Biodiversity and Conservation

, Volume 17, Issue 4, pp 873–881 | Cite as

Database records as a surrogate for sampling effort provide higher species richness estimations

Original Paper


The compilation of all the available taxonomic and distributional information on the species present in a territory frequently generates a biased picture of the distribution of biodiversity due to the uneven distribution of the sampling effort performed. Thus, quality protocol assessments such as those proposed by Hortal et al. (Conservation Biology 21:853–863, 2007) must be done before using this kind of information for basic and applied purposes. The discrimination of localities that can be considered relatively well-surveyed from those not surveyed enough is a key first step in this protocol and can be attained by the previous definition of a sampling effort surrogate and the calculation of survey completeness using different estimators. Recently it has been suggested that records from exhaustive databases can be used as a sampling-effort surrogate to recognize probable well-surveyed localities. In this paper, we use an Iberian dung beetle database to identify the 50 × 50 km UTM cells that appear to be reliably inventoried, using both data derived from standardized sampling protocols and database records as a surrogate for sampling effort. Observed and predicted species richness values in the shared cells defined as well-surveyed by both methods suggest that the use of database records provides higher species richness values, which are proportionally greater in the richest localities by the inclusion of rare species.


Biodiversity databases Sampling effort assessment Database records Survey completeness Species accumulation curves 



This paper was supported by the Spanish MEC project CGL2004-0439/BOS, a Fundación BBVA Project, and The European Distributed Institute of Taxonomy (EDIT) project.


