variables (e.g. whether predictor contributions are weighted, and whether they allow for interactions among variables) (Segurado and Araújo 2004, Elith et al. 2006, Austin 2007, Naimi et al. 2011, Peterson et al. 2011). The outputs of SDMs are sensitive to the specific rules used to parameterize them. When models are implemented in different platforms, rules used to fit them may not be comparab...

This is a brief tutorial to accompany a set of functions that we have written to facilitate fitting BRT (boosted regression tree) models in R . This tutorial is a modified version of the tutorial accompaniying Elith, Leathwick and Hastie’s article in Journal of Animal Ecology. It has been adjusted to match the implementation of these functions in the ’dismo’ package. The gbm* functions in the d...

The most commonly used approaches to describe distributions of species and biodiversity are known as correlative (syn. phenomenological) species distribution models (Elith & Leathwick, 2009). These methods aim to describe the patterns, not the mechanisms, in the association between species occurrences and environmental data (mainly climatic data). Helmholtz Centre for Environmental Research – U...

Comment: This is the final submitted manuscript for this paper, without further corrections. It has been reformatted for efficient printing. For a pdf of the final Blackwell publishing version please email Jane Elith SUMMARY 1. Ecologists use statistical models for both explanation and prediction, and need techniques that are flexible enough to express typical features of their data such as non...

Antoine Guisan,* Reid Tingley, John B. Baumgartner, Ilona Naujokaitis-Lewis, Patricia R. Sutcliffe, Ayesha I. T. Tulloch, Tracey J. Regan, Lluis Brotons, Eve McDonald-Madden, Chrystal Mantyka-Pringle, Tara G. Martin, Jonathan R. Rhodes, Ramona Maggini, Samantha A. Setterfield, Jane Elith, Mark W. Schwartz, Brendan A. Wintle, Olivier Broennimann, Mike Austin, Simon Ferrier, Michael R. Kearney, H...

Species distribution modeling (SDM) is widely used in ecology and conservation. Currently, the most available data for SDM are species presence-only records (available through digital databases). There have been many studies comparing performance of alternative algorithms data. Among these, a 2006 paper from Elith colleagues has particularly influential field, partly because they several novel ...

C. F. Dormann ([email protected]), B. Gruber and S. Lautenbach, Helmholtz Centre for Environmental Research-UFZ, Dept of Computational Landscape Ecology, Permoserstr. 15, DE-04318 Leipzig, Germany. CFD also at: Biometry and Environmental System Analysis, Tennenbacher Stra ß e 4, Univ. Freiburg, DE-79085 Freiburg, Germany. BG also at: Inst. for Applied Ecology, Faculty of Appl...

Correlative techniques for estimating environmental requirements of species— variably termed ecological niche modeling or species distribution modeling—are becoming very popular tools for ecologists and biogeographers in understanding diverse aspects of biodiversity. These tools, however, are frequently applied in ways that do not fit well into knowledge frameworks in population ecology and bio...

Ecological niche modelling and species distribution techniques (ENM SDM respectively) were first developed to infer putative suitable environmental conditions for in terrestrial systems. While there are distinctions be made between ENM SDM, hereafter we primarily use the term ‘ENM’, as SDMs can considered ENMs that have subsequently been projected into geographic space (Warren, 2012). workflows...

In order to better protect and conserve biodiversity, ecologists use machine learning and statistics to understand how species respond to their environment and to predict how they will respond to future climate change, habitat loss and other threats. A fundamental modeling task is to estimate the probability that a given species is present in (or uses) a site, conditional on environmental varia...