kuenm: an R package for detailed development of ecological niche models using Maxent

By M. Cobos, A. Townsend Peterson, N. Barve, Luis Osorio-Olvera. PeerJ, 2019

852 citations recorded by the source corpus.

Background Ecological niche modeling is a set of analytical tools with applications in diverse disciplines, yet creating these models rigorously is now a challenging task. The calibration phase of these models is critical, but despite recent attempts at providing tools for performing this step, adequate detail is still missing. Here, we present the kuenm R package, a new set of tools for performing detailed development of ecological niche models using the platform Maxent in a reproducible way. Results This package takes advantage of the versatility of R and Maxent to enable detailed model calibration and selection, final model creation and evaluation, and extrapolation risk analysis. Best parameters for modeling are selected considering (1) statistical significance, (2) predictive power, and (3) model complexity. For final models, we enable multiple parameter sets and model transfers, making processing simpler. Users can also evaluate extrapolation risk in model transfers via mobility-oriented parity (MOP) metric. Discussion Use of this package allows robust processes of model calibration, facilitating creation of final models based on model significance, performance, and simplicity. Model transfers to multiple scenarios, also facilitated in this package, significantly reduce time invested in performing these tasks. Finally, efficient assessments of strict-extrapolation risks in model transfers via the MOP and MESS metrics help to prevent overinterpretation in model outcomes.

Subfields: Artificial Intelligence, Ecological Modeling, Environmental Engineering

View Paper: https://www.semanticscholar.org/paper/de8cc37b853176a3dbe1bc139bf75dc4d4eb4561

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

In Ecological Modeling

In Environmental Engineering

Citations in the corpus

ntbox: An r package with graphical user interface for modelling and evaluating multidimensional ecological niches (2020, methodology): “…it performs a partial ROC test using an environmental background provided by the user; model performance is computed as omission rates for both training and testing data (Cobos, Peterson, Barve, et al., 2019; Cobos, Peterson, Osorio-Olvera, et al., 2019; Osorio-Olvera, Yañez-Arenas, et al., 2020).”
A standard protocol for reporting species distribution models (2020, methodology): “We define model complexity as the flexibility of the fitted biodiversity–environment relationship (cf. Merow et al. 2014, Muscarella et al. 2014, Cobos et al. 2019).”