedCentralCrossRefGoogle Scholar
  53. Herkt KMB, Skidmore AK, Fahr J (2017) Macroecological conclusions based on IUCN expert maps: a call for caution. Glob Ecol Biogeogr 26:930–941CrossRefGoogle Scholar
  54. 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 29:773–785CrossRefGoogle Scholar
  55. Hijmans RJ, Cameron SE, Parra JL, Jones PG, Jarvis A (2005) Very high resolution interpolated climate surfaces for global land areas. Int J Climatol 25:1965–1978CrossRefGoogle Scholar
  56. Hirzel AH, Hausser J, Chessel D, Perrin N (2002) Ecological-niche factor analysis: how to compute habitat-suitability maps without absence data? Ecology 83:2027–2036CrossRefGoogle Scholar
  57. Holte D, Köppen U, Schmitz-Ornés A (2017) A comparison of migratory strategies of partial migratory raptors from Germany. J Ornithol 158:579–592CrossRefGoogle Scholar
  58. Hoying KM, Kunz TH (1998) Variation in size at birth and post-natal growth in the insectivorous bat Pipistrellus subflavus (Chiroptera: Vespertilionidae). J Zool 245:15–27CrossRefGoogle Scholar
  59. Huston MW (2002) Introductory essay: critical issues for improving predictions. In: Scott JM, Hugland PJ, Morrison ML et al (eds) Predicting species occurrences: issues of accuracy and scale. Island Press, Washington, DC, pp 7–21Google Scholar
  60. Jiguet F, Barbet-Massin M, Henry PY (2010) Predicting potential distributions of two rare allopatric sister species the globally threatened Doliornis cotingas in the Andes. J Field Ornithol 81:325–339CrossRefGoogle Scholar
  61. Kabir M, Hameed S, Ali H, Bosso L, Ud Din J, Bischof R, Redpath S, Ali Nawaz M (2017) Habitat suitability and movement Corridors of Grey Wolf (Canis lupus) in Northern Pakistan. n. PLoS ONE 12(11):e0187027. PubMedPubMedCentralCrossRefGoogle Scholar
  62. Kwon HS, Kim BJ, Jang GS (2016) Modelling the spatial distribution of wildlife animals using presence and absence data. Contemp Probl Ecol 9:515–518CrossRefGoogle Scholar
  63. Le Roux M, Redon M, Archaux F, Long J, Vincent S, Luque S (2017) Conservation planning with spatially explicit models: a case for horseshoe bats in complex mountain landscapes. Landsc Ecol 32:1005–1021CrossRefGoogle Scholar
  64. Lewis SE (1993) Effect of climatic variation on reproduction by pallid bats (Antrozous pallidus). Can J Zool 71:1429–1433CrossRefGoogle Scholar
  65. Liu C, Berry PM, Dawson TP, Pearson RG (2005) Selecting thresholds of occurrence in the prediction of species distributions. Ecography 28:385–393CrossRefGoogle Scholar
  66. Lobo JM, Jimenez-Valverde A, Hortal J (2010) The uncertain nature of absences and their importance in species distribution modelling. Ecography 33:103–114CrossRefGoogle Scholar
  67. Marmion M, Parviainen M, Luoto M, Heikkinen RK, Thuiller W (2009) Evaluation of consensus methods in predictive species distribution modelling. Divers Distrib 15:59–69CrossRefGoogle Scholar
  68. Masing M, Lutsar L (2007) Hibernation temperatures in seven species of sedentary bats (Chiroptera) in northeastern Europe. Acta Zool Lit 17:47–55CrossRefGoogle Scholar
  69. Mathewson PD, Moyer-Horner L, Beever EA, Briscoe NJ, Kearney M, Yahn JM, Porter WP (2017) Mechanistic variables can enhance predictive models of endotherm distributions: the American pika under current past and future climates. Glob Chang Biol 23:1048–1064PubMedCrossRefGoogle Scholar
  70. Miller-Rushing AJ, Weltzin J (2009) Phenology as a tool to link ecology and sustainable decision making in a dynamic environment. New Phytol 184:743–745PubMedCrossRefGoogle Scholar
  71. Mitchell-Jones AJ, Amori G, Bogdanowicz W, Spitzenberger F, Krystufek B, Stubbe CM (1999) The Atlas of European mammals. Poyser Natural History, LondonGoogle Scholar
  72. Morganti M, Preatoni D, Sarà M (2017) Climate determinants of breeding and wintering ranges of lesser kestrels in Italy and predicted impacts of climate change. J Avian Biol. CrossRefGoogle Scholar
  73. Muscarella R, Galante PJ, Soley-Guardia M, Boria RA, Kass JM, Uriarte M, Anderson RP (2014) ENMeval: an R package for conducting spatially independent evaluations and estimating optimal model complexity for Maxent ecological niche models. Methods Ecol Evol 5:1198–1205CrossRefGoogle Scholar
  74. Nagel A, Nagel R (1991) How do bats choose optimal temperatures for hibernation? Comp Biochem Physiol Part A 99:323–326CrossRefGoogle Scholar
