ENMeval (version 0.1.0)

ENMeval-package: Automated runs and evaluations of ecological niche models

Description

Automatically partitions data into bins for model training and testing, executes ecological niche models (ENMs) across a range of user-defined settings, and calculates evaluation metrics to help achieve a balance between goodness-of-fit and model complexity.

Details

ll{ Package: ENMeval Type: Package Version: 0.1.0 Date: 2014-08-25 License: GNU 3.0 }

The ENMeval package (1) automatically partitions data into training and testing bins using one of six methods (including several options for spatially independent partitions as well as user-defined bins), (2) executes a series of ENMs using Maxent (Phillips et al. 2006) with a variety of user-defined settings (i.e., feature classes and regularization multipliers), conducting k-fold cross validation, and (3) calculates multiple evaluation metrics to aid in selecting model settings that balance model goodness-of-fit and complexity (i.e., "model tuning" or "smoothing").

ENMevaluate is the primary function of the ENMeval package, and multiple other functions highlighted below are called when it is run. The six options for partitioning occurrence data into training and testing (i.e., calibration and evaluation) bins are: n-1 jackknife, random k-fold, user-specified bins, and three explicit methods of masked geographically structured k-fold partitioning (see: get.evaluation.bins). After model training, these bins are used to calculate five metrics of model performance for each combination of settings: model discrimination (AUC of test localities), the difference between training and testing AUC, two different threshold-based omission rates, and the small sample-size corrected version of the Akaike information criterion (AICc), the latter using the unpartitioned dataset. A model prediction (as a raster layer) using the full (unpartitioned) dataset is generated for each combination of feature class and regularization multiplier settings. Similarity of these models in geographic space (i.e., "niche overlap") can be calculated to better understand how model settings change predictions (see calc.niche.overlap). The results of ENMevaluate are returned as an object of class ENMevaluation-class. A basic plotting function (eval.plot) can be used to visualize how evaluation metrics depend on model settings.

References

Hijmans, R. J., Phillips, S., Leathwick, J. and Elith, J. 2011. dismo package for R. Available online at: http://cran.r-project.org/web/packages/dismo/index.html.

Phillips, S. J., Anderson, R. P., and Schapire, R. E. 2006. Maximum entropy modeling of species geographic distributions. Ecological Modelling, 190: 231-259.

Phillips, S. J., and Dudik, M. 2008. Modeling of species distributions with Maxent: new extensions and a comprehensive evaluation. Ecography, 31: 161-175.

See Also

maxent in the dismo package