Although generally considered environmentally friendly, wind power has been associated with extensive mortality of birds and bats. In this perspective, there is a need for reliable estimates of fatalities at wind farms, where the heterogeneity of the basic information, used among environmental assessment studies, is unlikely to support an accurate universal estimation method. We tested the applicability of the Stochastic Dynamic Methodology (StDM) to estimate bat fatalities, based on multifactorial cause-effect relationships (by integrating multi-model inference statistical analysis and dynamic modelling) between mortality estimates, detected fatalities and the selected key-components of the reality, such as the real number of bat mortalities simulated, the rate of carcasses removal, the searcher efficiency, the monitoring periodicity and the number of turbines for different realistic scenarios associated with particular wind farm conditions. Although some existing mortality estimators are considered accurate, the choice of a given universal formula for all mortality assessments, based on deterministic parameters and assumptions, may originate unsuspected errors. Therefore, we propose a flexible dynamic modelling framework, the StDM estimator, where the obtained algorithms are adaptable to the universe of application intended. The StDM estimator takes into account random, non-constant and scenario dependent parameters, providing bias-corrected estimates. The StDM estimator was applied for the European wind farm context and validated in the most cases tested, through the confrontation with independent data. Overall, this approach is considered a valuable tool to improve the quality of mortality estimates at onshore wind facilities, within the local, environmental and methodological gradients (including the cases where no mortality is detected), namely in the scope of environmental impact assessments and general ecological monitoring programmes.