Case study conducted by Kharouba et. al. used a climate and land use change scenario in Canada for a pseudo experiment to test model reliability for predicting species range shifts over long time periods (30-60 years) and very large geographical areas for 297 butterfly species. They used historical distribution data with six environmental predictor variables over a 10 million km2 range and modeled with MaxEnt. Steps included: generating a historic species distribution model (1900-1930), projecting these models with environmental data from 1960-1990 (projected model), model species distribution using current environmental data and species occurrence records (current model), and test the ability of using projected models to predict the current models (by comparing actual current distribution) and determine whether this method is suitable to predict species distribution change over time. The accuracy of each model was determined using AUC. Models that constructed historic and current species distribution individually had high value AUC, but when historic model was used to project current distribution, it both underestimated and overestimated suitable habitat when actually compared to the current distribution. Results depended on the species of interest and how that species responds to environmental change. Using this method to predict future distribution in response to climate change can be considered reliable, but projection accuracy depends on scale (pixel vs. region). Other factors to be considered when using this method of modeling, or could make this method even stronger, should include plant responses (butterfly resources) to climate change, feeding habits of the butterfly (i.e. generalist vs. specialist butterflies), species traits and their responses to climate change, and species response to community-level changes.