Impact of model complexity on cross-temporal transferability in Maxent species distribution models: An assessment using paleobotanical data
By E. Moreno-Amat, Rubén G. Mateo, D. Nieto‐Lugilde, N. Morueta‐Holme, Jens‐Christian Svenning, I. García-Amorena. 2015
184 citations recorded by the source corpus.
Subfields: Ecological Modeling, Ecology, Evolution, Behavior and Systematics, Nature and Landscape Conservation
View Paper: https://www.semanticscholar.org/paper/2883672268337905696222a3b63805d5159a082c
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
- Jane Elith
- Wilfried Thuiller
- A. Townsend Peterson
- Antoine Guisan
- Miguel B. Araújo
- Risto Heikkinen
- Jorge M. Lobo
- Carsten F. Dormann
In Ecology, Evolution, Behavior and Systematics
- Jane Elith
- Wilfried Thuiller
- A. Townsend Peterson
- C. Randin
- Risto Heikkinen
- Carsten F. Dormann
- Damaris Zurell
- Phil J. Bouchet
In Nature and Landscape Conservation
- Jane Elith
- Wilfried Thuiller
- A. Townsend Peterson
- Antoine Guisan
- Miguel B. Araújo
- Risto Heikkinen
- Jorge M. Lobo
- Carsten F. Dormann
Citations in the corpus
- Outstanding Challenges in the Transferability of Ecological Models. (2018, background): “often do not extrapolate well to novel data [35].”
- Predictor complexity and feature selection affect Maxent model transferability: Evidence from global freshwater invasive species (2020, methodology): “While AICc has been shown to be useful in restricting models to a reasonable range of complexity (Moreno-Amat et al., 2015; Warren & Seifert, 2011), it can bias model selection towards certain feature types owing to its use of non-zero Maxent parameters as an estimate of degrees of freedom (Hastie…”