Background Stream ecosystems comprise complex interactions among biological communities and their physicochemical surroundings, contributing to their overall ecological health. Despite this, many monitoring programs ignore changes in the bacterial communities that are the base of food webs in streams, often focusing on stream physicochemical assessments or macroinvertebrate community diversity instead. We used 16S rRNA gene sequencing to assess bacterial community compositions within 600 New Zealand stream biofilm samples from 204 sites within a 6-week period (February–March 2010). Sites were either dominated by indigenous forests, exotic plantation forests, horticulture, or pastoral grasslands in the upstream catchment. We sought to predict each site’s catchment land use and environmental conditions based on the composition of the stream bacterial communities.

Results Random forest modelling allowed us to use bacterial community composition to predict upstream catchment land use with 65% accuracy; urban sites were correctly assigned 90% of the time. Despite the variation inherent when sampling across a ~ 1000-km distance, bacterial community data could correctly differentiate undisturbed sites, grouped by their dominant environmental properties, with 75% accuracy. The positive correlations between actual values and those predicted by the models built using the stream biofilm bacterial data ranged from weak (average log N concentration in the stream water, R2 = 0.02) to strong (annual mean air temperature, R2 = 0.69).

Conclusions Freshwater bacterial community data provide useful insights into land use impacts on stream ecosystems; they may be used as an additional measure to screen stream catchment attributes.