Biodiversity and Conservation

, 18:3629

Effect of characteristics of butterfly species on the accuracy of distribution models in an arid environment

  • Tim Newbold
  • Tom Reader
  • Samy Zalat
  • Ahmed El-Gabbas
  • Francis Gilbert
Original Paper

Abstract

Species distribution models show great promise as tools for conservation ecology. However, their accuracy has been shown to vary widely among taxa. There is some evidence that this variation is partly owing to ecological differences among species, which make them more or less easy to model. Here we test the effect of five characteristics of Egyptian butterfly species on the accuracy of distribution models, the first such comparison for butterflies in an arid environment. Unlike most previous studies, we perform independent contrasts to control for species relatedness. We show that range size, both globally and locally, has a negative effect on model accuracy. The results shed light on causes of variation in distribution model accuracy among species, and hence have relevance to practitioners using species distribution models in conservation decision making.

Keywords

AUC Ecological characteristics Independent contrasts Lepidoptera Maxent Species distribution models 

References

  1. Araújo MB, Luoto M (2007) The importance of biotic interactions for modelling species distributions under climate change. Glob Ecol Biogeogr 16:743–753. doi:10.1111/j.1466-8238.2007.00359.x CrossRefGoogle Scholar
  2. Beck J, Kitching IJ (2007) Correlates of range size and dispersal ability: a comparative analysis of sphingid moths from the Indo-Australian tropics. Glob Ecol Biogeogr 16:341–349. doi:10.1111/j.1466-8238.2007.00289.x CrossRefGoogle Scholar
  3. Beck J, Kitching IJ, Linsenmair KE (2006) Measuring range sizes of South-East Asian hawkmoths (Lepidoptera: Sphingidae): effects of scale, resolution and phylogeny. Glob Ecol Biogeogr 15:339–348. doi:10.1111/j.1466-822X.2006.00230.x CrossRefGoogle Scholar
  4. Berg Å, Gärdenfors U, von Proschwitz T (2004) Logistic regression models for predicting occurrence of terrestrial molluscs in southern Sweden—importance of environmental data quality and model complexity. Ecography 27:83–93. doi:10.1111/j.0906-7590.2004.03553.x CrossRefGoogle Scholar
  5. Boone RB, Krohn WB (1999) Modeling the occurrence of bird species: are the errors predictable? Ecol Appl 9:835–848. doi:10.1890/1051-0761(1999)009[0835:MTOOBS]2.0.CO;2 CrossRefGoogle Scholar
  6. Braby MF, Vila R, Pierce NE (2006) Molecular phylogeny and systematics of the Pieridae (Lepidoptera: Papilionoidea): higher classification and biogeography. Zool J Linn Soc 147:238–275Google Scholar
  7. Bro-Jørgensen J (2007) The intensity of sexual selection predicts weapon size in male bovids. Evol Int J Org Evol 61:1316–1326. doi:10.1111/j.1558-5646.2007.00111.x Google Scholar
  8. Brotons L, Thuiller W, Araújo MB et al (2004) Presence-absence versus presence-only modelling methods for predicting bird habitat suitability. Ecography 27:437–448. doi:10.1111/j.0906-7590.2004.03764.x CrossRefGoogle Scholar
  9. Brower AVZ (2000) Phylogenetic relationships among the Nymphalidae (Lepidoptera) inferred from partial sequences of the wingless gene. Proc R Soc Lond B Biol Sci 267:1201–1211. doi:10.1098/rspb.2000.1129 CrossRefGoogle Scholar
  10. Burnham KP, Anderson DR (2002) Model selection and multimodel inference: a practical information-theoretic approach. Springer, New YorkGoogle Scholar
  11. Carrascal LM, Seoane J, Palomino D et al (2006) Species-specific features affect the ability of census-derived models to map winter avian distribution. Ecol Res 21:681–691. doi:10.1007/s11284-006-0173-y CrossRefGoogle Scholar
