Scaled chrysophytes and pH inference models: the effects of converting scale counts to cell counts and other species data transformations

Predictive pH models developed using scaled chrysophytes (Synurophyceae, Chrysophyceae) have thus far been based on the relative abundance of scales and not whole cells. This paper examines the effects of transforming scale to cell numbers on the predictive abilities of pH inference models, and the effects of logarithmic and square-root transformations of the species data on the predictive abilities of pH inference models.Very similar pH inference models were developed based on either the relative abundance of scales or cells. Thus, in this data-set, there appears to be no statistical advantage in transforming raw scale counts to cell counts prior to calculating the relative abundances. However, if one wishes to compare paleochrysophyte populations to actual long-term limnological chrysophyte collections, a scale-to-cell transformation would be desirable. Logarithmic and square-root transformations of the species data improve the pH inference models. These transformations increase the effective number of occurrences of chrysophyte taxa when compared to the untransformed scale and cell pH models. The logarithmic and square-root transformations improve the pH inference models because the dominant taxa, which are often pH generalists, are down-weighted in comparison to the more pH specialist, sub-dominant taxa. We suggest researchers use either a logarithmic or square-root transformation on chrysophyte scale data to improve quantitative reconstructions of lakewater pH and possibly other variables.