Beta diversity quantifies variation in species composition across ecological communities and is fundamental for understanding biodiversity patterns across space and environmental gradients. Inference is complicated by incomplete sampling, differences among communities and imperfect detection. We develop a partially exchangeable Bayesian feature allocation model for species occurrence data that accommodates unobserved species and incorporates site-specific covariates to relate variation in species composition to environmental conditions. The model yields explicit conditional distributions for species richness and sharing, together with predictions of the species newly recorded at individual sites or newly shared between sites under additional sampling. We also introduce a novel probabilistic beta diversity index with posterior uncertainty quantification. An occupancy--detection representation preserves the tractability of the occurrence model and allows sensitivity analysis under specified detectability assumptions. This tractability makes the approach scalable, enabling inference in large biodiversity studies. Simulation studies assess predictive performance, and an application to global airborne fungal data illustrates how accounting for unobserved species and borrowing information across sites affect inference on compositional heterogeneity.

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