A predictive approach to assess urban biodiversity and plan for future development scenarios

Abstract Protecting and enhancing biodiversity in urbanized areas is recognized as an important priority. To achieve this through urban planning, there must be empirically derived tools to predict biodiversity at the appropriate spatial scales and resolutions, given various options in urban designs to compare the expected biodiversity outcomes and make optimal decisions. We demonstrate how this can be done by developing models that predict the expected species densities or ‘alpha diversity’ in urban landscapes for four animal groups: birds, butterflies, odonates and amphibians, based on assemblage data from spatiotemporally replicated surveys conducted in the tropical city of Singapore. We demonstrate two use cases for these predictive models: citywide assessment and future scenario planning. For citywide assessment, sub‐city ‘towns’ (equivalent to districts or suburbs elsewhere) were compared and benchmarked relative to all other towns, based on the average species densities as indicators of habitat value for each of the four animal groups. For future scenario planning, four development scenarios were compared and the compatibility of vector‐type planning layers with the models was tested. An open‐source R package, biodivercity, was developed that would facilitate the use of the same workflow elsewhere: to build, apply and validate predictive models elsewhere given similar available empirical data. Synthesis and applications . The models developed can also be examined to generate recommendations for further actions that can improve biodiversity across different spatial scales. These techniques can be incorporated into current planning practices to achieve a more quantitative and performance‐based approach to enhancing biodiversity at fine spatial scales in human‐dominated landscapes.