A multitude of smart phone and more traditional tools are used with increasing frequency by volunteers on roads for long-term monitoring of wildlife. Data collected by volunteers on roads has recently indicated large-scale declines of some widespread amphibians in Western Europe. However, it is unclear how representative such data are or not in relation to the actual species distribution. Spatial biases could skew results towards more urbanised areas and consequently produce incorrect or partial trend estimations at regional or national scales. Our objective was to compare and verify potential spatial biases of road based data using distribution datasets of different origins. As a case study, we used the common toad (Bufo bufo), a fast-declining species and the main amphibian targeted by conservation action on roads in Europe. We calculated ecological niche models with the built used Maxent models to compare road survey data obtained from the UK flagship, 35 year-long “Toads on Roads” project, containing almost 2 million amphibian records, in Great Britain with models using national-scale toad distribution records in Great Britain as well as with models using randomly generated points on roads. Road based distribution models that used data collected by volunteers on roads produced similar results to those obtained from overall species distribution, indicating the lack of selection bias and a high spatial coverage of volunteer-collected data on roads. Toads were present in most parts of the country but were generally absent from mountainous areas and, despite the high availability of potential recorders, showed nearly complete absence in large urban areas. To our knowledge, this is the first study that comparatively evaluates species distribution models created using datasets of different origin in order to verify the influence of potential spatial bias of data collected by volunteers on roads. We show that for countries with high road density road network coverage, such as Great Britain, road based data collected by volunteers represent a robust dataset in terms of coverage and a critical citizen science contribution to conservation.