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Evaluating the sampling bias in pattern of subterranean species richness: combining approaches

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Abstract

We investigated the pattern of species richness of obligate subterranean (troglobiotic) beetles in caves in the northwestern Balkans, given unequal and biased sampling. On the regional scale, we modeled the relationship between species numbers and sampling intensity using an asymptotic Clench (Michaelis–Menten) function. On the local scale, we calculated Chao 2 species richness estimates for 20 × 20 km grid cells, and investigated the distribution of uniques, species found in only one cave within the grid cell. Cells having high positive residuals, those with above average species richness than expected according to the Clench function, can be considered true hotspots. They were nearly identical to the observed areas of highest species richness. As sampling intensity in a grid cell increases the expected number of uniques decreases for any fixed number of species in the grid cell. High positive residuals show above average species richness for a certain level of sampling intensity within a cell, so further sampling has the most potential for additional species. In some cells this was supported by high numbers of uniques, also indicating insufficient sampling. Cells with low negative residuals have fewer species than would be expected, and some of them also had a low number of uniques, both indicating sufficient sampling. By combining different analyses in a novel way we were able to evaluate observed species richness pattern as well as identify, where further sampling would be most beneficial. Approach we demonstrate is of broad interest to study of biota with high levels of endemism, small distribution ranges and low catchability.

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References

  • Brose U (2002) Estimating species richness of pitfall catches by non-parametric estimators. Pedobiologia 46:101–107

    Article  Google Scholar 

  • Chao A (1984) Non-parametric estimation of the number of classes in a population. Scand J Stat 11:265–270

    Google Scholar 

  • Christman MC, Culver DC (2001) The relationship between cave biodiversity and available habitat. J Biogeogr 28:367–380

    Article  Google Scholar 

  • Christman MC, Culver DC, Madden M et al (2005) Patterns of endemism of the eastern North American cave fauna. J Biogeogr 32:1441–1452

    Article  Google Scholar 

  • Clench HK (1979) How to make regional lists of butterflies: some thoughts. J Lepid Soc 33:216–231

    Google Scholar 

  • Colwell RK (2005) EstimateS: statistical estimation of species richness and shared species from samples. Version 7.5. User’s guide and application. http://purl.oclc.org/estimates. Accessed 10 Dec 2009

  • Colwell RK, Coddington JA (1994) Estimating terrestrial biodiversity through extrapolation. Philos Trans R Soc B 345:101–118

    Article  CAS  Google Scholar 

  • Culver DC, Pipan T (2009) The biology of caves and other subterranean habitats. Oxford University Press, Oxford

    Google Scholar 

  • Culver DC, Christman MC, Sket B et al (2004) Sampling adequacy in an extreme environment: species richness patterns in Slovenian caves. Biodivers Conserv 13:1209–1229

    Article  Google Scholar 

  • Culver DC, Deharveng L, Bedos A et al (2006) The mid-latitude biodiversity ridge in terrestrial cave fauna. Ecography 29:120–128

    Article  Google Scholar 

  • Deharveng L, Stoch F, Gibert J et al (2009) Groundwater biodiversity in Europe. Freshw Biol 54:709–726

    Article  Google Scholar 

  • Dennis RLH, Thomas CD (2000) Bias in butterfly distribution maps: the influence of hot spots and recorder’s home range. J Insect Conserv 4:73–77

    Article  Google Scholar 

  • Dole-Olivier MJ, Castellarini F, Coineau N et al (2009) Towards and optimal sampling strategy to assess groundwater biodiversity: comparison across six European regions. Freshw Biol 54:777–796

    Article  Google Scholar 

  • Elphick CS (1997) Correcting avian richness estimates for unequal sample effort in atlas studies. Ibis 139:189–190

    Article  Google Scholar 

  • Gotelli NJ, Colwell RK (2001) Quantifying biodiversity: procedures and pitfalls in the measurement and comparison of species richness. Ecol Lett 4:379–391

    Article  Google Scholar 

  • Graham CH, Hijmans RJ (2006) A comparison of methods for mapping species ranges and species richness. Glob Ecol Biogeogr 15:578–587

    Article  Google Scholar 

  • Harrison JA, Martinez P (1995) Measurement and mapping of avian diversity in southern Africa: implications for conservation planning. Ibis 137:410–417

    Article  Google Scholar 

  • Hortal J, Garcia-Pereira P, Garcia-Barros E (2004) Butterfly species richness in mainland Portugal: predictive models of geographic distribution patterns. Ecography 27:68–82

    Article  Google Scholar 

  • Jeannel R (1924) Monographie des Bathyscinae. Arch Zool Exp Gen 63:1–436

    Google Scholar 

  • Jeannel R (1928) Monographie des Trechinae. Morphologie comparee et distribution geographique d’un groupe de Coleopteres. Troisieme Livraison. Les Trechini cavernicoles. J Entomol 35:1–800

    Google Scholar 

  • Lamoreux J (2004) Stygobites are more wide-ranging than troglobites. J Cave Karst Stud 66:18–19

    Google Scholar 

  • Lennon JL, Koleff P, Greenwood JJD et al (2004) Contribution of rarity and commonness to patterns of species richness. Ecol Lett 7:81–87

    Article  Google Scholar 

  • Lobo JM, Martin-Piera F (2002) For a predictive model for species richness of Iberian dung beetle based on spatial and environmental variables. Conserv Biol 16:158–173

    Article  Google Scholar 

  • Magurran AE (2004) Measuring biological diversity. Blackwell, London

    Google Scholar 

  • Malard F, Boutin C, Camacho AI et al (2009) Diversity patterns of stygobiotic crustaceans across multiple spatial scales in Europe. Freshw Biol 54:756–776

