A species distribution modelling analysis of Rafflesia pricei (Rafflesiaceae), a parasitic flowering plant endemic to Borneo

Rafflesia pricei (Rafflesiaceae), an endangered holoparasitic plant with large, elusive flowers, faces conservation challenges in Sabah, Malaysia, due to its cryptic life cycle and habitat vulnerability. This study used species distribution models (SDMs) to analyse environmental factors shaping its distribution, aiming to identify areas suitable for new populations and inform conservation efforts. Creating SDMs for a poorly known species as R. pricei can be a difficult task because occurrence data is limited, therefore the method ‘ensemble of small models’ was used, implementing three standard algorithms: generalised linear models, generalised boosted regression models, and support vector machines. Analysing elevation, bioclimatic variables, edaphic characteristics, and cloud cover data revealed a complex interplay of drivers. Initial assessments highlighted elevation, consistent with known sub-montane occurrences. However, a refined model identified the mean daily mean air temperature of the coldest quarter as the most significant predictor, suggesting a critical role for subtle temperature variations in flowering, host physiology, and pollination. Moisture-related bioclimatic factors also had considerable influence, while edaphic characteristics were less prominent. The study highlights the fragmented and undersized nature of habitats suitable for R. pricei in Sabah. This fragmentation, combined with sensitivity to environmental changes and threats from deforestation and agriculture, necessitates targeted conservation. Identifying key environmental drivers provides a scientific basis for selecting and managing potential reserves to safeguard R. pricei and its host. The study advocates for consolidating larger, interconnected habitat patches, particularly within the Heart of Borneo initiative, to maximise long-term survival and ecosystem preservation. By revealing R. pricei’s environmental dependencies, this research is anticipated to enhance the monitoring and conservation management of the species. Further research into eco-physiological responses to key factors, including El Niño impacts, is recommended.