  An important objective of experimental biology is the quantification of the
relationship between predictor and response variables, a statistical analysis
often termed variance partitioning (VP). In this paper, a series of simulations
is presented, aiming to generate quantitative estimates of the expected
statistical uncertainty of VP analyses. We demonstrate scenarios with
considerable uncertainty in VP estimates, such that it significantly reduces
the statistical reliability of the obtained results. Especially when a
predictor variable of a dataset shows a low between-group variance, VP
estimates may show a high margin of error. This becomes particularly important
when the respective predictor variable only explains a small fraction of the
overall variance, or the number of replicates is particularly small. Moreover,
it is demonstrated that the expected error of VP estimates of a dataset can be
approximated by bootstrap resampling, giving researchers a tool for the
quantification of the uncertainty associated with an arbitrary VP analysis. The
applicability of this method is demonstrated by a re-analysis of the Oribatid
mite dataset introduced by Borcard and Legendre in 1994 and the Barro Colorado
Island tree count dataset by Condit and colleagues. We believe that this study
may encourage biologists to approach routine statistical analyses such as VP
more critically, and report the error associated with them more frequently.
