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Landscape Ecology

, Volume 26, Issue 1, pp 83–94 | Cite as

Landscape variables impact the structure and composition of butterfly assemblages along an urbanization gradient

  • Benjamin Bergerot
  • Benoit Fontaine
  • Romain Julliard
  • Michel Baguette
Research Article

Abstract

How urbanization affects the distribution patterns of butterflies is still poorly known. Here we investigated the structure and composition of butterfly assemblages along an urbanization gradient within the most urbanized and densely populated region in France (Île-de-France). Using a method issued from artificial neural networks, i.e. self-organizing maps (SOMs), we showed the existence of four typical assemblages ranging from urban-tolerant species to urban-avoider species. We identified indicator species of these assemblages: the peacock butterfly (Inachis io) in urbanized areas, the swallowtail (Papilio machaon) in sites with intermediate human pressure, or the meadow brown (Maniola jurtina), the small heath (Coenonympha pamphilus) and the gatekeeper (Pyronia tithonus) in meadows around Paris. A discriminant analysis showed that the four assemblages were mainly segregated by landscape elements, both by structural variables (habitat type, proportion of rural areas and artificial urban areas, patch surface) and functional variables (distance to the nearest wood, artificial area and park). Artificial neural networks and SOMs coupled stepwise discriminant analysis proved to be promising tools that should be added to the toolbox of community and spatial ecologists.

Keywords

Community Human pressure Urban development Landscape structure Artificial neural networks 

Notes

Acknowledgments

We would like to thank Jean-Pierre Moussus for helpful corrections on the manuscript. We thank also all volunteers of the Mairie de Paris and the Conseil Général de Seine Saint Denis for their help in this study.

