Data-integration of opportunistic species observations into hierarchical modeling frameworks improves spatial predictions for urban red squirrels

Abstract

The prevailing trend of increasing urbanization and habitat fragmentation makes knowledge of species’ habitat requirements and distribution a crucial factor in conservation and urban planning. Species distribution models (SDMs) offer powerful toolboxes for discriminating the underlying environmental factors driving habitat suitability. Nevertheless, challenges in SDMs emerge if multiple data sets - often sampled with different intention and therefore sampling scheme – can complement each other and increase predictive accuracy. Here, we investigate the potential of using recent data integration techniques to model potential habitat and movement corridors for Eurasian red squirrels (Sciurus vulgaris), in an urban area. We constructed hierarchical models integrating data sets of different quality stemming from unstructured on one side and semi-structured wildlife observation campaigns on the other side in a combined likelihood approach and compared the results to modeling techniques based on only one data source - wherein all models were fit with the same selection of environmental variables. Our study highlights the increasing importance of considering multiple data sets for SDMs to enhance their predictive performance. We finally used Circuitscape (version 4.0.5) on the most robust SDM to delineate suitable movement corridors for red squirrels as a basis for planning road mortality mitigation measures. Our results indicate that even though red squirrels are common, urban habitats are rather small and partially lack connectivity along natural connectivity corridors in Berlin. Thus, additional fragmentation could bring the species closer to its limit to persist in urban environments, where our results can act as a template for conservation and management implications.