Modelling and predicting fungal distribution patterns using herbarium data

136 citations recorded by the source corpus.

How researchers see this paper

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

A strict maximum likelihood explanation of MaxEnt, and some implications for distribution modelling (2013, methodology): “Strong faith in ℓ1regularisation with default tuning as a guarantor against overfitting is expressed in many studies ( e.g., Hernandez et al. 2006, Parolo et al. 2008, Wisz et al. 2008, Wollan et al. 2008, Merckx et al. 2011, Rupprecht et al. 2011).”
How biotic interactions may alter future predictions of species distributions: future threats to the persistence of the arctic fox in Fennoscandia (2012, background): “Regularization deals with the selection of environmental variables (regulating some to zero) and has shown to perform well and likely outperforms other modelling procedures that pre-select variables (Wollan et al., 2008).”
Climate variation effects on fungal fruiting (2014, methodology): “Distribution (niche) model approaches (Wollan et al., 2008), such as MaxEnt (Phillips et al., 2006), are useful ways to learn more about fungal distribution patterns.”
A statistical explanation of MaxEnt for ecologists (2011, methodology): “groups of species, or other Norway Macrofungi Wollan et al. (2008)”
The usefulness of elevation as a predictor variable in species distribution modelling (2012, methodology): “This application has shown to perform well and outperorms other methods to pre-select variables (Wollan et al., 2008).”