One of the main inputs into a niche model is the environmental variables. Optimizing the choice of variables is important for many reasons, primarily interpretation and subsequent accuracy on independent test data.

In almost all cases to date, annual climate averages have been used in modeling species distributions. Where models have been developed and annual averages of climate compared with monthly variables and others such as vegetation, improvements in the accuracy were attributed to the monthly climate data sets (i.e. greater temporal resolution).

The table below from Improving ecological niche models by data mining large environmental datasets for surrogate models (Ecological Modeling) lists the variables selected in the models for the 6 species and clearly shows variables other than annual temperature averages are selected. In this case, a variable called treecover is selected most frequently (3 times), while mgvc188, the first eigenvector of vegetation, is selected next most frequently (2 times) and the rest were monthly climate variables.

Only one annual variables was selected. These are typical of all the results I get these days, where annual climate averages are rarely selected as predictor variables. It makes sense. Why would a niche species be well modeled by a generic variable? Rather a niche variable should be the most predictive.