References

  1. Alhoniemi E, Himberg J, Parhankangas J, Vesanto J (2000) SOM toolbox. Laboratory of Computer and Information Science, Helsinki University of Technology, Helsinki. http://www.cis.hut.fi/projects/somtoolbox. Accessed September 2009
  2. Baguette M, Van Dyck H (2007) Landscape connectivity and animal behavior: functional grain as a key determinant for dispersal. Landscape Ecol 22:1117–1129CrossRefGoogle Scholar
  3. Bergerot B, Fontaine B, Renard M, Cadi A, Julliard R (2010) Preferences for exotic flowers do not promote urban life in butterflies. Landsc Urban Plan (in press)Google Scholar
  4. Bink BA (1992) Ecologische atlas van de Dagvlinders van Noordwest-Europa (Ecological atlas of the butterflies of NW Europe). Schuyt & Co, HaarlemGoogle Scholar
  5. Black D, Henderson JV (2003) Urban evolution in the USA. J Econ Geogr 3:343–373CrossRefGoogle Scholar
  6. Blair RB, Launer AE (1997) Butterfly diversity and human land use: species assemblages along an urban gradient. Biol Conserv 80:113–125CrossRefGoogle Scholar
  7. Boggs CL, Watt WB, Ehrlich PR (2003) Butterflies: ecology and evolution taking flight. University of Chicago, ChicagoGoogle Scholar
  8. Brown JKS, Freitas AVL (2002) Butterfly communities of urban forest fragments in Campinas, São Paulo, Brazil: structure, instability, environmental correlates, and conservation. J Insect Conserv 6:217–231CrossRefGoogle Scholar
  9. Cole LJ, McCracken DI, Downie IS, Dennis P, Foster GN, Waterhouse T, Murphy KJ, Griffin AL, Kennedy MP (2005) Comparing the effects of farming practices on ground beetle (Coleoptera: Carabidae) and spider (Araneae) assemblages of Scottish farmland. Biodivers Conserv 14:441–460CrossRefGoogle Scholar
  10. Cook LM, Dennis RLH, Hardy PB (2001) Butterfly-hostplant fidelity, vagrancy and measuring mobility from distribution maps. Ecography 24:497–504CrossRefGoogle Scholar
  11. Corne S, Murray T, Openshaw S, See L, Turton I (1999) Using computational intelligence techniques to model subglacial water systems. J Geogr Sci 1:37–60Google Scholar
  12. Dennis RLH, Hardy PB (2001) Loss rates of butterfly species with urban development. A test of atlas data and sampling artefacts at a fine scale. Biodivers Conserv 10:1831–1837CrossRefGoogle Scholar
  13. Desender K, Turin H (1989) Loss of habitats and changes in the composition of the ground and tiger beetle fauna in four West European Countries since 1950 (Coleoptera: Carabidae, Cicindelidae). Biol Conserv 48:277–294CrossRefGoogle Scholar
  14. Didham RK, Ghazoul J, Stork NE, Davis AJ (1996) Insects in fragmented forests: a functional approach. Trends Ecol Evol 11:255–260CrossRefGoogle Scholar
  15. Dobkins LH, Ioannides YM (2001) Spatial interactions among U.S. cities: 1900–1990. Reg Sci Urban Econ 31:701–732CrossRefGoogle Scholar
  16. Dufrêne M, Legendre P (1997) Species assemblages and indicator species: the need for a flexible asymmetrical approach. Ecol Monogr 67:345–366Google Scholar
  17. Dumbrell AJ, Hill JK (2005) Impacts of selective logging on canopy and ground assemblages of tropical forest butterflies: implications for sampling. Biol Conserv 125:123–131CrossRefGoogle Scholar
  18. Dunn OJ (1964) Multiple contrasts using rank sums. Technometrics 5:241–252CrossRefGoogle Scholar
  19. Eaton J, Eckstein Z (1997) Cities and growth: evidence from France and Japan. Reg Sci Urban Econ 27:443–474CrossRefGoogle Scholar
  20. ECOMOS (2003) Ecological soil occupation mode. http://www.iau-idf.fr/lile-de-france/un-portrait-par-les-chiffres/occupation-du-sol.html. Accessed February 2010
  21. Erhardt A (1985) Diurnal Lepidoptera: sensitive indicators of cultivated and abandoned grassland. J Appl Ecol 22:849–861CrossRefGoogle Scholar
  22. Fattorini S (2006) A new method to identify important conservation areas applied to the butterflies of the Aegean Islands (Greece). Anim Conserv 9:75–83CrossRefGoogle Scholar
  23. Feltwell J (1981) Large white butterfly: the biology, biochemistry, and physiology of Pieris Brassicae. D.R.W. Junk Publishers, BostonGoogle Scholar
  24. Frankie GW, Ehler LE (1978) Ecology of insects in urban environments. Annu Rev Entomol 23:367–387CrossRefGoogle Scholar