  12. Elith J, Graham CH, Anderson RP et al (2006) Novel methods improve prediction of species’ distributions from occurrence data. Ecography 29:129–151. doi:10.1111/j.2006.0906-7590.04596.x CrossRefGoogle Scholar
  13. Freitas AVL, Brown KS Jr (2004) Phylogeny of the Nymphalidae (Lepidoptera). Syst Biol 53:363–383. doi:10.1080/10635150490445670 CrossRefPubMedGoogle Scholar
  14. García-Barros E (2000) Body size, egg size, and their interspecific relationships with ecological and life history traits in butterflies (Lepidoptera: Papilionoidea, Hesperioidea). Biol J Linn Soc Lond 70:251–284. doi:10.1111/j.1095-8312.2000.tb00210.x CrossRefGoogle Scholar
  15. Gaston KJ, Blackburn TM, Greenwood JJD et al (2000) Abundance–occupancy relationships. J Appl Ecol 37(S1):39–59. doi:10.1046/j.1365-2664.2000.00485.x CrossRefGoogle Scholar
  16. Gilbert F, Zalat S (2007) The butterflies of Egypt: Atlas, red data listing and conservation. BioMAP, EEAA, Cairo. Available at http://www.nottingham.ac.uk/~plzfg/publicns.htm
  17. Hansen MC, Defries RS, Townshend JRG et al (2000) Global land cover classification at 1 km spatial resolution using a classification tree approach. Int J Remote Sens 21:1331–1364. doi:10.1080/014311600210209 CrossRefGoogle Scholar
  18. Harvey PH, Pagel MD (1991) The comparative method in evolutionary biology. Oxford University Press, OxfordGoogle Scholar
  19. Hepinstall JA, Krohn WB, Sader SA (2002) Effects of niche width on the performance and agreement of avian habitat models. In: Scott JM, Heglund PJ, Morrison ML et al (eds) Predicting species occurrences: issues of accuracy and scale. Island Press, WashingtonGoogle Scholar
  20. Hernandez PA, Graham CH, Master LL et al (2006) The effect of sample size and species characteristics on performance of different species distribution modeling methods. Ecography 29:773–785. doi:10.1111/j.0906-7590.2006.04700.x CrossRefGoogle Scholar
  21. Hernandez PA, Franke I, Herzog SK et al (2008) Predicting species distributions in poorly-studied landscapes. Biodivers Conserv 17:1353–1366. doi:10.1007/s10531-007-9314-z CrossRefGoogle Scholar
  22. Hijmans RJ, Cameron SE, Parra JL et al (2005) Very high resolution interpolated climate surfaces for global land areas. Int J Climatol 25:1965–1978. doi:10.1002/joc.1276 CrossRefGoogle Scholar
  23. Hunt G, Roy K, Jablonski D (2005) Species-level heritability reaffirmed: a comment on “On the heritability of geographic range sizes”. Am Nat 166:129–135. doi:10.1086/430722 CrossRefPubMedGoogle Scholar
  24. Huntley B, Green RE, Collingham YC et al (2004) The performance of models relating species geographical distributions to climate is independent of trophic level. Ecol Lett 7:417–426. doi:10.1111/j.1461-0248.2004.00598.x CrossRefGoogle Scholar
  25. Jablonski D (1987) Heritability at the species level: analysis of geographic ranges of Cretaceous mollusks. Science 238:360–363. doi:10.1126/science.238.4825.360 CrossRefPubMedGoogle Scholar
  26. Kadmon R, Farber O, Danin A (2003) A systematic analysis of factors affecting the performance of climatic envelope models. Ecol Appl 13:853–867. doi:10.1890/1051-0761(2003)013[0853:ASAOFA]2.0.CO;2 CrossRefGoogle Scholar
  27. Karl JW, Svancara LK, Heglund PJ (2002) Species commonness and the accuracy of habitat-relationship models. In: Scott JM, Heglund PJ, Morrison ML et al (eds) Predicting species occurrences: issues of accuracy and scale. Island Press, WashingtonGoogle Scholar
