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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. 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. 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. 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 incidence-based metrics carries a particular risk of false conclusions when undersampled data are involved. Developing bias corrections for such metrics would be desirable.
Over the last two decades, macroecology the analysis of large-scale, multi-species ecological patterns and processes has established itself as a major line of biological research. Analyses of statistical links between environmental variables and biotic responses have long and successfully been employed as a main approach, but new developments are due to be utilized. Scanning the horizon of macroecology, we identified four challenges that will probably play a major role in the future. We support our claims by examples and bibliographic analyses. 1) Integrating the past into macroecological analyses, e.g. by using paleontological or phylogenetic information or by applying methods from historical biogeography, will sharpen our understanding of the underlying reasons for contemporary patterns. 2) Explicit consideration of the local processes that lead to the observed larger-scale patterns is necessary to understand the fine-grain variability found in nature, and will enable better prediction of future patterns (e.g. under environmental change conditions). 3) Macroecology is dependent on large-scale, high quality data from a broad spectrum of taxa and regions. More available data sources need to be tapped and new, small-grain large-extent data need to be collected. 4) Although macroecology already lead to mainstreaming cutting-edge statistical analysis techniques, we find that more sophisticated methods are needed to account for the biases inherent to sampling at large scale. Bayesian methods may be particularly suitable to address these challenges. To continue the vigorous development of the macroecological research agenda, it is time to address these challenges and to avoid becoming too complacent with current achievements.