Cascading predictions from common to uncommon species improves species distribution models for plants

Species distribution models (SDMs) traditionally rely on abiotic factors like climate and topography to predict plant species distributions. While effective at broad scales, these models often fail at finer spatial resolutions due to their inability to capture localized environmental conditions and biotic interactions, such as competition and facilitation, that strongly influence species presence. To address these limitations, we propose a cascading prediction framework that leverages species co-occurrence relationships to improve SDM predictions especially at small spatial scales. In this approach, we first predict the presence of common, dominant species based on environmental data and then use these predictions to inform the presence of less common species. We explore two variations: (i) the Predictive Cascade, which uses model-based predictions of frequent species to help predict the remaining species, and (ii) the Disjunctive Observational Cascade, which integrates presence-only data from citizen science platforms to the Cascade pipeline. By incorporating biotic interactions and competitive hierarchies into SDMs, our cascading approach constitutes a novel method to enhance prediction accuracy at fine spatial resolutions, particularly in species-rich environments where current state-of-the-art models struggle. • Cascading Predictions for Uncommon Species: Introduces a cascading prediction model that predicts the presence/absence of common plant species from environmental data and then combines those predictions with the same environmental data to predict the presence/absence of all species. This technique addresses the challenge of limited data for uncommon species. • Enhanced Prediction with Citizen Science Data: Incorporating presence-only data from sources like iNaturalist or Pl@ntNet significantly improves cascading predictions, especially for the common species used by the cascading model, leading to better overall predictions in species distribution models.