  75. Newton I (2008) The migration ecology of birds Elsevier AmsterdamGoogle Scholar
  76. Olson DM, Dinerstein E, Wikramanayake ED, Burgess ND, Powell GV, Underwood EC, Loucks CJ (2001) Terrestrial ecoregions of the world: a new map of life on earth: a new global map of terrestrial ecoregions provides an innovative tool for conserving biodiversity. Bioscience 51:933–938CrossRefGoogle Scholar
  77. Olsson O, Rogers DJ (2009) Predicting the distribution of a suitable habitat for the white stork in Southern Sweden: identifying priority areas for reintroduction and habitat restoration. Anim Conserv 12:62–70CrossRefGoogle Scholar
  78. Ortega Z, Pérez-Mellado V (2016) Seasonal patterns of body temperature and microhabitat selection in a lacertid lizard. Acta Oecol 77:201–206CrossRefGoogle Scholar
  79. Papadatou E, Butlin RK, Altringham JD (2008) Seasonal roosting habits and population structure of the long-fingered bat Myotis capaccinii in Greece. J Mammal 89:503–512CrossRefGoogle Scholar
  80. Perry RW (2013) A review of factors affecting cave climates for hibernating bats in temperate North America. Environ Rev 21:28–39CrossRefGoogle Scholar
  81. Peterson AT (2011) Ecological niches and geographic distributions (MPB-49)(No 49) Princeton University PressGoogle Scholar
  82. Pio DV, Engler R, Linder HP, Monadjem A, Cotterill FP, Taylor PJ, Salamin N (2014) Climate change effects on animal and plant phylogenetic diversity in southern Africa. Glob Chang Biol 20:1538–1549CrossRefGoogle Scholar
  83. Racey PA, Swift SM (1981) Variations in gestation length in a colony of pipistrelle bats (Pipistrellus pipistrellus) from year to year. J Reprod Fertil 61:123–129PubMedCrossRefGoogle Scholar
  84. Raes N (2012) Partial versus full species distribution models. Nat Conserv 10:127–138CrossRefGoogle Scholar
  85. Razgour O, Rebelo H, Di Febbraro M, Russo D (2016) Painting maps with bats: species distribution modelling in bat research and conservation. Hystrix. CrossRefGoogle Scholar
  86. R Development Core Team (2012) R: A language and environment for statistical computing, reference index version, 2(0)Google Scholar
  87. Rebelo H, Tarroso P, Jones G (2010) Predicted impact of climate change on European bats in relation to their biogeographic patterns. Glob Chang Biol 16:561–576CrossRefGoogle Scholar
  88. Reddy S, Davalos LM (2003) Geographical sampling bias and its implications for conservation priorities in Africa. J Biogeogr 30:1719–1727CrossRefGoogle Scholar
  89. Rodrigues L, Zahn A, Rainho A, Palmeirim JM (2003) Contrasting the roosting behaviour and phenology of an insectivorous bat (Myotis myotis) in its southern and northern distribution ranges. Mammalia 67:321–336CrossRefGoogle Scholar
  90. Rubio-Salcedo M, Psomas A, Prieto M, Zimmermann NE, Martínez I (2017) Case study of the implications of climate change for lichen diversity and distributions. Biodivers Conserv 26:1121–1141CrossRefGoogle Scholar
  91. Russo D, Di Febbraro M, Rebelo H, Mucedda M, Cistrone L, Agnelli P, De Pasquale PP, Martinolo A, Scaravelli D, Spilinga C, Bosso L (2014) What story does geographic separation of insular bats tell? A case study on Sardinian Rhinolophids. PLoS ONE 9:e110894PubMedPubMedCentralCrossRefGoogle Scholar
  92. Russo D, Di Febbraro M, Cistrone L, Jones G, Smeraldo S, Garonna AP, Bosso L (2015) Protecting one protecting both? Scale-dependent ecological differences in two species using dead trees the rosalia longicorn beetle and the barbastelle bat. J Zool 297:165–175CrossRefGoogle Scholar
  93. Russo D, Cistrone L, Budinski I, Console G, Della Corte M, Milighetti C, Ancillotto L (2017) Sociality influences thermoregulation and roost switching in a forest bat using ephemeral roosts. Ecol Evol 7:5310–5321PubMedPubMedCentralCrossRefGoogle Scholar
  94. Silva TL, Vale CG, Godinho R, Fellous A, Hingrat Y, Alves PC, Brito JC (2017) Ecotypes and evolutionary significant units in endangered North African gazelles. Biol J Linnean Soc 122:286–300Google Scholar
  95. Smeraldo S, Di Febbraro M, Ćirović D, Bosso L, Trbojević I, Russo D (2017) Species distribution models as a tool to predict range expansion after reintroduction: a case study on Eurasian beavers (Castor fiber). J Nat Conserv 37:12–20CrossRefGoogle Scholar
  96. Speakman JR, Rowland A (1999) Preparing for inactivity: how insectivorous bats deposit a fat store for hibernation. Proc Nutr Soc 58:123–131PubMedCrossRefGoogle Scholar
