Quantitative Biology

Whywhere2.0: an R Package for Modeling Species Distributions on Big Environmental Data

Authors: David R.B. Stockwell
Comments: 13 Pages.

Previous studies have indicated that multi-interval discretization (segmentation) of continuous-valued attributes for classification learning might provide a robust machine learning approach to modelling species distributions. Here we apply a segmentation model to the $Bradypus~variegatus$ -- the brown-throated three-toed sloth -- using the species occurrence and climatic data sets provided in the niche modelling R package \texttt{dismo} and a set of 940 global data sets of mixed type on the Global Ecosystems Database. The primary measure of performance was the area under the curve of the receiver operating characteristic (AUC) on a k-fold validation of predictions of the segmented model and a third order generalized linear model (GLM). This paper also presents further advances in the \texttt{WhyWhere} algorithm available as an R package from the development site at http://github.com/davids99us/whywhere.
Category: Quantitative Biology