  25. Giraudel JL, Lek S (2001) A comparison of self-organizing map algorithm and some conventional statistical methods for ecological community ordination. Ecol Model 146:329–339CrossRefGoogle Scholar
  26. Giuliano WM, Accamando AK, McAdams EJ (2004) Lepidoptera-habitat relationships in urban parks. Urban Ecosyst 7:361–370CrossRefGoogle Scholar
  27. Goulson D (1993) Allozyme variation in the butterfly, Maniola jurtina (Lepidoptera, Satyrinae) (L): evidence for selection. Heredity 71:386–393CrossRefGoogle Scholar
  28. Hamer KC, Hill JK, Benedick S, Mustaffa N, Sherratt TN, Maryati M, Chey VK (2003) Ecology of butterflies in natural and selectively logged forests of northern Borneo: the importance of habitat heterogeneity. J Appl Ecol 40:150–162CrossRefGoogle Scholar
  29. Hanski I (1999) Metapopulation dynamics. Oxford University Press, OxfordGoogle Scholar
  30. Hanski I, Kuussaari M (1995) Butterfly metapopulation dynamics. In: Cappuccino N, Price PW (eds) Population dynamics: new approaches and synthesis. Academic Press, London, pp 149–171Google Scholar
  31. Hardy PB, Dennis RLH (1999) The impact of urban development on butterflies within a city region. Biodivers Conserv 8:1261–1279CrossRefGoogle Scholar
  32. Harrington R, Stork NE (1995) Insects in a changing environment. Academic Press, LondonGoogle Scholar
  33. Ibarra AA, Park YS, Brosse S, Reyjol Y, Lim P, Lek S (2005) Nested patterns of spatial diversity revealed for fish assemblages in a west European river. Ecol Freshw Fish 14:233–242CrossRefGoogle Scholar
  34. Ioannides YM, Overman HG (2003) Zipf’s law for cities: an empirical examination. Reg Sci Urban Econ 33:127–137CrossRefGoogle Scholar
  35. IUCN (2001) International Union for Conservation of Nature. http://www.iucn.org. Accessed March 2010
  36. Kenkel NC, Orloci L (1986) Applying metric and nonmetric multidimensional scaling to ecological studies: some new results. Ecology 64(4):919–928CrossRefGoogle Scholar
  37. Kohonen T (1982) Analysis of a simple self-organizing process. Biol Cybern 44:135–140CrossRefGoogle Scholar
  38. Kohonen T (2001) Self-organizing map. Springer, HeidelbergGoogle Scholar
  39. Kruskal WH, Wallis WA (1952) Use of ranks in one-criterion variance analysis. J Am Stat Assoc 47:583–621CrossRefGoogle Scholar
  40. Lasne E, Bergerot B, Lek S, Laffaille P (2007) Fish zonation and indicator species for the evaluation of the ecological status of rivers: example of the Loire basin (France). River Res Appl 23:1–14CrossRefGoogle Scholar
  41. Lek S, Guegan JF (1999) Artificial neural networks as a tool in ecological modelling, an introduction. Ecol Model 120:65–73CrossRefGoogle Scholar
  42. Lek S, Scardi M, Verdonschot PFM, Park YS, Descy JP (2005) Modelling community structure in freshwater ecosystems. Springer, New YorkCrossRefGoogle Scholar
  43. Leon-Cortés JL, Cowley MJR, Thomas CD (2000) The distribution and decline of a widespread butterfly Lycaena phlaeas in a pastoral landscape. Ecol Entomol 25:285–296CrossRefGoogle Scholar
  44. Leon-Cortés JL, Pérez-Espinoza F, Marin L, Molina-Martinez A (2004) Complex habitat requirements and conservation needs of the only extant Baroniinae swallowtail butterfly. Anim Conserv 7:241–250CrossRefGoogle Scholar
  45. Magurran AE (1988) Ecological diversity and its measurement. Croom Helm, LondonGoogle Scholar
  46. Manel S, Dias JM, Ormerod SJ (1999) Comparing discriminant analysis, neural networks and logistic regressions for predicting species’ distribution: a case study with an Himalayan river bird. Ecol Model 120:337–347CrossRefGoogle Scholar
  47. McDonnell MJ, Pickett STA (1990) Ecosystem structure and function along urban–rural gradients: an unexploited opportunity for ecology. Ecology 71:1232–1237CrossRefGoogle Scholar
  48. McIntyre S, Barrett GW (1992) Habitat variegation, an alternative to fragmentation. Conserv Biol 6:146–147CrossRefGoogle Scholar
  49. Mennechez G, Schtickzelle N, Baguette M (2003) Metapopulation dynamics of the bog fritillary butterfly: comparison of demographic parameters and dispersal between a continuous and a highly fragmented landscape. Landscape Ecol 18:279–291CrossRefGoogle Scholar
