Presence‐only modelling using MAXENT: when can we trust the inferences?
Charles B. Yackulic,Charles B. Yackulic,Richard B. Chandler,Elise F. Zipkin,J. Andrew Royle,James D. Nichols,Evan H. Campbell Grant,Sophie Veran +7 more
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TLDR
There are many misconceptions concerning the use of presence-only models, including the misunderstanding that MAXENT, and other presence- only methods, relieve users from the constraints of survey design, and a series of recommendations that researchers analyse data in a presence–absence framework whenever possible, because fewer assumptions are required and inferences are made about clearly defined parameters such as occurrence probability.Abstract:
Summary
Recently, interest in species distribution modelling has increased following the development of new methods for the analysis of presence-only data and the deployment of these methods in user-friendly and powerful computer programs. However, reliable inference from these powerful tools requires that several assumptions be met, including the assumptions that observed presences are the consequence of random or representative sampling and that detectability during sampling does not vary with the covariates that determine occurrence probability.
Based on our interactions with researchers using these tools, we hypothesized that many presence-only studies were ignoring important assumptions of presence-only modelling. We tested this hypothesis by reviewing 108 articles published between 2008 and 2012 that used the MAXENT algorithm to analyse empirical (i.e. not simulated) data. We chose to focus on these articles because MAXENT has been the most popular algorithm in recent years for analysing presence-only data.
Many articles (87%) were based on data that were likely to suffer from sample selection bias; however, methods to control for sample selection bias were rarely used. In addition, many analyses (36%) discarded absence information by analysing presence–absence data in a presence-only framework, and few articles (14%) mentioned detection probability. We conclude that there are many misconceptions concerning the use of presence-only models, including the misunderstanding that MAXENT, and other presence-only methods, relieve users from the constraints of survey design.
In the process of our literature review, we became aware of other factors that raised concerns about the validity of study conclusions. In particular, we observed that 83% of articles studies focused exclusively on model output (i.e. maps) without providing readers with any means to critically examine modelled relationships and that MAXENT's logistic output was frequently (54% of articles) and incorrectly interpreted as occurrence probability.
We conclude with a series of recommendations foremost that researchers analyse data in a presence–absence framework whenever possible, because fewer assumptions are required and inferences can be made about clearly defined parameters such as occurrence probability.read more
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A practical guide to MaxEnt for modeling species' distributions: what it does, and why inputs and settings matter
TL;DR: 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.
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
The importance of correcting for sampling bias in MaxEnt species distribution models
Stephanie Kramer-Schadt,Jürgen Niedballa,John D. Pilgrim,Boris Schröder,Boris Schröder,Jana Lindenborn,Vanessa Reinfelder,Milena Stillfried,Ilja Heckmann,Anne K. Scharf,Dave M. Augeri,Susan M. Cheyne,Andrew J. Hearn,Joanna Ross,David W. Macdonald,John Mathai,James A. Eaton,Andrew J. Marshall,Gono Semiadi,Rustam Rustam,Henry Bernard,Raymond Alfred,Hiromitsu Samejima,J. W. Duckworth,Christine Breitenmoser-Wuersten,Jerrold L. Belant,Heribert Hofer,Andreas Wilting +27 more
TL;DR: It is concluded that a substantial improvement in the quality of model predictions can be achieved if uneven sampling effort is taken into account, thereby improving the efficacy of species conservation planning.
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.
Journal ArticleDOI
The Effects of Sampling Bias and Model Complexity on the Predictive Performance of MaxEnt Species Distribution Models
TL;DR: Correcting for geographical sampling bias led to major improvements in goodness of fit, but did not entirely resolve the problem: predictions made with clustered ecological data were inferior to those made with the herbarium dataset, even after sampling bias correction.
References
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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.
Journal ArticleDOI
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
TL;DR: This work compared 16 modelling methods over 226 species from 6 regions of the world, creating the most comprehensive set of model comparisons to date and found that presence-only data were effective for modelling species' distributions for many species and regions.
Journal ArticleDOI
Predictive habitat distribution models in ecology
TL;DR: A review of predictive habitat distribution modeling is presented, which shows that a wide array of models has been developed to cover aspects as diverse as biogeography, conservation biology, climate change research, and habitat or species management.
Journal ArticleDOI
Modeling of species distributions with Maxent: new extensions and a comprehensive evaluation
TL;DR: This paper presents a tuning method that uses presence-only data for parameter tuning, and introduces several concepts that improve the predictive accuracy and running time of Maxent and describes a new logistic output format that gives an estimate of probability of presence.
Journal ArticleDOI
A statistical explanation of MaxEnt for ecologists
TL;DR: A new statistical explanation of MaxEnt is described, showing that the model minimizes the relative entropy between two probability densities defined in covariate space, which is likely to be a more accessible way to understand the model than previous ones that rely on machine learning concepts.
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