Road networks and human density are major factors contributing to habitat fragmentation and loss, isolation of wildlife populations and reduced genetic diversity. Terrestrial mammals are particularly sensitive to road networks and encroachment by human populations. However, there are limited assessments of the impacts of road networks and human density on population-specific nuclear genetic diversity, and it remains unclear how these impacts are modulated by life history traits. Using generalized linear mixed models and microsatellite data from 1444 North American terrestrial mammal populations we show that taxa with large home range sizes, dense populations, and large body sizes had reduced nuclear genetic diversity with increasing road impacts and human density, but the overall influence of life history traits was generally weak. Instead, we observed a high degree of genus-specific variation in genetic responses to road impacts and human density. Human density negatively affected allelic diversity or heterozygosity more than road networks (13 versus 5-7 of 25 assessed genera, respectively); increased road networks and human density also positively affected allelic diversity and heterozygosity in 15 and 6-9 genera, respectively. Large bodied, human-averse species were generally more negatively impacted than small, urban-adapted species. Genus-specific responses to habitat fragmentation by ongoing road development and human encroachment likely depend on the specific capability to (i) navigate roads as either barriers or movement corridors, and (ii) exploit resource-rich urban environments. The non-uniform genetic response to roads and human density highlights the need to implement efforts to mitigate the risk of vehicular collisions, while also facilitating gene flow between populations of particularly vulnerable taxa.

Microsatellite genetic data was derived from the MacroPopGen database (Lawrence et al. 2019).

Human population density was derived from the Gridded Population of the World dataset (Center for International Earth Science Information Network 2011).

Road impact data was assigned to each population by processing road networks data from gROADS (Center for International Earth Science Information Network 2013) dataset and multiplying the average annual daily traffic estimate from the Highway Performance Monitoring System (available from: https://catalog.data.gov/dataset/highway-performancemonitoring-system-hpms-2011-nhs-cd4a2) by road class.