  50. Merckx T, Van Dyck H (2007) Habitat fragmentation affects habitat-finding ability of the speckled wood butterfly, Pararge aegeria L. Anim Behav 74:1029–1037CrossRefGoogle Scholar
  51. Nelson GS, Nelson SM (2001) Bird and butterfly communities associated with two types of urban riparian areas. Urban Ecosyst 5:95–108CrossRefGoogle Scholar
  52. New TR (1997) Are Lepidoptera an effective ‘umbrella group’ for biodiversity conservation? J Insect Conserv 1:5–12CrossRefGoogle Scholar
  53. Niell RS, Brussard PF, Murphy DD (2007) Butterfly community composition and oak woodland vegetation response to rural residential development. Landsc Urban Plan 81:235–245CrossRefGoogle Scholar
  54. Olden JD, Joy MK, Death RG (2006) Rediscovering the species in community-wide predictive modeling. Ecol Appl 16:1449–1460CrossRefPubMedGoogle Scholar
  55. Park YS, Grenouillet G, Esperance B, Lek S (2006) Stream fish assemblages and basin land cover in a river network. Sci Total Environ 365:140–153CrossRefPubMedGoogle Scholar
  56. Piscart C, Bergerot B, Laffaille P, Marmonier P (2010) Are amphipod invaders a threat to regional biodiversity? Biol Invasions 12:853–863CrossRefGoogle Scholar
  57. Pollard E, Yates TJ (1993) Monitoring butterflies for ecology and conservation. The British Butterfly Monitoring Scheme. Conservation Biology Series 1Google Scholar
  58. Roy DB, Rothery P, Brereton T (2007) Reduced-effort schemes for monitoring butterfly populations. J Appl Ecol 44:993–1000CrossRefGoogle Scholar
  59. Rubinoff D, Powell JA (2004) Conservation of fragmented small populations: endemic species persistence on California’s smallest channel island. Biodivers Conserv 13:2537–2550CrossRefGoogle Scholar
  60. Saarinen K, Valtonen A, Jantunen J, Saarnio S (2005) Butterflies and diurnal moths along road verges: does road type affect diversity and abundance? Biol Conserv 123:403–412CrossRefGoogle Scholar
  61. Savard JPL, Clergeau P, Mennechez G (2000) Biodiversity concepts and urban ecosystems. Landsc Urban Plan 48(3–4):131–142CrossRefGoogle Scholar
  62. Schmitt T, Rakosy L (2007) Changes of traditional agrarian landscapes and their conservation implications: a case study of butterflies in Romania. Divers Distrib 13:855–862CrossRefGoogle Scholar
  63. Somervuo P, Kohonen T (1999) Self organizing map and learning vector quantization for feature sequences. Neural Process Lett 10:151–159CrossRefGoogle Scholar
  64. Steffan-Dewenter I, Tscharntke T (2000) Butterfly community structure in fragmented habitats. Ecol Lett 3:449–456CrossRefGoogle Scholar
  65. Stevens VM, Turlure C, Baguette M (2010) A meta-analysis of dispersal in butterflies. Biol Rev (in press)Google Scholar
  66. Tolman T, Lewington R (1997) Butterflies of Britain and Europe. HarperCollins Ltd., London, 320 ppGoogle Scholar
  67. Vanreusel W, van Dyck H (2007) When functional habitat does not match vegetation types: a resource-based approach to map butterfly habitat. Biol Conserv 135:202–211CrossRefGoogle Scholar
  68. Vesanto J, Alhoniemi E (2000) Clustering of the self-organizing map. IEEE Trans Neural Netw 11:586–600CrossRefPubMedGoogle Scholar
  69. Ward JH (1963) Hierarchical grouping to optimize an objective function. J Am Stat Assoc 58:238–244CrossRefGoogle Scholar
  70. Wood BC, Pullin AS (2002) Persistence of species in a fragmented urban landscape: the importance of dispersal ability and habitat availability for grassland butterflies. Biodivers Conserv 11(8):1451–1468CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media B.V. 2010

Authors and Affiliations

  • Benjamin Bergerot
    • 1
  • Benoit Fontaine
    • 1
  • Romain Julliard
    • 1
  • Michel Baguette
    • 2
    • 3
  1. 1.CNRS-MNHN-PARIS VI ‘Conservation des Espèces, Restauration et Suivi des Populations’, CRBPOParisFrance
  2. 2.CNRS—MNHN, UMR-7179 ‘Mécanismes adaptatifs: des organismes aux communautés’BrunoyFrance
  3. 3.CNRS USR 2936 ‘Station d’écologie expérimentale du CNRS à Moulis’MoulisFrance

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