Cited by 2,729

The DOI matches its Crossref record; 2 of the 2 references it cites were found in a registry or an index.

Opening the black box: an open‐source release of Maxent

Phillips, Steven J., Anderson, Robert P., Dudík, Miroslav, Schapire, Robert E., Blair, Mary E.·2017·Ecography

Abstract

This software note announces a new open‐source release of the Maxent software for modeling species distributions from occurrence records and environmental data, and describes a new R package for fitting such models. The new release (ver. 3.4.0) will be hosted online by the American Museum of Natural History, along with future versions. It contains small functional changes, most notably use of a complementary log‐log (cloglog) transform to produce an estimate of occurrence probability. The cloglog transform derives from the recently‐published interpretation of Maxent as an inhomogeneous Poisson process (IPP), giving it a stronger theoretical justification than the logistic transform which it replaces by default. In addition, the new R package, maxnet, fits Maxent models using the glmnet package for regularized generalized linear models. We discuss the implications of the IPP formulation in terms of model inputs and outputs, treating occurrence records as points rather than grid cells and interpreting the exponential Maxent model (raw output) as as an estimate of relative abundance. With these two open‐source developments, we invite others to freely use and contribute to the software.

Overview

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Integrity signals

Registry

Crossref · 10.1111/ecog.03049

Cites

2 of 2 references verified0 citation errors · 0 not checkable

Statistics

Verification

Claims the paper makes

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Claims other papers make about this one

Each is checked against this paper’s own text: does it say what the citing paper says it does?

Self-check

Percentages, sums and reported differences re-computed from the paper’s own numbers.

The self-check needs the paper’s full text.

