Biodiversity and Conservation

, Volume 19, Issue 11, pp 3035–3048 | Cite as

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

  • Maja Zagmajster
  • David C. Culver
  • Mary C. Christman
  • Boris Sket
Original paper


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.


Biodiversity Northwestern Balkans Obligate cave beetles Residual analysis Sampling intensity Species richness hotspots Terrestrial troglobionts Uniques 



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».

Supplementary material

10531_2010_9873_MOESM1_ESM.eps (1.8 mb)
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 (718 kb)
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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Copyright information

© Springer Science+Business Media B.V. 2010

Authors and Affiliations

  • Maja Zagmajster
    • 1
  • David C. Culver
    • 2
  • Mary C. Christman
    • 3
  • Boris Sket
    • 1
  1. 1.Oddelek za biologijo, Biotehniška fakultetaUniverza v LjubljaniLjubljanaSlovenia
  2. 2.Department of Environmental ScienceAmerican UniversityWashington, DCUSA
  3. 3.Department of Statistics—IFASUniversity of FloridaGainesvilleUSA

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