Biodiversity and Conservation

, Volume 19, Issue 3, pp 883–899 | Cite as

Sampling bias and the use of ecological niche modeling in conservation planning: a field evaluation in a biodiversity hotspot

  • Gabriel C. CostaEmail author
  • Cristiano Nogueira
  • Ricardo B. Machado
  • Guarino R. Colli
Original Paper


Ecological niche modeling (ENM) has become an important tool in conservation biology. Despite its recent success, several basic issues related to algorithm performance are still being debated. We assess the ability of two of the most popular algorithms, GARP and Maxent, to predict distributions when sampling is geographically biased. We use an extensive data set collected in the Brazilian Cerrado, a biodiversity hotspot in South America. We found that both algorithms give richness predictions that are very similar to other traditionally used richness estimators. Also, both algorithms correctly predicted the presence of most species collected during fieldwork, and failed to predict species collected only in very few cases (usually species with very few known localities, i.e., <5). We also found that Maxent tends to be more sensitive to sampling bias than GARP. However, Maxent performs better when sampling is poor (e.g., low number of data points). Our results indicates that ENM, even when provided with limited and geographically biased localities, is a very useful technique to estimate richness and composition of unsampled areas. We conclude that data generated by ENM maximize the utility of existing biodiversity data, providing a very useful first evaluation. However, for reliable conservation decisions ENM data must be followed by well-designed field inventories, especially for the detection of restricted range, rare species.


Biodiversity hotspots Brazil Cerrado Conservation planning Ecological niche modeling GARP Maxent Sampling bias Species distribution Squamates 



We thank D. Shepard and A. T. Peterson for comments on the manuscript. Fieldwork was funded by Conservation International—Brazil, and field support was provided by Pequi, a Brazilian nongovernmental organization. Work on PNCM was authorized by IBAMA permit # 13204-1. We thank, P. Valdujo, S. Balbino, R. Recoder, and R. Bosque for help during fieldwork. This study was submitted in partial fulfilment of GCC’s PhD degree at the University of Oklahoma. The species locality database was assembled as part of doctoral studies conducted by CN, supported by a FAPESP fellowship (# 02/00015-3). We thank J. Caldwell, M. Kaspari, J. Kelly, T. Rashed, and L. Vitt, for serving on GCC’s doctoral committee and providing comments. GCC is supported by a Fulbright/CAPES PhD fellowship (15053155-2018/04-7). GRC by CNPq grant (# 302343/88-1). Portions of this research were supported by a National Science Foundation grant to Laurie J. Vitt and Janalee P. Caldwell (DEB-0415430).


