Integrating species distribution models (SDM) in spatially explicit GIS-tools to support nature-sensitive urban planning

Abstract

Urban areas are expanding globally, presenting challenges and opportunities for biodiversity conservation and ecosystem services. To inform biodiversity-sensitive urban planning, predictive and spatially explicit tools that integrate ecological modelling into operational workflows are needed. We present a framework that couples ensemble species distribution models (ESDMs) with a QGIS-based decision-support tool to evaluate urban habitat suitability for conservation-relevant bird species. We designed this approach in the Ruhr Metropolis in western Germany, a polycentric urban region characterized by high population density and diverse land use. ESDMs were developed for 26 bird species across five ecological guilds (Farmland, Openland, Synanthropic, Water, Woodland) using presence-only citizen science data and high-resolution environmental predictors. Models were constructed using biomod2, with pseudoabsences, spatial thinning, and block cross-validation to account for sampling bias and spatial autocorrelation. Logistic regression models for these five guilds were implemented in the open-source QGIS-based toolbox EnhancES for assessing ecosystem services to allow real-time spatial evaluation of habitat suitability.

Model performance was TSS ≥ 0.58 and ROC ≥ 0.78 across all species guilds; suitability predictions aligned well with ecological expectations. Applying the tool to the city of Bochum with the example of the Green Belt E demonstrated its ability to identify multifunctional biodiversity hotspots and conservation priorities.

This study demonstrates how ecological modelling outputs can support integrative biodiversity assessments and be translated into practical spatial planning tools. By embedding the results of predictive models into accessible GIS frameworks such as EnhancES, we enable urban planners to evaluate ecosystem services and habitat functions, fostering data-driven decisions in spatial planning. Predictive habitat suitability outputs as part of an ecosystem service assessment scheme inform spatial prioritization, guide urban green infrastructure design, and evaluate planning scenarios. By linking ecological modelling with decision-relevant GIS-tools, this framework bridges the science-practice gap and facilitates data-driven planning for resilient, multifunctional urban landscapes.