<p>Species distribution models (SDMs) are vital tools in ecology and conservation. The integration of increasingly available citizen science data with planned survey data offers a significant opportunity to improve estimates of species distributions. Whilst integrated SDMs often combine presence-only and abundance data, the link between the two data types is still not well understood. This study proposes a Bayesian spatial fusion modelling framework to jointly analyse presence-only and abundance data for the African baobab in Benin. The aim was to understand and map the spatial variation in the species’ distribution. We briefly reviewed process-based models for count and point process data. We explored various data fusion strategies using Integrated Nested Laplace Approximations (INLA) and Stochastic Partial Differential Equations (SPDE) for fast Bayesian computation. The results revealed a heterogeneous baobab distribution across Benin, characterised by a spatial autocorrelation range of 34.4 km (95% Bayesian credible interval, BCI = 27.59 - 42.52). Key drivers of this distribution include environmental factors such as annual temperature, rainfall of the driest month, soil texture (silt/clay fractions), and slope. A spatial fusion model incorporating shared latent components and common covariates’ effects demonstrated the highest performance level, surpassing alternative fusion approaches. The model achieved the highest mean composite scores for the Area Under the ROC Curve (AUC) (0.85 ± 0.02), accuracy (0.77 ± 0.02), and True Skill Statistics (TSS) (0.66 ± 0.05). The shared component model effectively captures the dependence structure between the data types and enhances prediction precision by leveraging information across datasets. This research underscores the potential of spatial fusion modelling for integrating disparate data sources, providing a robust framework to advance SDM inference in data-limited contexts and address broader, complex spatial regression problems.</p>