This project aims to evaluate the urban factors that may influence biodiversity visitation rates in Edmonton by extracting data from the WildEdmonton program monitoring network. After filtering images using human interpreters and artificial intelligence algorithms, only wildlife images were saved and recorded in a spreadsheet. To avoid duplication, a modified version of the CamTrapR code was applied to the data, resulting in a comprehensive list of species and their corresponding frequencies recorded by each remote camera. Then, urban factors of interest were extracted using the geographic coordinates of the cameras and combined with the biodiversity data to create a final table used in this research project (Figure 5).

The research objective includes four urban factors (Figure 8): the road network, trails surrounding the city, various types of water bodies, and land cover updated to 2021. These factors are described as follows:

• Roads Network: This is the Single Street Network shapefile published by the City of Edmonton in July 2021 (City of Edmonton, 2017). From this shapefile, arterial, collector, and local roads were selected.  

• Trails Network:  The Open Space Strategy unit of the City of Edmonton has generated a polyline shapefile that maps out both approved and informal trails. However, the informal trail data is not publicly available, and it has not undergone validation via fieldwork. 

• Water bodies: Hydrographic features mapped by the City of Edmonton in shapefile format.  

• Land Cover: The Land Cover dataset used in this research project was created as part of the Urban Primary Land Vegetation Inventory (uPVLI) framework (City of Edmonton, 2015), developed by the City of Edmonton. This framework defines six distinct land cover classes for the years 2005, 2015, 2018, and 2021. In this study, the 2021 dataset was utilized. 

For the first research question, gradient analysis was applied to establish associations between two data sets, namely urban factors and wildlife visitation rates. In this case, the biological response was explained by the urban factors gradient. The direct approach gradient was utilized, which involves ordinating the predictor variable (urban factors) first and then examining how the response variables (wildlife visits per day) are associated with that ordination. This approach allows for the exploration of the relationship between urbanization and biodiversity and provides insights into how the environment is affected by urbanization. 

The second research question was addressed using the multivariate regression tree (MRT) approach to investigate the association between multiple predictor variables (urban features) and multiple response variables (visitation rates of different species). This method identifies groups of similar observations, but differentiates these groups by imposing the predictor variables as a constraint to create clusters. For instance, a threshold for the distance from the city centre and the nearest trail (as predictor variables) can be estimated, which is then utilized to distinguish the frequency of the most commonly identified mammal species (as response variables). The analysis is carried out to determine whether these species are more or less detected depending on their distance from the threshold. These predicted thresholds will serve as baseline values to support future strategies for biodiversity protection and urban planning.