ABSTRACTClimate change is anticipated to escalate the frequency and severity of global natural disasters over the next few decades, thereby significantly reshaping species distributions and populations. Species distribution models (SDMs), as essential tools in biogeography and biodiversity conservation, are pivotal for evaluating the impacts of climate change on species and forecasting their distribution ranges under different climate change scenarios over various periods. However, the absence of necessary background knowledge for model construction significantly affects the accuracy of these models, with the selection of different occurrence data sources being a key factor that constrains the accuracy of model predictions. In this study, using Quercus variabilis as a case study, which has diverse ecological, economic, and cultural values, we employed the Biomod2 ensemble modeling platform to comparatively analyze disparities between two different occurrence data sources (i.e., online specimen and scientific survey data) in the species distribution prediction accuracy, relative contribution of major environmental variables, and predicted distribution ranges. Furthermore, we examined potential discrepancies between these two data sources in the migration distance and direction of the species distribution centroid under different future climate scenarios over various periods. Our results indicated substantial differences in the simulation outcomes of SDMs derived from various occurrence data sources. SDMs based on scientific survey data had higher predictive accuracy (AUC = 0.9720, TSS = 0.8370), with the simulated species distribution ranges not only closely matching the actual distributions but also showing more pronounced changes in suitable habitat areas and centroid migration trends under future climate scenarios. In comparison, models based on online specimen data predicted a wider species distribution range, yet exhibited less pronounced trends in suitable area changes and centroid migration under future climate scenarios. Additionally, although the main environmental variables affecting the simulation outcomes from different occurrence data sources were essentially identical, they varied in their contributions and order of importance. Among them, human activity had a relatively stronger contribution for the online specimen data (17.76%), while topographic variables had a stronger impact for the scientific survey data, such as elevation (17.79%). Therefore, the choice of occurrence data sources have a significant impact on SDMs modeling results; this study provides insights and guidance for selecting optimal occurrence data sources to enhance the reliability of SDMs simulations.