References

QMaxent's defaults, evaluation procedures, and survey-planning workflows are grounded in the species distribution modeling (SDM) literature. This chapter groups the works the plugin draws on by topic, with a one-line annotation on how each reference is used inside QMaxent. The plugin source code carries inline citations to these works in the relevant modules (workers/maxent_worker.py, bridge/elapid_bridge.py, bridge/priority_sites.py, core/venv_manager.py).

Maxent — core methodology

Phillips, S. J., Anderson, R. P., & Schapire, R. E. (2006). Maximum entropy modeling of species geographic distributions. Ecological Modelling, 190(3-4), 231–259. The original Maxent paper. QMaxent's feature-class taxonomy (LQPHT), jackknife importance, and ROC-based evaluation all come from this formulation.

Phillips, S. J., & Dudík, M. (2008). Modeling of species distributions with Maxent: new extensions and a comprehensive evaluation. Ecography, 31(2), 161–175. The benchmark paper that established the sample-size feature-class rule QMaxent's "Auto" mode follows, and the regularization-multiplier default of 1.0.

Phillips, S. J., Anderson, R. P., Dudík, M., Schapire, R. E., & Blair, M. E. (2017). Opening the black box: an open-source release of Maxent. Ecography, 40(7), 887–893. The release of the open-source Maxent and the formal recommendation to adopt the cloglog output transform — adopted as QMaxent's default.

Fithian, W., & Hastie, T. (2013). Finite-sample equivalence in statistical models for presence-only data. The Annals of Applied Statistics, 7(4), 1917–1939. The paper that establishes the formal equivalence between Maxent and infinitely-weighted logistic regression — the foundation for elapid's implementation.

elapid — software backend

Anderson, C. B. (2023). elapid: Species distribution modeling tools for Python. Journal of Open Source Software, 8(84), 5435. The peer-reviewed release of the elapid library that QMaxent uses for its MaxentModel, GeographicKFold, and feature transformers.

Pedregosa, F., et al. (2011). Scikit-learn: Machine learning in Python. Journal of Machine Learning Research, 12, 2825–2830. The numerical core that elapid's regularized-likelihood maximisation delegates to.

Harris, C. R., et al. (2020). Array programming with NumPy. Nature, 585, 357–362. The array library underlying elapid, scikit-learn, and the QMaxent raster-extraction layer.

Plugin architecture

Wu, Q. (2026). GeoAI: A Python package for integrating artificial intelligence with geospatial data analysis and visualization. Journal of Open Source Software, 11(118), 9605. https://doi.org/10.21105/joss.09605 The QGIS plugin from which QMaxent's dependency-installation workflow in core/venv_manager.py was adapted — in particular the _get_qgis_python() pattern that resolves the real Python interpreter on Windows (where sys.executable points to qgis-bin.exe) and the subprocess pipe handling used during pip install.

Cross-validation and model evaluation

Roberts, D. R., et al. (2017). Cross-validation strategies for data with temporal, spatial, hierarchical, or phylogenetic structure. Ecography, 40(8), 913–929. The paper that established spatial cross-validation as the default for SDMs — directly shaping QMaxent's Geographic K-Fold default and the warning text in The Parameters tab.

Hijmans, R. J. (2012). Cross-validation of species distribution models: removing spatial sorting bias and calibration with a null model. Ecology, 93(3), 679–688. Provides the formal argument for buffered leave-one-out CV when sample sizes are small; QMaxent's *Buffered LOO option implements this.*

Valavi, R., Elith, J., Lahoz-Monfort, J. J., & Guillera-Arroita, G. (2019). blockCV: An R package for generating spatially or environmentally separated folds for k-fold cross-validation of species distribution models. Methods in Ecology and Evolution, 10(2), 225–232. The block-CV recipes implemented by QMaxent's Checkerboard option.

Muscarella, R., et al. (2014). ENMeval: An R package for conducting spatially independent evaluations and estimating optimal model complexity for Maxent ecological niche models. Methods in Ecology and Evolution, 5(11), 1198–1205. The hyperparameter-tuning framework whose checkerboard partitioning is implemented as a QMaxent CV option.