  28. Knouft JH, Losos JB, Glor RE et al (2006) Phylogenetic analysis of the evolution of the niche in lizards of the Anolis sagrei group. Ecology 87(S):29–38CrossRefGoogle Scholar
  29. Lobo JM, Jiménez-Valverde A, Real R (2008) AUC: a misleading measure of the performance of predictive distribution models. Glob Ecol Biogeogr 17:145–151. doi:10.1111/j.1466-8238.2007.00358.x CrossRefGoogle Scholar
  30. Luoto M, Pöyry J, Heikkinen RK et al (2005) Uncertainty of bioclimate envelope models based on the geographical distribution of species. Glob Ecol Biogeogr 14:575–584. doi:10.1111/j.1466-822X.2005.00186.x CrossRefGoogle Scholar
  31. Maddison WP, Maddison DR (2007) Mesquite: a modular system for evolutionary analysis. Version 2.01. http://mesquiteproject.org
  32. Manel S, Dias JM, Buckton ST et al (1999) Alternative methods for predicting species distribution: an illustration with Himalayan river birds. J Appl Ecol 36:734–747. doi:10.1046/j.1365-2664.1999.00440.x CrossRefGoogle Scholar
  33. Marmion M, Luoto M, Heikkinen RK, Thuiller W (2008) The performance of state-of-the-art modelling techniques depends on geographical distribution of species. Ecol Modell. doi:10.1016/j.ecolmodel.2008.10.019
  34. Martins EP (2004) COMPARE, version 4.6b. Computer programs for the statistical analysis of comparative data. http://compare.bio.indiana.edu/
  35. McPherson JM, Jetz W (2007) Effects of species’ ecology on the accuracy of distribution models. Ecography 30:135–151Google Scholar
  36. McPherson JM, Jetz W, Rogers DJ (2004) The effects of species’ range sizes on the accuracy of distribution models: ecological phenomenon or statistical artefact? J Appl Ecol 41:811–823. doi:10.1111/j.0021-8901.2004.00943.x CrossRefGoogle Scholar
  37. Mitchell MS, Lancia RA, Gerwin JA (2001) Using landscape-level data to predict the distribution of birds on a managed forest: effects of scale. Ecol Appl 11:1692–1708. doi:10.1890/1051-0761(2001)011[1692:ULLDTP]2.0.CO;2 CrossRefGoogle Scholar
  38. Newbold T, Gilbert F, Zalat S et al (2009) Climate-based models of spatial patterns of species richness in Egypt’s butterfly and mammal fauna. J Biogeogr (in press)Google Scholar
  39. Page RDM (1996) Treeview: an application to display phylogenetic trees on personal computers. Comput Appl Biosci 12:357–358PubMedGoogle Scholar
  40. Pearce J, Ferrier S (2000a) Evaluating the predictive performance of habitat models developed using logistic regression. Ecol Modell 133:225–245. doi:10.1016/S0304-3800(00)00322-7 CrossRefGoogle Scholar
  41. Pearce J, Ferrier S (2000b) An evaluation of alternative algorithms for fitting species distribution models using logistic regression. Ecol Modell 128:127–147. doi:10.1016/S0304-3800(99)00227-6 CrossRefGoogle Scholar
  42. Pearce J, Ferrier S, Scotts D (2001) An evaluation of the predictive performance of distributional models for flora and fauna in north-east New South Wales. J Environ Manage 62:171–184. doi:10.1006/jema.2001.0425 CrossRefPubMedGoogle Scholar
  43. Pech P, Fric Z, Konvička M et al (2004) Phylogeny of Maculinea blues (Lepidoptera: Lycaenidae) based on morphological and ecological characters: evolution of parasitic myrmecophily. Cladistics 20:362–375. doi:10.1111/j.1096-0031.2004.00031.x CrossRefGoogle Scholar
  44. Peterson AT, Soberón J, Sánchez-Cordero V (1999) Conservatism of ecological niches in evolutionary time. Science 285:1265–1267. doi:10.1126/science.285.5431.1265 CrossRefPubMedGoogle Scholar
  45. Phillips SJ, Dudík M, Schapire RE (2004) A maximum entropy approach to species distribution modeling. In: Proceedings of the 21st International Conference on Machine Learning. ACM Press, New YorkGoogle Scholar
