Aim Closing knowledge gaps in species taxonomy and distribution (i.e. Linnean and Wallacean shortfalls, respectively) require spatially distributed high‐quality data. However, studies on terrestrial taxa have shown that occurrence data are biased owing to higher sampling efforts towards areas with greater accessibility, research infrastructure and attractiveness. Here, we tested whether these biasing factors are also important drivers of freshwater fish species research by assessing fishes’ sampling efforts in Brazil, a continental‐sized and megadiverse country. We hypothesized that the influence of biasing factors is scale‐dependent, with differential effects across regions, that is that they present non‐stationarity. Location Brazil. Methods We modelled sampling events of fish (gathered from online databases) as a function of accessibility (population density, distance from access routes and density of access routes), availability of research infrastructure (distance from research centres) and attractiveness of protected areas (distance from protected areas), by using a traditional stationary model (ordinary least square models—OLS) and a model taking into account non‐stationarity (geographically weighted regression—GWR). Results We recorded 43,538 sampling events distributed across Brazil. Freshwater fish samplings were spatially biased with a tendency for major efforts to be concentrated in sites with a higher density of access routes, population and nearness to research centres and protected areas. However, GWR models performed better than the OLS model, revealing the non‐stationarity of the effects of predictors in explaining the number of sampling events. Main conclusions Overcoming Linnean and Wallacean shortfalls for freshwater fish species in megadiverse regions such as Brazil can be aided by financially supporting the sampling efforts in less accessible and less populated sites farther from research centres and well‐studied protected areas. However, the source of biasing has strong scale dependency, so that reducing a specific source of bias is not expected to have the same effectiveness in all areas.