  1. Adler PB, Lauenroth WK (2003) The power of time: spatiotemporal scaling of species diversity. Ecol Lett 6:749–756CrossRefGoogle Scholar
  2. Anderson RP (2003) Real vs artefactual absences in species distributions: tests for Oryzomys albigularis (Rodentia: Muridae) in Venezuela. J Biogeogr 30:591–605CrossRefGoogle Scholar
  3. Brooks T, da Fonseca GAB, Rodrigues ASL (2004) Species, data, and conservation planning. Conserv Biol 18:1682–1688CrossRefGoogle Scholar
  4. Chiarucci A, Maccherini S, De Dominicis V (2001) Evaluation and monitoring of the flora in a nature reserve by estimation methods. Biol Conserv 101:305–314CrossRefGoogle Scholar
  5. Colwell RK (2000) EstimateS: statistical estimation of species richness and shared species from samples (Software and User’s Guide). Version 6.0b1. Available at
  6. Colwell RK, Coddington JA (1994) Estimating terrestrial biodiversity through extrapolation. Philos Trans R Soc Lond B Biol Sci 345:101–118PubMedCrossRefGoogle Scholar
  7. Chazdon RL, Colwell RK, Denslow JS, Guariguata MR (1998) Statistical methods for estimating species richness of woody regeneration in primary and secondary rain forests of north-eastern Costa Rica. In: Dallmeir F, Comiskey JA (eds) Forest biodiversity research, monitoring and modelling. Conceptual background and Old World case studies. Parthenon Publishing, Paris, France, pp 285–309Google Scholar
  8. Dennis RLH (2001) Progressive bias in species status is symptomatic of fine-grained mapping units subject to repeated sampling. Biodivers Conserv 10:483–494CrossRefGoogle Scholar
  9. Dennis RLH, Hardy PB (1999) Targeting squares for survey: predicting species richness and incidence for a butterfly atlas. Global Ecol Biogeogr Lett 8:443–454CrossRefGoogle Scholar
  10. Dennis RLH, Thomas CD (2000) Bias in butterfly distribution maps: the influence of hot spots and recorder’s home range. J Insect Conserv 4:73–77CrossRefGoogle Scholar
  11. Dennis RLH, Sparks TH, Hardy PB (1999) Bias in butterfly distribution maps: the effects of sampling effort. J Insect Conserv 3:33–42CrossRefGoogle Scholar
  12. Dennis RLH, Shreeve TG, Isaac NJB, Roy DB, Hardy PB, Fox R, Asher J (2006) The effects of visual apparency on bias in butterfly recording and monitoring. Biol Conserv 128:486–492CrossRefGoogle Scholar
  13. Faith D (2002) Those complementarity analysis do not reveal extent of conservation conflict in Africa. Science debate.
  14. Ferrier S (2002) Mapping spatial pattern in biodiversity for regional conservation planning: where to from here? Syst Biol 51:331–363PubMedCrossRefGoogle Scholar
  15. Funk VA, Richardson KS, Ferrier S (2005) Survey-gap analysis in expeditionary research: where do we go from here? Biol J Linn Soc Lond 85:549–567CrossRefGoogle Scholar
  16. Gaston KJ (1994) Rarity. Chapman & Hall, LondonGoogle Scholar
  17. GBIF (2003) Global biodiversity information facility strategic plan. Available at
  18. Gotelli NJ, Collwell RK (2001) Quantifying biodiversity: procedures and pitfalls in the measurement and comparison of species richness. Ecol Lett 4:379–391CrossRefGoogle Scholar
  19. Graham CH, Ferrier S, Huettman F, Moritz C, Peterson AT (2004) New developments in museum-based informatics and applications in biodiversity analysis. Trends Ecol Evol 19:497–503PubMedCrossRefGoogle Scholar
  20. Gu W, Swihart RK (2004) Absent or undetected? Effects of non-detection of species occurrence on wildlife-habitat models. Biol Conserv 116:195–203CrossRefGoogle Scholar
  21. Guisan A, Zimmermann NE (2000) Predictive habitat distribution models in ecology. Ecol Modell 135:147–186CrossRefGoogle Scholar
  22. Guralnick RP, Hill AW, Lane M (2007) Towards a collaborative, global infrastructure for biodiversity assessment. Ecol Lett 10:663–672PubMedCrossRefGoogle Scholar
  23. Hortal J, Lobo JM (2005) An ED-based protocol for optimal sampling of biodiversity. Biodivers Conserv 14:2913–2947CrossRefGoogle Scholar
  24. Hortal J, Lobo JM, Martín-Piera F (2001) Forecasting insect species richness scores in poorly surveyed territories: the case of the Portuguese dung beetles (Col. Scarabaeinae). Biodivers Conserv 10:1343–1367CrossRefGoogle Scholar
  25. Hortal J, Borges PA, Gaspar C (2006) Evaluating the performance of species richness estimators: sensitivity to sample grain size. J Anim Ecol 75:274–287PubMedCrossRefGoogle Scholar
  26. Hortal J, Lobo JM, Jiménez-Valverde A (2007) Limitations of biodiversity databases: case study on seed-plant diversity in Tenerife (Canary Islands). Conserv Biol 21:853–863PubMedCrossRefGoogle Scholar
  27. Kadmon R, Oren F, Avinoam D (2004) Effect of roadside bias on the accuracy of predictive maps produced by bioclimatic models. Ecol Appl 14:401–413CrossRefGoogle Scholar
  28. Lamoreux JF, Morrison JC, Ricketts TH, Olson DM, Dinerstein E, McKnight MW, Shugart HH (2005) Global tests of biodiversity concordance and the importance of endemism. Nature 440:212PubMedCrossRefGoogle Scholar
