Maximum entropy modeling of species geographic distributions

  title={Maximum entropy modeling of species geographic distributions},
  author={Steven J. Phillips and Robert P. Anderson and Robert E. Schapire},
  journal={Ecological Modelling},

Figures and Tables from this paper

Maximum power entropy method for ecological data analysis
In ecology predictive models of the geographical distribution of certain species are widely used to capture the spatial diversity. Recently a method of Maxent based on Gibbs distribution is
Modeling of species distributions with Maxent: new extensions and a comprehensive evaluation
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.
MaxEnt versus MaxLike: empirical comparisons with ant species distributions
For species distribution modeling, MaxLike, and similar models that are based on an explicit sampling process and that directly estimate probability of occurrence, should be considered as important alternatives to the widely-used MaxEnt framework.
Modeling species distribution
There are many methods for modeling species distribution in the landscape. In this chapter, the authors elaborate on the concepts of species modeling and present three popular techniques to generate
Bounding Species Distribution Models
It is suggested that multiple models of bounding, and the most conservative bounding of species distribution models, like those presented here, should probably replace the unbounded or loosely bounded techniques currently used.
Population distribution models: species distributions are better modeled using biologically relevant data partitions
Dividing the data of a widespread species into biologically relevant partitions greatly increased the performance of the distribution model; therefore, this approach may prove to be quite practical and informative for a wide range of modeling applications.
Habitat Suitability Modeling for Wildlife Management Objectives by Using Maximum Entropy Method
Habitat suitability models are useful tools for a variety of wildlife management objectives. Distributions of wildlife species can be predicted for geographical areas that have not been extensively
Using Historical Atlas Data to Develop High-Resolution Distribution Models of Freshwater Fishes
The community-based SDM framework broadens the capability to model species distributions by innovatively removing the constraint of lack of species absence data, thus providing a robust prediction of distribution for stream fishes in other regions where historical data exist, and for other taxa usually observed by community- based sampling designs.
Logistic Methods for Resource Selection Functions and Presence-Only Species Distribution Models
This work introduces a new scaled binomial loss function for estimating an underlying logistic model of species presence/absence, and demonstrates that approaches by Lele and Keim and by Lancaster and Imbens that surmount the identifiability issue by making parametric data assumptions do not typically produce valid probability estimates.
The challenge of modeling niches and distributions for data‐poor species: a comprehensive approach to model complexity
The results for this species intimate that AICc may consistently select models with fewer parameters and be more robust to sampling bias, and recommend that researchers assess the critical yet underappreciated issue of model complexity both via information criteria and performance on withheld data, comparing the results between the two approaches and taking into account ecological plausibility.


A maximum entropy approach to species distribution modeling
This work proposes the use of maximum-entropy techniques for this problem, specifically, sequential-update algorithms that can handle a very large number of features, and investigates the interpretability of models constructed using maxent.
Predictive habitat distribution models in ecology
Sensitivity of distributional prediction algorithms to geographic data completeness
Effects of sample size on accuracy of species distribution models
Niche Modeling and Geographic Range Predictions in the Marine Environment Using a Machine-learning Algorithm
(Vieglais et al., 2000). Museum data are high quality because voucher specimens can be examined if identification is questionable. However, like all point data, museum specimens provide only a
An improved approach for predicting the distribution of rare and endangered species from occurrence and pseudo-absence data
Summary 1. Few examples of habitat-modelling studies of rare and endangered species exist in the literature, although from a conservation perspective predicting their distribution would prove
Niche differentiation in Mexican birds: using point occurrences to detect ecological innovation
It is suggested that the potential existence of evolved, intraspecific niche differentiation in species niche requirements is revealed using genetic algorithms coupled with geographical information systems, which provide a powerful and novel approach to characterizing species ecological niches and geographical distributions.