Undersampling and the measurement of beta diversity

Abstract

Summary1. Beta diversity is a conceptual link between diversity at local and regional scales. Various additional methodologies of quantifying this and related phenomena have been applied. Among them, measures of pairwise (dis)similarity of sites are particularly popular. Undersampling, i.e. not recording all taxa present at a site, is a common situation in ecological data. Bias in many metrics related to beta diversity must be expected, but only few studies have explicitly investigated the properties of various measures under undersampling conditions. 2. On the basis of an empirical data set, representing near-complete local inventories of the Lepidoptera from an isolated Pacific island, as well as simulated communities with varying properties, we mimicked different levels of undersampling. We used 14 different approaches to quantify beta diversity, among them dataset-wide multiplicative partitioning (i.e. 'true beta diversity') and pairwise site x site dissimilarities. We compared their values from incomplete samples to true results from the full data. We used these comparisons to quantify undersampling bias and we calculated correlations of the dissimilarity measures of undersampled data with complete data of sites. 3. Almost all tested metrics showed bias and low correlations under moderate to severe undersampling conditions (as well as deteriorating precision, i.e. large chance effects on results). Measures that used only species incidence were very sensitive to undersampling, while abundance-based metrics with high dependency on the distribution of the most common taxa were particularly robust. Simulated data showed sensitivity of results to the abundance distribution, confirming that data sets of high evenness and/or the application of metrics that are strongly affected by rare species are particularly sensitive to undersampling. 4. The class of beta measure to be used should depend on the research question being asked as different metrics can lead to quite different conclusions even without undersampling effects. For each class of metric, there is a trade-off between robustness to undersampling and sensitivity to rare species. In consequence, using incidencebased metrics carries a particular risk of false conclusions when undersampled data are involved. Developing bias corrections for such metrics would be desirable.

Detecting diversity: emerging methods to estimate species diversity

“…Although this reduces bias compared with uncorrected species counts, mathematical methods for incorporating additional features of the study design, such as site-or survey-specific covariates affecting species' detection ( Figure 1), are not straightforward or often not possible [36]. Beta-diversity indices of community similarity are frequently calculated without correcting for imperfect detection [73] and their performance in the face of undetected species is variable [45,74]. The ChaoJaccard/Sorensen index [45,73] is one of the few betadiversity estimators that corrects for sampling bias by estimating the contribution of undetected species using the probability that individuals drawn randomly from the sample belong to a species shared by the two assemblages, based on species' relative abundances or occurrences.…”

Study of biological communities subject to imperfect detection: bias and precision of communityN‐mixture abundance models in small‐sample situations

“…The confounding of imperfect detection with ecological parameters has been observed in the single species situations for population extinction and colonization rates (Moilanen 2002;Ke´ry 2004;Ke´ry et al 2006;. Beck et al (2013) also showed that most metrics measuring community differences are susceptible to incomplete sampling, and called for the development of robust metrics. In our simulation experiments, although we assumed that detection probability of individual species was constant among the sampling sites, this assumption cannot be always accepted in field surveys.…”

Diversity, taxonomic composition, and functional aspects of fungal communities in living, senesced, and fallen leaves at five sites across North America

“…ANOSIM, NMDS, hierarchical clustering, and PERMANOVA were conducted using all non-singleton OTU (i.e., those occurring more than once in the entire dataset) to minimize the potential under-estimation of similarity due to undersampling (Manter & Bakker, 2015). The Morisita-Horn metric of similarity was used to minimize sensitivity to under-sampling, as it reflects the distribution of abundant taxa (Beck, Holloway & Schwanghart, 2013). Host individuals/leaf types from which <4 isolates were sequenced were excluded from all analyses (see Table 1).…”