  1. Anderson RP, Peterson AT, Gómez-Laverde M (2002) Using niche-based GIS modeling to test geographic predictions of competitive exclusion and competitive release in South American pocket mice. Oikos 98:3–16CrossRefGoogle Scholar
  2. Anderson RP, Lew D, Peterson AT (2003) Evaluating predictive models of species’ distributions: criteria for selecting optimal models. Ecol Model 162:211–232CrossRefGoogle Scholar
  3. Araújo MB, Luoto M (2007) The importance of biotic interactions for modelling species distributions under climate change. Global Ecol Biogeogr 16:743–753CrossRefGoogle Scholar
  4. Araújo MB, Rahbek C (2006) How does climate change affect biodiversity? Science 313:1396–1397CrossRefPubMedGoogle Scholar
  5. Austin MP (2007) Species distribution models and ecological theory: a critical assessment and some possible new approaches. Ecol Model 200:1–19CrossRefGoogle Scholar
  6. Brooks TM, da Fonseca GAB, Rodrigues ASL (2004) Species, data, and conservation planning. Conserv Biol 18:1682–1688CrossRefGoogle Scholar
  7. Chao A, Hwang WH, Chen YC et al (2000) Estimating the number of shared species in two communities. Stat Sin 10:227–246Google Scholar
  8. Chazdon RL, Colwell RK, Denslow JS et al (1998) Statistical methods for estimating species richness of woody regeneration in primary and secondary rain forests of NE Costa Rica. In: Dallmeier F, Comiskey JA (eds) Forest biodiversity research, monitoring, modeling: conceptual background, old world case studies. Parthenon Publishing, Paris, pp 285–309Google Scholar
  9. Colli GR, Bastos RP, Araújo AFB (2002) The character and dynamics of the Cerrado herpetofauna. In: Oliveira PS, Marquis RJ (eds) The Cerrados of Brazil: ecology, natural history of a neotropical Savanna. Columbia University Press, New YorkGoogle Scholar
  10. Colli GR, Giugliano LG, Mesquita DO et al (2009) A new species of Cnemidophorus from the Jalapão region, in the central Brazilian Cerrado. Herpetologica 65:311–327CrossRefGoogle Scholar
  11. Colwell RK (2005) EstimateS: statistical estimation of species richness and shared species from samples. User’s guide and application published at
  12. Colwell RK, Mao CX, Chang J (2004) Interpolating, extrapolating, and comparing incidence-based species accumulation curves. Ecology 85:2717–2727CrossRefGoogle Scholar
  13. Costa GC, Nogueira CC, Machado RB et al (2007) Squamate richness in the Brazilian Cerrado and its environmental–climatic associations. Divers Distrib 13:714–724CrossRefGoogle Scholar
  14. Costa GC, Wolfe C, Shepard DB et al (2008) Detecting the influence of climatic variables on species’ distributions: a test using GIS niche-based models along a steep longitudinal environmental gradient. J Biogeogr 35:637–646CrossRefGoogle Scholar
  15. Díaz-Francés E, Soberón J (2005) Statistical estimation and model selection of species-accumulation functions. Conserv Biol 19:569–573CrossRefGoogle Scholar
  16. Domínguez-Domínguez O, Martinez-Meyer E, Zambrano L et al (2006) Using ecological niche modeling as a conservation tool for freshwater species: live-bearing fishes in central Mexico. Conserv Biol 20:1730–1739CrossRefPubMedGoogle Scholar
  17. Duellman WE (1978) The biology of an equatorial herpetofauna in Amazonian Ecuador. Miscellaneous Publications of the University of Kansas, Museum of Natural History, Lawrence, pp 1–352Google Scholar
  18. Eken G, Bennun L, Brooks TM et al (2004) Key biodiversity areas as site conservation targets. Bioscience 54:1110–1118CrossRefGoogle Scholar
  19. Elith J, Graham CH, Anderson RP et al (2006) Novel methods improve prediction of species’ distributions from occurrence data. Ecography 29:129–151CrossRefGoogle Scholar
  20. 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
  21. Filho GAP, Montigelli GG (2006) Geographic distribution: Hydrodynastes gigas. Herpetol Rev 37:497Google Scholar
  22. França FGR, Araújo AFB (2007) Are there co-occurence patterns that structure snake communities in central Brazil? Braz J Biol 67:33–40CrossRefPubMedGoogle Scholar
  23. Freitas MA, Silva TFS, Rodriguez MT (2007) Geographic distribution: Chironius quadrilineatus. Herpetol Rev 38:354Google Scholar
  24. Garcia A (2006) Using ecological niche modelling to identify diversity hotspots for the herpetofauna of Pacific lowlands and adjacent interior valleys of Mexico. Biol Conserv 130:25–46CrossRefGoogle Scholar
  25. Guisan A, Broennimann O, Engler R et al (2006) Using niche-based models to improve the sampling of rare species. Conserv Biol 20:501–511CrossRefPubMedGoogle Scholar
  26. Guisan A, Graham CH, Elith J et al (2007a) Sensitivity of predictive species distribution models to change in grain size. Divers Distrib 13:332–340CrossRefGoogle Scholar
