Alan H. Fielding

TL;DR: Thirteen recommendations are made to enable the objective selection of an error assessment technique for ecological presence/absence models and a new approach to estimating prediction error, which is based on the spatial characteristics of the errors, is proposed.

TL;DR: In this article, a range of predictive models were developed using discriminant analysis and logistic regression for the Golden Eagle (Aquila chrysaetos), Raven (Corvus corax), and Buzzard (Buteo buteo) living in northwest Scotland.

TL;DR: Of high importance is the identification of a widely applicable set of transferability metrics, with appropriate tools to quantify the sources and impacts of prediction uncertainty under novel conditions.

TL;DR: This book provides an overview of these important data analysis methods, from long-established statistical methods to more recent machine learning techniques, to provide a framework that will enable the reader to recognise the assumptions and constraints that are implicit in all such techniques.

TL;DR: This is the first text aimed at introducing machine learning methods to a readership of professional ecologists, and a group of experienced machine learning workers, who have become interested in environmental problems, have written a chapter that demonstrates howMachine learning methods can be used to discover equations that describe the dynamic behaviour of ecological systems.