Leveraging obligate mutualism to resolve the rare species modeling paradox

Abstract In the past two decades, species distribution models have emerged as vital tools for devising biodiversity conservation planning, yet modeling rare, data‐deficient species remains a significant challenge. To address this “rare species modelling paradox,” we propose a novel approach: when two obligate mutualistic partners share a fundamental ecological niche, the distribution data of a well‐documented mutualistic partner can serve as a reliable proxy to model the distribution of its rare counterpart. We tested this hypothesis using the obligatory mutualism between a fly ( Philoliche longirostris ) and its forage plant ( Roscoea purpurea ). We first tested the niche overlap between the fly and the plant using Schoener's D and Hellinger's I as proxy measures. Subsequently, using the occurrence data of R. purpurea , we performed ensemble SDM and built a proxy distribution model for the fly and tested the proxy model's accuracy through ground truthing of fly occurrence at 40 sites predicted by the proxy model. Finally, we developed the final distribution models of P. longirostris by incorporating the points generated through ground‐truthing together with the currently known distribution points, and performed a Pearson correlation analysis between the probabilities of occurrence of the two models. Our analysis confirmed a significant niche overlap between the two species (Schoener's D = 0.552; Hellinger's I = 0.737). The ground truthing confirmed the presence of P. longirostris at 37 locations (92.5% accuracy), demonstrating high predictive performance of the proxy model. A final model showed a strong correlation ( r = 0.836) and high spatial overlap with the initial proxy model. This study suggests that mutualistic proxies can effectively circumvent the data limitation problem for rare species. Our framework offers a biologically grounded method for enhancing conservation planning of rare/data‐deficient species involved in tight ecological interactions, with broad applications for other ecological interactions.