  27. Guisan A, Zimmermann NE, Elith J et al (2007b) What matters for predicting the occurrences of trees: techniques, data, or species’ characteristics? Ecol Monogr 77:615–630CrossRefGoogle Scholar
  28. Heikkinen RK, Luoto M, Virkkala R et al (2007) Biotic interactions improve prediction of boreal bird distributions at macro-scales. Global Ecol Biogeogr 16:754–763CrossRefGoogle Scholar
  29. 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 29:773–785CrossRefGoogle Scholar
  30. Hijmans RJ, Graham CH (2006) The ability of climate envelope models to predict the effect of climate change on species distributions. Global Change Biol 12:2272–2281CrossRefGoogle Scholar
  31. Hijmans RJ, Cameron SE, Parra JL et al (2005) Very high resolution interpolated climate surfaces for global land areas. Int J Climatol 25:1965–1978CrossRefGoogle Scholar
  32. Hortal J, Lobo JM, Jimenez-Valverde A (2007) Limitations of biodiversity databases: case study on seed-plant diversity in Tenerife, Canary Islands. Conserv Biol 21:853–863CrossRefPubMedGoogle Scholar
  33. Kadmon R, Farber O, Danin A (2004) Effect of roadside bias on the accuracy of predictive maps produced by bioclimatic models. Ecol Appl 14:401–413CrossRefGoogle Scholar
  34. Klink CA, Machado RB (2005) Conservation of the Brazilian Cerrado. Conserv Biol 19:707–713CrossRefGoogle Scholar
  35. Lassueur T, Joost S, Randin CF (2006) Very high resolution digital elevation models: do they improve models of plant species distribution? Ecol Model 198:139–153CrossRefGoogle Scholar
  36. Lobo JM, Jiménez-Valverde A, Real R (2008) AUC: a misleading measure of the performance of predictive distribution models. Global Ecol Biogeogr 17:145–151CrossRefGoogle Scholar
  37. Loiselle BA, Jorgensen PM, Consiglio T et al (2008) Predicting species distributions from herbarium collections: does climate bias in collection sampling influence model outcomes? J Biogeogr 35:105–116Google Scholar
  38. Lomolino MV (2004) Conservation biogeography. In: Lomolino MV, Heaney LR (eds) Frontiers of biogeography: new directions in the geography of nature. Sinauer, Sunderland, pp 293–296Google Scholar
  39. Machado RB, Ramos Neto MB, Pereira PGP et al (2004) Estimativas de perda da área do Cerrado brasileiro. Conservation International Brasilia, Brasilia, DFGoogle Scholar
  40. Mittermeier RA, Gil PR, Hoffman M et al (2005) Hotspots revisited: Earth’s biologically richest and most endangered terrestrial ecoregions, 2nd edn. Conservation International, USAGoogle Scholar
  41. Myers N (2003) Biodiversity hotspots revisited. Bioscience 53:916–917CrossRefGoogle Scholar
  42. Myers N, Mittermeier RA, Mittermeier CG et al (2000) Biodiversity hotspots for conservation priorities. Nature 403:853–858CrossRefPubMedGoogle Scholar
  43. Nogueira C (2001) New records of squamate reptiles in central Brazilian Cerrado II: Brasília region. Herpetol Rev 32:285–287Google Scholar
  44. Nogueira C, Rodrigues MT (2006) The genus Stenocercus (Squamata: Tropiduridae) in extra-amazonian Brazil, with the description of two new species. South Am J Herpetol 1:149–165CrossRefGoogle Scholar
  45. Nogueira C, Colli GR, Martins M (2009) Local richness and distribution of the lizard fauna in natural habitat mosaics of the Brazilian Cerrado. Austral Ecol 34:83–96CrossRefGoogle Scholar
  46. Parra JL, Graham CC, Freile JF (2004) Evaluating alternative data sets for ecological niche models of birds in the Andes. Ecography 27:350–360CrossRefGoogle Scholar
  47. Pawar S, Koo MS, Kelley C et al (2007) Conservation assessment and prioritization of areas in Northeast India: priorities for amphibians and reptiles. Biol Conserv 136:346–361CrossRefGoogle Scholar
  48. Pearson RG, Raxworthy CJ, Nakamura M et al (2007) Predicting species distributions from small numbers of occurrence records: a test case using cryptic geckos in Madagascar. J Biogeogr 34:102–117CrossRefGoogle Scholar
  49. Peterson AT (2006) Uses and requirements of ecological niche models and related distributional models. Biodivers Inform 3:59–72Google Scholar
  50. Peterson AT, Nakazawa Y (2008) Environmental data sets matter in ecological niche modelling: an example with Solenopsis invicta and Solenopsis richteri. Global Ecol Biogeogr 17:135–144Google Scholar
  51. Peterson AT, Vieglais DA (2001) Predicting species invasions using ecological niche modeling: new approaches from bioinformatics attack a pressing problem. Bioscience 51:363–371CrossRefGoogle Scholar
  52. Peterson AT, Papes M, Eaton M (2007) Transferability and model evaluation in ecological niche modeling: a comparison of GARP and Maxent. Ecography 30:550–560Google Scholar
