kuenm

kuenm facilitates development and calibration of ecological niche models (ENMs) using Maxent for species distribution modeling and extrapolation risk assessment.


Key Features:

  • Detailed Model Calibration: Implements calibration workflows that select optimal parameters based on statistical significance, predictive power, and model complexity.
  • Model Creation and Evaluation: Generates final models from multiple parameter sets and enables model transfers for projection to other scenarios.
  • Extrapolation Risk Analysis: Evaluates extrapolation risk in model transfers using the mobility-oriented parity (MOP) metric and the MESS (Minimum Estimated Sample Size) metric to assess strict-extrapolation risks.
  • Reproducibility and Efficiency: Integrates with R and Maxent to enable reproducible model calibration and evaluation and to accelerate calibration and transfers across scenarios.

Scientific Applications:

  • Biodiversity Conservation: Predicts species distributions under various environmental conditions to inform conservation planning.
  • Climate Change Impact Studies: Assesses potential impacts of climate change on species habitats by evaluating extrapolation risks during model transfers.
  • Ecological Research: Supports development of niche models for studying ecological dynamics and species interactions.

Methodology:

Performs systematic model calibration, selection, and evaluation with parameter optimization based on statistical significance, predictive power, and model complexity; creates final models from multiple parameter sets and conducts model transfers; evaluates extrapolation risk using MOP and MESS; implemented in R and interfacing with Maxent.

Topics

Details

Maturity:
Mature
Cost:
Free of charge
Tool Type:
library
Operating Systems:
Linux, Windows, Mac
Programming Languages:
R
Added:
5/19/2019
Last Updated:
6/16/2020

Operations

Publications

Cobos ME, Peterson AT, Barve N, Osorio-Olvera L. kuenm: an R package for detailed development of ecological niche models using Maxent. PeerJ. 2019;7:e6281. doi:10.7717/peerj.6281. PMID:30755826. PMCID:PMC6368831.

PMID: 30755826
PMCID: PMC6368831
Funding: - PAPIIT UNAM: IN116018 - CONACyT-FORDECyT: 273646