Dozens of methods are routinely used in the modeling of species distribution and abundance (e.g., Guisan and Zimmerman 2000, Elith et al. 2006, Elith et al. 2007). To address uncertainties relating to model type, analysts are increasingly relying on ensemble modeling (Arau´jo et al. 2005, Arau´jo and New 2007, Jones-Farrand et al. 2011) within a consensus modelling framework (Marmion et al. 2009). Ensemble modeling averages over methods - and if models are weighted according to expert or model weights, predictions reflect results from the best supported models. Nevertheless, species, context, and data type all influence what is ultimately determined to be the best model. Hierarchical modeling, as described by Graves et al., is a coherent means of conjointly modeling multiple methods in the face of the uncertainty created by these various influences.

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