‘Batteries’ in Machine Learning: A First Experimental Assessment of Inference for Siberian Crane Breeding Grounds in the Russian High Arctic Based on ‘Shaving’ 74 Predictors

The Siberian crane (Leucogeranus leucogeranus,) remains an elusive but highly regarded species of global conservation concern. Breeding regions occur in the Russian high arctic, and two subpopulations are known. Here we present for the first time a machine learning-based summer habitat analysis using nesting data for the eastern population in the breeding grounds employing predictive modeling with 74 GIS predictors. There is a typical desire for parsimony to help increase interpretability of models, but findings generally show that it would not result in greatest improvement to the model and inference. ‘Batteries’ are a new concept in machine learning allowing to test a set of experiments that help to test on predictors and model selection. Here we show 28 of those ‘batteries’ and compared multiple approaches to model runs from iteratively dropping the least or most important predictor (‘variable shaving’) to allow all predictors to contribute. It was found that the generic ‘kitchen sink’ model with TreeNet (an optimized boosting algorithm from Salford Systems Ltd) performs best. However, while the use of ‘batteries’ remain widely underused in wildlife conservation management, ‘shaving’ was of great use to learn about the structure, role and impacts of predictors and their spatial performance supporting non-parsimonious work. Of great interest is the finding that a bundle of low-ranked predictors performs almost equal to, or better than, the so-called top predictors. This is called ‘Predictor swapping’. This is the best and most detailed habitat study and prediction for the Siberian crane in summer, thus far. It is to be used for conservation management and as a generic template for any species while data availability and the environmental crisis are on the rise, specifically for the high Arctic.