  46. Phillips SJ, Anderson RP, Schapire RE (2006) Maximum entropy modeling of species geographic distributions. Ecol Modell 190:231–259. doi:10.1016/j.ecolmodel.2005.03.026 CrossRefGoogle Scholar
  47. Pierce NE, Braby MF, Heath A et al (2002) The ecology and evolution of ant association in the Lycaenidae (Lepidoptera). Annu Rev Entomol 47:733–771. doi:10.1146/annurev.ento.47.091201.145257 CrossRefPubMedGoogle Scholar
  48. Pollock DD, Watt WB, Rashbrook VK et al (1998) Molecular phylogeny for Colias butterflies and their relatives (Lepidoptera : Pieridae). Ann Entomol Soc Am 91:524–531Google Scholar
  49. Pöyry J, Luoto M, Heikkinen RK et al (2008) Species traits are associated with the quality of bioclimatic models. Glob Ecol Biogeogr 17:403–414. doi:10.1111/j.1466-8238.2007.00373.x CrossRefGoogle Scholar
  50. Quinn RM, Gaston KJ, Roy DB (1998) Coincidence in the distributions of butterflies and their foodplants. Ecography 21:279–288. doi:10.1111/j.1600-0587.1998.tb00565.x CrossRefGoogle Scholar
  51. R Development Core Team (2004) R: a language and environment for statistical computing. R Foundation for Statistical Computing, ViennaGoogle Scholar
  52. Segurado P, Araújo MB (2004) An evaluation of methods for modelling species distributions. J Biogeogr 31:1555–1568. doi:10.1111/j.1365-2699.2004.01076.x CrossRefGoogle Scholar
  53. Seoane J, Carrascal LM, Alonso CL et al (2005) Species-specific traits associated to prediction errors in bird habitat suitability modelling. Ecol Modell 185:299–308. doi:10.1016/j.ecolmodel.2004.12.012 CrossRefGoogle Scholar
  54. Stockwell DRB, Peterson AT (2002) Effects of sample size on accuracy of species distribution models. Ecol Modell 148:1–13. doi:10.1016/S0304-3800(01)00388-X CrossRefGoogle Scholar
  55. VanDer Wal J, Shoo LP, Graham C, Williams SE (2009) Selecting pseudo-absence data for presence-only distribution modeling: how far should you stray from what you know? Ecol Modell 220:589–594. doi:10.1016/j.ecolmodel.2008.11.010 CrossRefGoogle Scholar
  56. Wahlberg N, Weingartner E, Nylin S (2003) Towards a better understanding of the higher systematics of Nymphalidae (Lepidoptera: Papilionoidea). Mol Phylogenet Evol 28:473–484. doi:10.1016/S1055-7903(03)00052-6 CrossRefPubMedGoogle Scholar
  57. Wahlberg N, Braby MF, Brower AVZ et al (2005) Synergistic effects of combining morphological and molecular data in resolving the phylogeny of butterflies and skippers. Proc R Soc Lond B Biol Sci 272:1577–1586. doi:10.1098/rspb.2005.3124 CrossRefGoogle Scholar
  58. Webb TJ, Gaston KJ (2003) On the heritability of geographic range sizes. Am Nat 161:553–566. doi:10.1086/368296 CrossRefPubMedGoogle Scholar
  59. Whittaker RJ, Willis KG, Field R (2001) Scale and species richness: towards a general, hierarchical theory of species diversity. J Biogeogr 28:453–470. doi:10.1046/j.1365-2699.2001.00563.x CrossRefGoogle Scholar
  60. Wintle BA, Elith J, Potts JM (2005) Fauna habitat modelling and mapping: a review and case study in the Lower Hunter Central Coast region of NSW. Austral Ecol 30:719–738. doi:10.1111/j.1442-9993.2005.01514.x CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media B.V. 2009

Authors and Affiliations

  • Tim Newbold
    • 1
  • Tom Reader
    • 1
  • Samy Zalat
    • 2
    • 3
  • Ahmed El-Gabbas
    • 3
  • Francis Gilbert
    • 1
    • 3
  1. 1.School of BiologyUniversity of NottinghamNottinghamUK
  2. 2.Suez Canal UniversityIsmailiaEgypt
  3. 3.BioMAP ProjectEgyptian Environmental Affairs AgencyMaadi, CairoEgypt

Personalised recommendations