Constrain to perform: Regularization of habitat models

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The performance of state-of-the-art modelling techniques depends on geographical distribution of species. (2009, background): “…al. (2005) showed that the predictive power of regression trees (RT) was highest when modelling species with high prevalence, and Dormann (2007) and Reineking and Schröder (2006) showed that different autologistic regression methods were severely impacted by the prevalence of species, such models…”
A strict maximum likelihood explanation of MaxEnt, and some implications for distribution modelling (2013, methodology): “In terms of parameter values for the optimisation criterion given by (44), Reineking & Schröder (2006) show that the λ of the F-ratio test corresponds to the (1 – α)-quantile of the χ2 distribution with 1 degree of freedom.”
A working guide to boosted regression trees. (2008, methodology): “In conventional regression, this is applied as global shrinkage (direct, proportional shrinkage on the full model) using ridge or lasso methods (Hastie et al . 2001; Reineking & Schroder 2006 ).”