The use of long-term monitoring data for studies of planktonic diversity : a cautionary tale from two Swiss lakes

Abstract

Long-term data have been suggested as resources for investigating environmental influences on biodiversity and, in turn, the role of biodiversity for ecosystem dynamics. However, scientists analysing biodiversity patterns in long-term data need to recognise that multidecadal time series are likely to suffer from inconsistencies in methodology, which might strongly complicate the interpretation of diversity patterns. Unfortunately, such inconsistencies are usually difficult to detect, and consequently, it is not known how strongly they affect the conclusions drawn.Here, we highlight two long-term data sets sampled by one laboratory to analyse patterns in phytoplankton richness in two Swiss lakes, Lake Zurich and Lake Walen. Apparent patterns in the long-term species richness in the two lakes arise from: (i) inconsistencies in species identification (changes in taxonomic literature and/or taxonomic expertise of the counting personnel) and (ii) changes in the detection limits of taxa. Hence, bias in these two case studies was strong enough to obscure any possible effects on species richness of environmental change (oligotrophication).We show that in the case of these two data sets, inconsistency confounds estimates of phytoplankton richness not only at the species level but even at the generic and familial levels. This suggests that a solution often proposed for inconsistency, that is, reanalysis of the data after aggregation to genus or family, may be insufficient.We suggest the use of two diagnostic plots, which may be used in other studies examining richness patterns in either long-term time series or comparative studies in which several scientists/laboratories contributed to data acquisition. These plots illustrate temporal or spatial patterns in (i) the percentage of taxa identified only to genus and (ii) in the 5% percentile of the concentrations of individual algal taxa. They will help to identify inconsistency problems due to changes or differences in (i) taxonomic expertise and (ii) detection limits.