    Article  Google Scholar 

  • McIntire EJB, Fajardo A (2009) Beyond description: the active and effective way to infer processes from spatial patterns. Ecology 90:46–56

    Article  PubMed  Google Scholar 

  • Poulin R (1995) Phylogeny, ecology, and the richness of parasite communities in vertebrates. Ecol Monogr 65:283–302

    Article  Google Scholar 

  • Poulin R, Rohde K (1997) Comparing the richness of metazoan ectoparasite communities of marine fishes: controlling for host phylogeny. Oecologia 110:278–283

    Article  Google Scholar 

  • Reddy S, Dávalos LM (2003) Geographical sampling bias and its implications for conservation priorities in Africa. J Biogeogr 30:1719–1727

    Article  Google Scholar 

  • Sket B (1999) The nature of biodiversity in hypogean waters and how it is endangered. Biodivers Conserv 8:1319–1338

    Article  Google Scholar 

  • Sket B (2005) Dinaric karst, diversity. In: Culver DC, White WB (eds) Encyclopedia of caves. Elsevier Academic Press, Amsterdam

    Google Scholar 

  • Sket B (2008) Can we agree on an ecological classification of subterranean animals? J Nat Hist 42:1549–1563

    Article  Google Scholar 

  • Sket B, Paragamian K, Trontelj P (2004) A census of the obligate subterranean fauna of the Balkan peninsula. In: Griffiths HI, Kryštufek B, Reed JM (eds) Balkan biodiversity, pattern and process in Europe’s biodiversity hotspot. Kluwer Academic Publishers, Dordrecht

    Google Scholar 

  • Smith DR, Brown JA, Lo NCH (2004) Application of adaptive sampling to biological populations. In: Thomson WL (ed) Sampling rare or elusive species. Concepts, designs, and techniques for estimating population parameters. Island Press, Washington, DC

    Google Scholar 

  • Soberón JM, Llorente JB (1993) The use of species accumulation functions for the prediction of species richness. Conserv Biol 7:480–488

    Article  Google Scholar 

  • Soberón J, Jimenez R, Golubov J et al (2007) Assessing completeness of biodiversity databases at different spatial scales. Ecography 30:152–160

    Google Scholar 

  • Soria-Auza RW, Kessler M (2008) The influence of sampling intensity on the perception of the spatial distribution of tropical diversity and endemism: a case study of ferns from Bolivia. Divers Distrib 14:123–130

    Article  Google Scholar 

  • Trontelj P, Douady CJ, Fišer C et al (2009) A molecular test for cryptic diversity in groundwater: how large are the ranges of macro-stygobionts? Freshw Biol 54:727–744

    Article  CAS  Google Scholar 

  • Tyre AJ, Tenhumberg B, Field SA et al (2003) Improving precision and reducing bias in biological surveys: estimating false-negative error rates. Ecol Appl 13:1790–1801

    Article  Google Scholar 

  • Williams PH, Margules CR, Hilbert DW (2002) Data requirements and data sources for biodiversity priority area selection. J Biosci 27:327–338

    Article  CAS  PubMed  Google Scholar 

  • Zagmajster M, Sket B, Podobnikar T (2006) Choosing a grid network for spatial analysis of subterranean biodiversity. In: Perko D, Nared J, Čeh M et al (eds) Geographical information systems in Slovenia 2005–2006. ZRC SAZU Publishing, Ljubljana

    Google Scholar 

  • Zagmajster M, Culver DC, Sket B (2008) Species richness patterns of obligate subterranean beetles (Insecta: Coleoptera) in a global biodiversity hotspot-effect of scale and sampling intensity. Divers Distrib 14:95–105

    Article  Google Scholar 

Download references

Acknowledgments

We are grateful to Špela Gorički, University of Maryland, for discussions on residual analysis. The work of MZ was financially supported by the Slovenian Research Agency and by UNESCO-L’Oréal international fellowship «For Women in Science».

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Correspondence to Maja Zagmajster.

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10531_2010_9873_MOESM1_ESM.eps

Appendix A The maps of species richness patterns of troglobiotic beetles in the northwestern Balkans, when the whole dataset is included: A—observed numbers of species, B—residuals of the Clench function fit (Soberón and Llorente 1993) of the observed number of species to sampling intensity (measured with number of caves with beetles). We used the following classes: the first, grid cells having at least 85% of the maximum observed in the richest grid cell; the second, grid cells having between 60 and 85% of the maximum; the third, grid cells with between 30 and 60% of the maximum; the fourth class contained between two species and 30% of the maximum number of species; and, the fifth class exactly one species. In case of residuals, delimitation is presented separately for positive and negative ones, with the fourth and fifth classes merged in one (Lambert Conformal Conical Projection). (EPS 1887 kb)

10531_2010_9873_MOESM2_ESM.eps

Appendix B The Clench function fit (Soberón and Llorente 1993) of the observed number of species of obligate subterranean beetles to sampling intensity (measured with number of caves with troglobiotic beetles) per 20 × 20 km grid cells in the northwestern Balkans, when the whole dataset is included. The asymptote parameter estimate is 0.80825, the rate parameter estimate is 0.03752 (Clench function parameters a and b, see text), with RMSE 2.35. (EPS 717 kb)

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Zagmajster, M., Culver, D.C., Christman, M.C. et al. Evaluating the sampling bias in pattern of subterranean species richness: combining approaches. Biodivers Conserv 19, 3035–3048 (2010). https://doi.org/10.1007/s10531-010-9873-2

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  • DOI: https://doi.org/10.1007/s10531-010-9873-2

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