Modelling species distributions using regression quantiles

By S. Vaz, C. Martin, P. Eastwood, Bruno Ernande, A. Carpentier, G. Meaden, F. Coppin. 2007

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Using species distribution models in paleobiogeography: A matter of data, predictors and concepts (2011, background): “These techniques can be divided into: (i) those based on the statistical fitting of data to the theoretical relationships between the species' presence and the environment (e.g. GLMz, GAMs, PLS, CART, andMaxEnt; Austin et al., 1990; Yee and Mitchell, 1991; Austin, 2002; Vaz et al., 2008; Carrascal e”
Understanding what controls the spatial distribution of fish populations using a multi-model approach (2011, methodology): “…to the upper distribution quantiles, the method is well suited to model potential habitat in which environment variables can be used to predict potentially suitable areas but are not used to predict the average population response (Vaz et al., 2008, Eastwood et al., 2001, Eastwood et al., 2003).”
Linking temperate demersal fish species to habitat: scales, patterns and future directions (2013, background): “Development of these approaches provides promising areas for future development (see Pittman et al. 2004; Vaz et al. 2008; Hermoso et al. 2011; Johnson et al. 2012).”
Benthos distribution modelling and its relevance for marine ecosystem management (2015, methodology): ““Factorceiling” (Thrush et al., 2003), quantile regression (Cade and Noon, 2003; Vaz et al., 2008), and machine-learning methods recently introduced for DM such as random forest and boosted regression trees (Table 1) are also considered here.”