Citations

Cites

Flagged first, then by citations

Show all 2 references

Research works printed but not linked: 28

  1. Aarts G., 2012, Comparative interpretation of count, presence–absence and point methods for species distribution models, Methods Ecol. Evol, 3, 177, 10.1111/j.2041-210X.2011.00141.x · DOI
  2. Aiello‐Lammens M. E., 2015, spThin: an R package for spatial thinning of species occurrence records for use in ecological niche models, Ecography, 38, 541, 10.1111/ecog.01132 · DOI
  3. Anderson R. P, 2013, A framework for using niche models to estimate impacts of climate change on species distributions, Ann. N. Y. Acad. Sci, 1297, 8, 10.1111/nyas.12264 · DOI
  4. Barry S. C., 2002, Generalized additive modelling and zero inflated count data, Ecol. Model, 157, 179, 10.1016/S0304-3800(02)00194-1 · DOI
  5. Boria R. A., 2014, Spatial filtering to reduce sampling bias can improve the performance of ecological niche models, Ecol. Model, 275, 73, 10.1016/j.ecolmodel.2013.12.012 · DOI
  6. Diggle P, 2003, Statistical analysis of spatial point patterns, mathematics, biology
  7. Elith J., 2006, Novel methods improve prediction of species’ distributions from occurrence data, Ecography, 29, 129, 10.1111/j.2006.0906-7590.04596.x · DOI · Paper page
  8. Elith J., 2011, A statistical explanation of MaxEnt for ecologists, Divers. Distrib, 17, 43, 10.1111/j.1472-4642.2010.00725.x · DOI · Paper page
  9. Fithian W., 2013, Finite‐sample equivalence in statistical models for presence‐only data, Ann. Appl. Stat, 7, 1917, 10.1214/13-AOAS667 · DOI
  10. Fithian W., 2015, Bias correction in species distribution models: pooling survey and collection data for multiple species, Methods Ecol. Evol, 6, 424, 10.1111/2041-210X.12242 · DOI
  11. Friedman J., 2010, Regularization paths for generalized linear models via coordinate descent, J. Stat. Softw, 33, 1, 10.18637/jss.v033.i01 · DOI · Paper page
  12. Guillera‐Arroita G., 2014, Maxent is not a presence absence method: a comment on Thibaud et al, Methods Ecol. Evol, 5, 1192, 10.1111/2041-210X.12252 · DOI
  13. Merow C., 2013, A practical guide to MaxEnt for modeling species distributions: what it does, and why inputs and settings matter, Ecography, 36, 1058, 10.1111/j.1600-0587.2013.07872.x · DOI · Paper page
  14. Merow C., 2016, Improving niche and range estimates with Maxent and point process models by integrating spatially explicit information, Global Ecol. Biogeogr, 25, 1022, 10.1111/geb.12453 · DOI
  15. Muscarella R., 2014, ENMeval: an R package for conducting spatially independent evaluations and estimating optimal model complexity for Maxent ecological niche models, Methods Ecol. Evol, 5, 1198, 10.1111/2041-210X.12261 · DOI · Paper page
  16. Phillips S. J., 2008, Modeling of species distributions with Maxent: new extensions and a comprehensive evaluation, Ecography, 31, 161, 10.1111/j.0906-7590.2008.5203.x · DOI · Paper page
  17. Phillips S. J., 2004, A maximum entropy approach to species distribution modeling, Proceedings of the Twenty‐First International Conference on Machine Learning
  18. Phillips S. J., 2006, Maximum entropy modeling of species geographic distributions, Ecol. Model, 190, 231, 10.1016/j.ecolmodel.2005.03.026 · DOI · Paper page
  19. Phillips S. J., 2009, Sample selection bias and presence‐only distribution models: implications for background and pseudo‐absence data, Ecol. Appl, 19, 181, 10.1890/07-2153.1 · DOI · Paper page
  20. Radosavljevic A., 2014, Making better Maxent models of species distributions: complexity, overfitting and evaluation, J. Biogeogr, 41, 629, 10.1111/jbi.12227 · DOI · Paper page
  21. Reddy S., 2003, Geographical sampling bias and its implications for conservation priorities in Africa, J. Biogeogr, 30, 1719, 10.1046/j.1365-2699.2003.00946.x · DOI
  22. Renner I. W., 2013, Equivalence of Maxent and Poisson point process models for species distribution modeling in ecology, Biometrics, 69, 274, 10.1111/j.1541-0420.2012.01824.x · DOI
  23. Renner I. W., 2015, Point process models for presence‐only analysis, Methods Ecol. Evol, 6, 366, 10.1111/2041-210X.12352 · DOI
  24. Soberón J., 2004, Biodiversity informatics: managing and applying primary biodiversity data, Phil. Trans. R. Soc. B, 359, 689, 10.1098/rstb.2003.1439 · DOI
  25. Syfert M. M., 2013, The effects of sampling bias and model complexity on the predictive performance of MaxEnt species distribution models, PLoS One, 8, e55158, 10.1371/journal.pone.0055158 · DOI
  26. VanDerWal J., 2009, Abundance and the environmental niche: environmental suitability estimated from niche models predicts the upper limit of local abundance, Am. Nat, 174, 282, 10.1086/600087 · DOI
  27. Warton D., 2010, Poisson point process models solve the “pseudo‐absence problem” for presence‐only data in ecology, Ann. Appl. Stat, 4, 1383
  28. Wenger S. J., 2008, Estimating species occurrence, abundance, and detection probability using zero‐inflated distributions, Ecology, 89, 2953, 10.1890/07-1127.1 · DOI

Cited by

Sorted by citations

See all 2,729

Metadata

Authors
Phillips, S. J., Anderson, R. P., Dudík, M., Schapire, R. E., & Blair, M. E.
Affiliations
  • Center for Biodiversity and Conservation, American Museum of Natural History New York NY USA
  • Dept of Biology, City College of New York City Univ. of New York New York NY USA
  • Microsoft Research New York NY USA
Venue
Ecography
Publisher
Wiley
Published
2017-05-09
Volume
40
Issue
7
Pages
887-893
ISSN
  • 0906-7590
  • 1600-0587
Cited by
2,729
References
2
Language
en

About these checks

Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

Statistics and arithmetic are recomputed, not judged.

Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.

What counts as a claim ›How we measure confidence ›How we check this ›

Are you an author, or do you see a problem with this page? Request a review