Prediction of ungulates abundance through local linear algorithms

Abstract

We use a local learning algorithm to predict the abundance of the Alpine ibex population living in the Gran Paradiso National Park, Northern Italy. Population abundance, recorded for a period of 40 years, have been recently analyzed by [Jacobson, A., Provenzale, A., Von Hardenberg, A., Bassano, B., Festa-Bianchet, M., 2004. Climate forcing and density dependence in a mountain ungulate population. Ecology 85, 1598–1610], who showed that the rate of increase of the population depends both on its density and snow depth. In the same paper, a threshold linear model is proposed for predicting the population abundance. In this paper, we identify a similar linear model in a local way, using a lazy learning algorithm. The advantages of the local model over the traditional global model are: improved forecast accuracy, easier understanding of the role and behaviour of the parameters, effortless way to keep the model up-to-date. Both data and software used in this work are of public domain; therefore, experiments can be easily replicated and further discussions are welcome.