Kass, J. M., et al. (2018). Wallace: A flexible platform for reproducible modeling of species niches and distributions built for community expansion. Methods in Ecology and Evolution, 9(4), 1151–1156. The interactive ENM platform whose hyperparameter-tuning workflow QMaxent intentionally complements rather than duplicates.

Sample bias correction

Phillips, S. J., et al. (2009). Sample selection bias and presence-only distribution models: implications for background and pseudo-absence data. Ecological Applications, 19(1), 181–197. The paper underlying QMaxent's *Down-weight spatially clustered points option in the Parameters → Advanced section.*

Boria, R. A., Olson, L. E., Goodman, S. M., & Anderson, R. P. (2014). Spatial filtering to reduce sampling bias can improve the performance of ecological niche models. Ecological Modelling, 275, 73–77. Cited in Data tab as the reason to spatially-thin presence layers before modeling.

Elith, J., Kearney, M., & Phillips, S. (2010). The art of modelling range-shifting species. Methods in Ecology and Evolution, 1(4), 330–342. The MESS-style extrapolation analysis whose logic underlies QMaxent's unified projection-preflight dialog.

SDM best-practice reviews

Elith, J., et al. (2011). A statistical explanation of MaxEnt for ecologists. Diversity and Distributions, 17(1), 43–57. The standard pedagogical reference for understanding what Maxent does and does not assume.

Merow, C., Smith, M. J., & Silander Jr, J. A. (2013). A practical guide to MaxEnt for modeling species' distributions: what it does, and why inputs and settings matter. Ecography, 36(10), 1058–1069. The companion practical guide to Elith et al. 2011.

Araújo, M. B., et al. (2019). Standards for distribution models in biodiversity assessments. Science Advances, 5(1), eaat4858. The IPBES-aligned reporting standard QMaxent's XLSX export was designed to satisfy. Cited throughout this manual.

Radosavljevic, A., & Anderson, R. P. (2014). Making better Maxent models of species distributions: complexity, overfitting and evaluation. Journal of Biogeography, 41(4), 629–643. The empirical paper documenting how feature-class tuning without spatial CV inflates AUC; underlies the warning text in the Parameters tab about the Auto rule.

Survey design

Williams, J. N., et al. (2009). Using species distribution models to predict new occurrences for rare plants. Diversity and Distributions, 15(4), 565–576. The original *Discovery-mode survey-design paper whose workflow QMaxent implements in the Priority Sites tab.*

Rhoden, C. M., Peterman, W. E., & Taylor, C. A. (2017). Maxent- directed field surveys identify new populations of narrowly endemic habitat specialists. PeerJ, 5, e3632. The model-validation companion to Williams et al. 2009; QMaxent's *Model validation mode reproduces this design.*

Stevens, D. L. Jr., & Olsen, A. R. (2004). Spatially balanced sampling of natural resources. Journal of the American Statistical Association, 99(465), 262–278. The spatially-balanced-sampling theory underlying QMaxent's between-candidate spacing constraint.

Robinson, N. M., et al. (2018). Refining survey effort: how detection probability shapes the design of biodiversity surveys. Methods in Ecology and Evolution, 9(3), 575–587. Cited in Priority Sites for typical survey-budget heuristics.

Worked example reference

Lee, S.-J., et al. (2025). Breeding habitat prediction and nest-site characteristics of the fairy pitta (Pitta nympha) in Geoje-si, South Korea — Insights from a species distribution model. Global Ecology and Conservation, 64, e03939. The published Java-MaxEnt analysis reproduced in the Pitta nympha worked example.

How QMaxent cites itself

If you publish a study using QMaxent, please cite:

Yu, B.-H. (2026). QMaxent: Maxent species distribution modeling in QGIS. Software. https://github.com/osgeokr/qmaxent

The exact CITATION.cff is the canonical source on the repository.