Traffic density and noise shape wildlife movement in the city: insights from modeling blackbird connectivity in a dense city
Year: 2026
Type: preprint
Abstract: Context Traffic infrastructure fragments wildlife habitats and populations, yet its barrier effects are comparatively understudied in cities where such infrastructure is dense. Connectivity models can quantify these barrier effects and inform restoration but typically rely on land cover and road type information, omitting ecologically relevant factors such as traffic density and noise. Objectives We aimed to assess the barrier effects of urban traffic infrastructure on wildlife movement, divided into three components: sealed land cover, traffic density, and noise levels. We aimed to evaluate whether urban connectivity models need updating to better reflect traffic infrastructure effects on wildlife. Methods We extended a data-driven framework and used the common blackbird ( Turdus merula ) in Munich as a case study. We compared multiple functions to transform traffic density and noise into landscape resistances, using empirical movement-proxy data to select the resistance set best describing the barrier effect. We subsequently validated the resulting connectivity model against independent movement-proxy data. Results Connectivity models incorporating traffic and noise substantially outperformed previous land-cover-only models (R² = 0.29 vs. 0.17). High traffic density and noise above 70 dB(A) acted as barriers, with effects extending beyond the linear traffic network into otherwise suitable habitats. Our results indicate that current urban connectivity models underestimate fragmentation caused by traffic density and noise. Conclusions Traffic and noise provide underutilized levers for wildlife conservation. Our approach provides a transferable framework, applicable to other cities and species, to better understand the role of traffic for functional connectivity.
Source: Research Square
Institutions: Technical University of Munich, Université d'Angers
Language: English
Subfield: Ecology
Field: Environmental Science
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W7165572097