Strengths and Limitations

As with any scientific analysis tool, there are a variety of strengths and weaknesses associated with using an MCE model. First and foremost, utilizing an MCE model enables users to analyze the complex relationships associated with different environmental and socio-economic impacts. This information is then used to better understand the trade-offs that will need to be evaluated when making decisions affecting the environment and surrounding ecosystems. For the context of this study, the weighted overlay technique enables the analysis of multiple different factors that contributed to the organization of important criteria and constraint factors influencing moose habitat identification. The use of the Euclidean distance analysis tool is also a major strength in this analysis. This tool helps to measure the distance of important factors like proximity to wetlands, forest cover, past fire events, roads, and non-forest cover in relation to other, in order to derive the most desired location.

During the analysis of this study, there are certain limitations that influence the effectiveness of the final output. The first possible limitation is with regards to the historical fire location data that is used. This data only documents fire information between 1980 and 1996, in which variables such as smoke extent and specific vegetation species available in zone 8 are unable to be obtained.  It is assumed that the moose would not live in an area that has a large smoke extent. However, it is extremely difficult to calculate this extent due to external factors, such as the change in wind speed and direction. Furthermore, information on vegetation type is found, but not specific vegetation species. In this case, it is assumed that moose eat a diet of shrubs, tall grasses and tree buds year-round, which generalizes the analysis. In doing so, this could potentially create inaccuracies when conducting the pairwise analysis, considering that an ideal food source is a critical variable in the decision matrix. This judgment can then have the potential to derive incorrect data moving forward with the analysis when determining the appropriate ratios for conducting the MCE analysis (Kumar et al., 2014). Finally, it is important to note that the weights that are applied to specific factors directly influence the outcomes produced, so removing any single factor has the prospect of changing the distribution of potential habitats within the study area. Therefore, it is essential to ensure that all factors are considered when determining the suitability of habitable land based on the determined weights (Eastman, 1999).