Modelling changes in biodiversity have become a necessary component of smart urban planning practices. However, concepts such as biodiversity are often evaluated using area-based composite indices, the results of which are heavily reliant on specific parameters chosen. This paper explores the design and implementation of a butterfly biodiversity index by comparing two widely accepted modelling techniques: principal component analysis and spatial multi-criteria decision analysis (MCDA). A high degree of scale dependency has been demonstrated in previous studies exploring the use of area-based composite measures. To evaluate the impact of scale, each model was assessed at two different spatial resolutions. The outcomes were analyzed, mapped and compared using ordinary least squares, geographically weighted regression and global Moran?s I to evaluate relative biodiversity patterns across the City of Toronto, Canada. Findings indicate that the impact of spatial scale was significant, whereby the coarser resolution models were found to be more highly correlated with biodiversity, compared to the finer resolution models. The results of this study contribute to a growing body of literature that explores key conceptual questions regarding the robustness of GIS-based MCDA, the impact of scale in urban ecology studies, and the use of composite indices to manage spatial ecological data.
