Limitations

          A primary limitation of any study like this is the spatial resolution of the data. Although the whitebark pine range data was acquired in a raster with a spatial resolution of 1 km, trying to use the similar (30 seconds) spatial resolution for the climatic variables proved too much for our computational power. Therefore, we elected to use a 5 minute resolution. 

          For the occurrence data for the Clark's nutcracker, the biggest worry is the quality of the sightings, and by extent the data. Although the dataset used was an agglomerate from a number of sources (and included both observations and preserved specimen occurrences), from a visual inspection of both the dataset in Excel, and the point data in ArcGIS Pro, the data seemed of a reasonable standard. However, even if it may show biases in terms of where the data was collected, the bias file created in R should deal with that, at least somewhat. 

          Although we choose the 19 bioclimatic variables and elevation instead of using monthly environmental layers to avoid correlation, this is still a large number of variables, and correlation could still be a problem. Albeit, we hope it will be less so. Ideally we would look for correlations and excluded laters, however this also would require an intimate understanding of the species so as to remove the layers that are known not to affect them the most. We did attempt to check for correlation between all the layers using R studio however we were unable to get the code to run successfully. 

          One major limitation of this study was that we did not look at the species distribution for the two species under climate change scenarios in the future. This is a place for future research.