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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.
Novel methods improve prediction of species' distributions from occurrence data
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.
A statistical explanation of MaxEnt for ecologists
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.
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.
The art of modelling range‐shifting species
Modelling approaches are explored that aim to minimize extrapolation errors and assess predictions against prior biological knowledge to promote methods appropriate to range‐shifting species.
Sample selection bias and presence-only distribution models: implications for background and pseudo-absence data.
It is argued that increased awareness of the implications of spatial bias in surveys, and possible modeling remedies, will substantially improve predictions of species distributions and as large an effect on predictive performance as the choice of modeling method.
Opening the black box: an open-source release of Maxent
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.
The prize collecting Steiner tree problem: theory and practice
An improved pruning rule is introduced for the Goemans-WiUiamson Minimization algorithm that is slightly faster and provides solutions that are at least as good and typically significantly better than existing algorithms.
Point process models for presence‐only analysis
Presence‐only data are widely used for species distribution modelling, and point process regression models are a flexible tool that has considerable potential for this problem, when data arise as