Visualising and assessing the probable error from downscaling ecological time series data

Collecting natural data at regular, fine scales is an onerous and often costly procedure. However, there is a basic need for fine scale data when applying inductive methods such as neural networks or genetic algorithms for the development of ecological models. This paper will address the issues involved in interpolating data for use in machine learning methods by considering how to determine if a downscaling of the data is valid. The approach is based on a multi-scale estimate of errors. The resulting function has similar properties to a time series variogram; however, the comparison at different scales is based on the variance introduced by rescaling from the original sequence. This approach has a number of properties, including the ability to detect frequencies in the data below the current sampling rate, an estimate of the probable average error introduced when a sampled variable is downscaled and a method for visualising the sequences of a time series that are most susceptible to error due to sampling. The described approach is ideal for supporting the ongoing sampling of ecological data and as a tool for assessing the impact of using interpolated data for building inductive models of ecological response.