||Artificial Light At Night (ALAN) is one of the most important anthropogenic environmental components that affects biodiversity worldwide. Despite extensive knowledge on ALAN, being a measure of human activity that directly impacts numerous aspects of animal behaviour, such as orientation and distribution, little is known about its effects on density distribution on a large spatial scale. That is why we decided to explore by means of the Species Distribution Modelling approach (SDM) how ALAN as one of 33 predictors determines farmland and forest bird species densities. In order to safeguard study results from any inconsistency caused by the chosen method, we used two approaches, i.e. the Generalised Additive Model (GAM) and the Random Forest (RF). Within each approach, we developed two models for two bird species, the Black woodpecker and the European stonechat: the first with ALAN, and the second without ALAN as an additional predictor. Having used out-of-bag procedures in the RF approach, information-theoretic criteria for the GAM, and evaluation models based on an independent dataset, we demonstrated that models with ALAN had higher predictive density power than models without it. The Black woodpecker definitely and linearly avoids anthropogenic activity, defined by the level of artificial light, while the European stonechat tolerates human activity to some degree, especially in farmland habitats. What is more, a heuristic analysis of predictive maps based on models without ALAN shows that both species reach high densities in regions where they are deemed rare. Hence, the study proves that urbanisation processes, which can be reflected by ALAN, are among key predictors necessary for developing Species Density Distribution Models for both farmland and forest bird species.