The challenge of modeling niches and distributions for data‐poor species: a comprehensive approach to model complexity
By Peter J. Galante, Babatunde Alade, Robert Muscarella, S. Jansa, S. Goodman, R. P. Anderson. 2018
149 citations recorded by the source corpus.
Subfields: Ecological Modeling, Ecology, Genetics
View Paper: https://www.semanticscholar.org/paper/82d3bef11be38731038e7b9f5a134d2839947650
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Ecological Modeling
- Robert A. Boria
- D. Yeo
- Yiwen Zeng
- B. Low
- Peter J. Galante
- Robert Muscarella
- H. Tan
- Mariano Soley-Guardia
In Ecology
- Robert A. Boria
- Yiwen Zeng
- D. Yeo
- B. Low
- Peter J. Galante
- Robert Muscarella
- H. Tan
- Mariano Soley-Guardia
In Genetics
Citations in the corpus
- Predictor complexity and feature selection affect Maxent model transferability: Evidence from global freshwater invasive species (2020, background): “…exhibit poorer performance in some cross-validation transferability assessments (e.g., slightly lower discriminatory ability on withheld testing data; Muscarella et al., 2014), although this may be confounded by geographic sampling bias present within training datasets (Galante et al., 2018).”
- ENMeval 2.0: Redesigned for customizable and reproducible modeling of species’ niches and distributions (2021, background): “Lastly, AICc can also be considered along with other performance metrics (Galante et al., 2018), and ENMeval 2.0 both provides more than the earlier version and allows users to add others.”