Mapping tick-borne hazard across gradients of urban intensity in metropolitan regions

Urban greenspaces are ecologically novel habitats where wildlife movement can shape vector-borne disease risk. Understanding how landscape structure influences tick presence and infection risk is essential for improving hazard assessment in metropolitan regions.

We assessed Lyme disease hazard, defined as the density of Borrelia-infected Ixodes scapularis nymphs, across 141 greenspaces in New York City-Long Island (NYC-LI) and Greater Boston during 2023-2024. Sites were selected using a stratified design along gradients of housing density and wildlife functional connectivity. Active tick surveillance data were analyzed using spatial generalized linear mixed models that accounted for landscape composition, landscape configuration, and weather-related variables, as well as spatial autocorrelation. Continuous hazard maps were generated for each region.

Tick hazard declined with increasing housing density across both metropolitan regions. Percent tree canopy cover at the 100-m scale was positively associated with tick presence and nymph density, whereas percent impervious surface cover at the 1000-m scale showed a consistent negative association with hazard. Functional connectivity modified these relationships, with greenspaces embedded in more connected landscapes maintaining elevated hazard under higher levels of urbanization. Despite overall tick densities being lower in 2024, the spatial pattern of hazard in NYC-LI were similar between years, with higher hazard concentrated in eastern Long Island and central Staten Island. External validation in Greater Boston demonstrated comparable spatial patterns and good model performance, supporting cross-region transferability.

Landscape composition and configuration jointly shape the distribution of tick vectors in urban environments. This standardized, reproducible, spatially explicit workflow advances tick hazard assessment in urban setting and, when integrated with human behavioral data, can improve exposure estimation and inform more targeted Lyme disease prevention.