Separating wheat from chaff: Diatom taxon selection usingan artificial neural network pruning algorithm

This study addresses the question of what diatom taxa to includein a modern calibration set based on their relative contribution in apalaeolimnological calibration model. Using a pruning algorithm for ArtificialNeural Networks (ANNs) which determines the functionality of individual taxa interms of model performance, we pruned the Surface Water Acidification Project(SWAP) pH-diatom data-set until the predictive performance of thepruned set (as assessed by a jackknifing procedure) was statistically differentfrom the initial full-set. Our results, based on the validation at each5% data-set reduction, show that (i) 85% of the taxa canbe removed without any effect on the pH model calibration performance, and (ii)that the complexity and the dimensionality reduction of the model by theremoval of these non-essential or redundant taxa greatly improve therobustness of the calibration. A comparison between the commonly usedmarginal criteria for inclusion (species tolerance andHill's N2) and our functionality criterion shows that the importance ofeach taxon in an ANN palaeolimnological model calibration does not appear todepend on these marginal characteristics.