  97. Speakman JR, Thomas DW (2003) Physiological ecology and energetics of bats. In: Kunz TH, Fenton MB (eds) Bat ecology. University of Chicago Press, Chicago, pp 430–490Google Scholar
  98. Syfert MM, Smith MJ, Coomes DA (2013) The effects of sampling bias and model complexity on the predictive performance of MaxEnt species distribution models. PLoS ONE 8:e55158PubMedPubMedCentralCrossRefGoogle Scholar
  99. Thuiller W (2003) BIOMOD–optimizing predictions of species distributions and projecting potential future shifts under global change. Glob Chang Biol 9:1353–1362CrossRefGoogle Scholar
  100. Thuiller W, Araújo MB, Lavorel S (2004) Do we need land-cover data to model species distributions in Europe? J Biogeog 31(3):353–361CrossRefGoogle Scholar
  101. Thuiller W, Richardson DM, Pyšek P, Midgley GF, Hughes GO, Rouget M (2005) Niche-based modelling as a tool for predicting the risk of alien plant invasions at a global scale. Glob Chang Biol 11:2234–2250CrossRefGoogle Scholar
  102. Thuiller W, Lafourcade B, Engler R, Araújo MB (2009) BIOMOD–a platform for ensemble forecasting of species distributions. Ecography 32:369–373CrossRefGoogle Scholar
  103. Tulloch AI, Sutcliffe P, Naujokaitis-Lewis I, Tingley R, Brotons L, Ferraz KMP, Rhodes JR (2016) Conservation planners tend to ignore improved accuracy of modelled species distributions to focus on multiple threats and ecological processes. Biol Conserv 199:157–171CrossRefGoogle Scholar
  104. Tuttle MD, Stevenson D (1982) Growth and survival of bats. In: Kunz TH (ed) Ecology of bats. Plenum Press, New York, pp 105–150CrossRefGoogle Scholar
  105. Vale CG, Campos JC, Silva TL, Gonçalves DV, Sow AS, Martínez-Freiría F, Brito JC (2016) Biogeography and conservation of mammals from the West Sahara-Sahel: an application of ecological niche-based models and GIS. Hystrix 27:1–10Google Scholar
  106. Van Horn B (2002) Approaches to habitat modelling: the tensions between pattern and process and between specificity and generality. In: Scott JM et al (eds) Predicting species occurrences: issues of accuracy and scale. Island Press, WashingtonGoogle Scholar
  107. Wiens JA (2002) Predicting species occurrences: progress, problems, and prospects. In: Scott JM, Heglund PJ, Morrison ML, Haufler JB, Raphael MG, Wall WA, Samson FB (eds) Predicting species occurrences: issues of accuracy and scale. Island Press, Washington, DC, pp 739–749Google Scholar
  108. Wiens JA, Stralberg D, Jongsomjit D, Howell CA, Snyder MA (2009) Niches models and climate change: assessing the assumptions and uncertainties. Proc Natl Acad Sci 106:19729–19736PubMedPubMedCentralCrossRefGoogle Scholar
  109. Wisz MS, Hijmans RJ, Li J, Peterson AT, Graham CH, Guisan A (2008) Effects of sample size on the performance of species distribution models. Divers Distrib 14:763–773CrossRefGoogle Scholar
  110. Zhang J, Nielsen SE, Chen Y, Georges D, Qin Y, Wang SS, Thuiller W (2017) Extinction risk of North American seed plants elevated by climate and land-use change. J Appl Ecol 54:303–312CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media B.V., part of Springer Nature 2018

Authors and Affiliations

  • Sonia Smeraldo
    • 1
  • Mirko Di Febbraro
    • 2
  • Luciano Bosso
    • 1
  • Carles Flaquer
    • 3
  • David Guixé
    • 4
  • Fulgencio Lisón
    • 5
  • Angelika Meschede
    • 6
  • Javier Juste
    • 7
  • Julia Prüger
    • 8
  • Xavier Puig-Montserrat
    • 3
    • 9
  • Danilo Russo
    • 1
    • 10
  1. 1.Wildlife Research Unit, Dipartimento di AgrariaUniversità degli Studi di Napoli Federico IINaplesItaly
  2. 2.EnvixLab, Dipartimento Bioscienze e TerritorioUniversità del MolisePescheItaly
  3. 3.Bat Research GroupGranollers Museum of Natural SciencesGranollersSpain
  4. 4.Forest Sciences Centre of CataloniaLleidaSpain
  5. 5.Laboratorio de Ecología del Paisaje Forestal, Departamento de Ciencias ForestalesUniversidad de La FronteraTemucoChile
  6. 6.Institute of Zoology IIUniversity of Erlangen-NurembergErlangenGermany
  7. 7.Estacion Biológica de Doñana (CSIC)SevilleSpain
  8. 8.Interessengemeinschaft für Fledermausschutz und -forschung in Thüringen e.VSchweinaGermany
  9. 9.Galanthus AssociationCataloniaSpain
  10. 10.School of Biological SciencesUniversity of BristolBristolUK

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