  29. Lobo JM (2008) More complex distribution models or more representative data? Biodiv Inform (in press)Google Scholar
  30. Lobo JM, Martín-Piera F (2002) Searching for a predictive model for Iberian dung beetle species richness based on spatial and environmental variables. Conserv Biol 16:158–173CrossRefGoogle Scholar
  31. Lobo JM, Baselga A, Hortal J, Jiménez-Valverde A, Gómez JF (2007) How does the knowledge about the spatial distribution of Iberian dung beetle species accumulates over time? Divers Distrib 13:772–780CrossRefGoogle Scholar
  32. Lyons KG, Brigham CA, Traut BH, Schwartz MW (2005) Rare species and ecosystem functioning. Conserv Biol 19:1019–1024CrossRefGoogle Scholar
  33. Martín-Piera F, Lobo JM (2003) Database records as a sampling effort surrogate to predict spatial distribution of insects in either poorly or unevenly surveyed areas. Acta Entomol Ibérica Macaronésica 1:23–35Google Scholar
  34. Martín-Piera F, López-Colón JI (2000) Fauna Ibérica, vol 14. In: Ramos MA et al (eds) Coleoptera, Scarabaeoidea 1. Museo Nacional de Ciencias Naturales, CSIC, Madrid, 526 ppGoogle Scholar
  35. Martínez-Meyer E (2005) Climate change and biodiversity: some considerations in forecasting shifts in species potential distributions. Biodivers Inform 2:42–55Google Scholar
  36. Neldner VJ, Crossley DC, Cofinas M (1995) Using Geographic Information Systems (GIS) to determine the adequacy of sampling in vegetation surveys. Biol Conserv 73:1–17CrossRefGoogle Scholar
  37. Nelson BW, Ferreira CAC, da Silva MF, Kawasaki ML (1990) Endemism centers, refugia and botanical collection density in Brazilian Amazonia. Nature 345:714–716CrossRefGoogle Scholar
  38. Parnell JAN, Simpson DA, Moat J, Kirkup DW, Chantaranothai P, Boyce PC, Bygrave P, Dransfield S, Jebb MHP, Macklin J, Meade C, Middleton DJ, Muasya AM, Prajaksood A, Pendry CA, Poomar R, Suddee S, Wilkin P (2003) Plant collecting spread and densities: their potential impact on biogeographical studies in Thailand. J Biogeogr 30:193–209CrossRefGoogle Scholar
  39. Peterson AT, Slade NA (1998) Extrapolating inventory results into biodiversity estimates and the importance of stopping rules. Divers Distrib 4:95–105CrossRefGoogle Scholar
  40. Peterson AT, Navarro-Sigüenza AG, Benítez-Díaz H (1998) The need for continued scientific collecting: a geographic analysis of Mexican bird specimens. Ibis 140:288–294Google Scholar
  41. Pulliam HR (1988) Sources, sinks and population regulation. Am Nat 132:652–661CrossRefGoogle Scholar
  42. Pulliam HR (2000) On the relationship between niche and distribution. Ecol Lett 3:349–361CrossRefGoogle Scholar
  43. Reddy S, Dávalos LM (2003) Geographical sampling bias and its implications for conservation priorities in Africa. J Biogeogr 30:1719–1727CrossRefGoogle Scholar
  44. Reutter BA, Helfer V, Hirzel AH, Vogel P (2003) Modelling habitat-suitability using museum collections: an example with three sympatric Apodemus species from the Alps. J Biogeogr 30:581–590Google Scholar
  45. Ricklefs RE, Schluter D (1993) Species diversity: regional and historical influences. In: Ricklefs RE, Schluter D (eds) Species diversity in ecological communities. University of Chicago Press, Chicago, pp 350–363Google Scholar
  46. Romo H, García-Barros E (2005) Distribución e intensidad de los estudios faunísticos sobre mariposas diurnas en la Península Ibérica e Islas Baleares (Lepidoptera, Papilionoidea y Hesperoidea). Graellsia 61:37–50Google Scholar
  47. Romo H, García-Barros E, Lobo JM (2006) Identifying recorder-induced geographic bias in an Iberian butterfly database. Ecography 29:873–885CrossRefGoogle Scholar
  48. Soberón J, Llorente J (1993) The use of species accumulation functions for the prediction of species richness. Conserv Biol 7:480–488CrossRefGoogle Scholar
  49. Soberón J, Peterson AT (2004) Biodiversity informatics: managing and applying primary biodiversity data. Philos Trans R Soc Lond B 359:689–698CrossRefGoogle Scholar
  50. Soberón J, Llorente J, Benítez H (1996) An international view of national biological surveys. Ann Miss Bot Garden 83:562–573CrossRefGoogle Scholar
  51. Soberón JM, Llorente J, Oñate 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
  52. Soberón J, Jiménez R, Golubov J, Koleff P (2007) Assessing completeness of biodiversity databases at different spatial scales. Ecography 30:152–160Google Scholar
  53. Williams PH, Margules CR, Hilbert DW (2002) Data requirements and data sources for biodiversity priority area selection. J Bioscience 27:327–338CrossRefGoogle Scholar
  54. Zaniewski AE, Lehmann A, Overton JM (2002) Predicting species spatial distributions using presence-only data: a case study of native New Zealand ferns. Ecol Modell 157:261–280CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media B.V. 2008

Authors and Affiliations

  1. 1.Departamento de Biodiversidad y Biología EvolutivaMuseo Nacional de Ciencias Naturales (CSIC)MadridSpain

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