Supporting the planning of urban blue-green infrastructure for biodiversity: A multi-scale prioritisation framework.

Primary considerations for urban blue-green infrastructure (BGI) encompass sustainable stormwater/urban heat management while biodiversity conservation is often considered an inherent benefit rather than a core planning requirement. However, ecological function of BGI as 'stepping stones' or linear corridors for otherwise fragmented habitats is undisputed. While quantitative approaches for modelling ecological connectivity in conservation planning are well established, mismatches in scope and scale with models that support the planning of BGI makes their adoption and integration difficult across disciplines. Technical complexities have led to ambiguity around circuit and network-based approaches, focal node placement, spatial extents, and resolution. Furthermore, these approaches are often computationally intensive, and considerable gaps remain in their use for identifying local-scale critical "pinch-points" that urban planners may respond to with the integration of BGI interventions that address biodiversity enhancement among other ecosystem services. Here, we present a framework that simplifies and integrates the merits of regional connectivity assessments with a focus on urban areas to prioritise BGI planning interventions while reducing computational demands. Our framework facilitates: (1) modelling potential ecological corridors at a coarse regional scale, (2) prioritising local-scale BGI interventions based on the relative contribution of individual nodes in this regional network, and (3) inferring connectivity hot- and cold-spots for local-scale BGI interventions. We illustrate this in the Swiss lowlands, demonstrating how, compared to previous work, we are able to identify and rank different priority locations across the region for BGI interventions in support of biodiversity enhancement and how their local-scale functional design may be benefited by addressing specific environmental variables.