A practical guide to MaxEnt for modeling species' distributions: what it does, and why inputs and settings matter
TLDR
A detailed explanation of how MaxEnt works and a prospectus on modeling options are provided to enable users to make informed decisions when preparing data, choosing settings and interpreting output to highlight the need for making biologically motivated modeling decisions.Abstract:
The MaxEnt software package is one of the most popular tools for species distribution and environmental niche modeling, with over 1000 published applications since 2006. Its popularity is likely for two reasons: 1) MaxEnt typically outperforms other methods based on predictive accuracy and 2) the software is particularly easy to use. MaxEnt users must make a number of decisions about how they should select their input data and choose from a wide variety of settings in the software package to build models from these data. The underlying basis for making these decisions is unclear in many studies, and default settings are apparently chosen, even though alternative settings are often more appropriate. In this paper, we provide a detailed explanation of how MaxEnt works and a prospectus on modeling options to enable users to make informed decisions when preparing data, choosing settings and interpreting output. We explain how the choice of background samples reflects prior assumptions, how nonlinear functions of environmental variables (features) are created and selected, how to account for environmentally biased sampling, the interpretation of the various types of model output and the challenges for model evaluation. We demonstrate MaxEnt’s calculations using both simplified simulated data and occurrence data from South Africa on species of the flowering plant family Proteaceae. Throughout, we show how MaxEnt’s outputs vary in response to different settings to highlight the need for making biologically motivated modeling decisions.read more
Citations
More filters
Journal ArticleDOI
Opening the black box: an open-source release of Maxent
Steven J. Phillips,Robert P. Anderson,Robert P. Anderson,Miroslav Dudík,Robert E. Schapire,Mary E. Blair +5 more
TL;DR: A new open-source release of the Maxent software for modeling species distributions from occurrence records and environmental data is announced, and a new R package for fitting Maxent models using the glmnet package for regularized generalized linear models is described.
Journal ArticleDOI
ENMeval: An R package for conducting spatially independent evaluations and estimating optimal model complexity for Maxent ecological niche models
Robert Muscarella,Peter J. Galante,Mariano Soley-Guardia,Robert A. Boria,Jamie M. Kass,María Uriarte,Robert P. Anderson,Robert P. Anderson +7 more
TL;DR: ENMeval as mentioned in this paper is an R package that creates data sets for k-fold cross-validation using one of several methods for partitioning occurrence data (including options for spatially independent partitions), builds a series of candidate models using Maxent with a variety of user-defined settings and provides multiple evaluation metrics to aid in selecting optimal model settings.
Journal ArticleDOI
SDMtoolbox: a python-based GIS toolkit for landscape genetic, biogeographic and species distribution model analyses
TL;DR: The toolkit simplifies many GIS analyses required for species distribution modelling and other analyses, alleviating the need for repetitive and time-consuming climate data pre-processing and post-SDM analyses.
Journal ArticleDOI
Is my species distribution model fit for purpose? Matching data and models to applications
Gurutzeta Guillera-Arroita,José J. Lahoz-Monfort,Jane Elith,Ascelin Gordon,Heini Kujala,Pia E. Lentini,Michael A. McCarthy,Reid Tingley,Brendan A. Wintle +8 more
TL;DR: In this paper, the authors synthesize current knowledge and provide a simple framework that summarizes how interactions between data type and the sampling process determine the quantity that is estimated by a species distribution model.
References
More filters
Journal ArticleDOI
Regression Shrinkage and Selection via the Lasso
TL;DR: A new method for estimation in linear models called the lasso, which minimizes the residual sum of squares subject to the sum of the absolute value of the coefficients being less than a constant, is proposed.
Book
Model Selection and Multimodel Inference: A Practical Information-Theoretic Approach
TL;DR: The second edition of this book is unique in that it focuses on methods for making formal statistical inference from all the models in an a priori set (Multi-Model Inference).
Book
The Elements of Statistical Learning: Data Mining, Inference, and Prediction
TL;DR: In this paper, the authors describe the important ideas in these areas in a common conceptual framework, and the emphasis is on concepts rather than mathematics, with a liberal use of color graphics.
Journal ArticleDOI
Very high resolution interpolated climate surfaces for global land areas.
Robert J. Hijmans,Susan E. Cameron,Susan E. Cameron,Juan L. Parra,Peter G. Jones,Andy Jarvis +5 more
TL;DR: In this paper, the authors developed interpolated climate surfaces for global land areas (excluding Antarctica) at a spatial resolution of 30 arc s (often referred to as 1-km spatial resolution).
Journal ArticleDOI
Maximum entropy modeling of species geographic distributions
TL;DR: In this paper, the use of the maximum entropy method (Maxent) for modeling species geographic distributions with presence-only data was introduced, which is a general-purpose machine learning method with a simple and precise mathematical formulation.
Related Papers (5)
Modeling of species distributions with Maxent: new extensions and a comprehensive evaluation
Novel methods improve prediction of species' distributions from occurrence data
Jane Elith,Catherine H. Graham,Robert P. Anderson,Miroslav Dudík,Simon Ferrier,Antoine Guisan,Robert J. Hijmans,Falk Huettmann,John R. Leathwick,Anthony Lehmann,Jin Li,Lúcia G. Lohmann,Bette A. Loiselle,Glenn Manion,Craig Moritz,Miguel Nakamura,Yoshinori Nakazawa,Jacob C. M. Mc Overton,A. Townsend Peterson,Steven J. Phillips,Karen Richardson,Ricardo Scachetti-Pereira,Robert E. Schapire,Jorge Soberón,Stephen E. Williams,Mary S. Wisz,Niklaus E. Zimmermann +26 more