  53. Peterson AT, Papes M, Soberon J (2008) Rethinking receiver operating characteristics analysis applications in ecological niche modeling. Ecol Model 213:63–72CrossRefGoogle Scholar
  54. Phillips SJ, Dudik M (2008) Modeling of species distributions with Maxent: new extensions and a comprehensive evaluation. Ecography 31:161–175CrossRefGoogle Scholar
  55. Phillips SJ, Dudik M, Shapire RE (2004) A maximum entropy approach to species distribution modeling. In: Greiner R, Schuurmans D (eds) Proceedings of the 21st international conference on machine learning. ACM Press Banff, Canada, pp 655–662Google Scholar
  56. Phillips SJ, Anderson RP, Schapire RE (2006) Maximum entropy modeling of species geographic distributions. Ecol Model 190:231–259CrossRefGoogle Scholar
  57. Poyry J, Luoto M, Heikkinen RK et al (2008) Species traits are associated with the quality of bioclimatic models. Global Ecol Biogeogr 17:403–414CrossRefGoogle Scholar
  58. R Development Core Team (2008) R: a language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria., ISBN 3-900051-07-0
  59. Raes N, ter Steege H (2007) A null-model for significance testing of presence-only species distribution models. Ecography 30:727–736CrossRefGoogle Scholar
  60. Raxworthy CJ, Martinez-Meyer E, Horning N et al (2003) Predicting distributions of known and unknown reptile species in Madagascar. Nature 426:837–841CrossRefPubMedGoogle Scholar
  61. Raxworthy CJ, Ingram CM, Rabibisoa N et al (2007) Applications of ecological niche modeling for species delimitation: a review and empirical evaluation using day geckos (Phelsuma) from Madagascar. Syst Biol 56:907–923CrossRefPubMedGoogle Scholar
  62. Rodrigues MT, Pavan D, Curcio FF (2007) Two new species of lizards of the genus Bachia (Squamata, Gymnophthalmidae) from central Brazil. J Herpetol 41:545–553CrossRefGoogle Scholar
  63. Rodrigues MT, Camacho A, Nunes PMS et al (2008) A new species of the lizard genus Bachia (Squamata: Gymnophthalmidae) from the cerrados of central Brazil. Zootaxa 1875:39–50Google Scholar
  64. Segurado P, Araújo MB (2004) An evaluation of methods for modelling species distributions. J Biogeogr 31:1555–1568CrossRefGoogle Scholar
  65. Silveira AL (2007) Geographic distribution: Amphisbaena fuliginosa. Herpetol Rev 38:481Google Scholar
  66. Soberón J, Llorente J (1993) The use of species accumulation functions for the prediction of species richness. Conserv Biol 7:480–488CrossRefGoogle Scholar
  67. Soberón JM, Llorente JB, Onate L (2000) The use of specimen-label databases for conservation purposes: an example using Mexican Papilionid and Pierid butterflies. Biodivers Conserv 9:1441–1466CrossRefGoogle Scholar
  68. Soberón J, Jiménez R, Golubov J et al (2007) Assessing completeness of biodiversity databases at different spatial scales. Ecography 30:152–160Google Scholar
  69. Stockwell DRB, Noble IR (1992) Induction of sets of rules from animal distribution data: a robust and informative method of data analysis. Math Comput Simul 33:385–390CrossRefGoogle Scholar
  70. Stockwell DRB, Peterson AT (2002) Effects of sample size on accuracy of species distribution models. Ecol Model 148:1–13CrossRefGoogle Scholar
  71. Strüssmann C, Carvalho MA (1998) New herpetological records for the state of Mato Grosso, western Brazil. Herpetol Rev 29:183–185Google Scholar
  72. Sullivan BK (1981) Distribution and relative abundance of snakes along a transect in California. J Herpetol 15:245–246CrossRefGoogle Scholar
  73. Trivedi MR, Berry PM, Morecroft MD et al (2008) Spatial scale affects bioclimate model projections of climate change impacts on mountain plants. Global Change Biol 14:1089–1103CrossRefGoogle Scholar
  74. Valdujo PH, Nogueira CC, Baumgarten L et al (2009) Squamate reptiles from Parque Nacional das Emas and surroundings, Cerrado of Central Brazil. Check List 5:405–417Google Scholar
  75. Whittaker RJ, Araújo MB, Paul J et al (2005) Conservation biogeography: assessment and prospect. Divers Distrib 11:3–23CrossRefGoogle Scholar
  76. Zweig MH, Campbell G (1993) Receiver-operating characteristics (ROC) plots—a fundamental evaluation tool in clinical medicine. Clin Chem 39:561–577PubMedGoogle Scholar

Copyright information

© Springer Science+Business Media B.V. 2009

Authors and Affiliations

  • Gabriel C. Costa
    • 1
    Email author
  • Cristiano Nogueira
    • 2
  • Ricardo B. Machado
    • 2
  • Guarino R. Colli
    • 2
  1. 1.Centro de Biociências, Departamento de Botânica, Ecologia e ZoologiaUniversidade Federal do Rio Grande do NorteNatalBrazil
  2. 2.Departamento de ZoologiaUniversidade de BrasíliaBrasíliaBrazil

Personalised recommendations