Phenotypic selection for improvement of Spondias tuberosa trees in on-farm biodiversity conservation

Abstract

Investigating the distribution modeling of Spondias tuberosa, as well as its phenotypic diversity, is critical for species conservation and tree improvement initiatives. This species holds great importance in bioeconomy, yet there is limited knowledge to address its production. Utilizing predictive distribution modeling, we can identify areas in Sergipe with a high likelihood of the natural occurrence of S. tuberosa, which is crucial for targeted conservation efforts. Comprehensive phenotypic characterization was conducted on a natural population, selecting individuals and examining traits ranging from tree architecture to fruit biometry, and including information on seed conservation under storage conditions. The findings revealed substantial variance in germination rates and seedling vigor related to the duration of endocarp storage, although no significant differences were observed in overall germination success across different storage times. However, prolonged storage resulted in an increase in abnormal seedlings and deteriorated seeds. The integration of phenotypic data and predictive modeling provides a robust framework for understanding ecological dynamics and supports sustainable management practices for S. tuberosa, aligning with bioeconomic goals. This research underscores the importance of maintaining phenotypic variability within populations, which is vital for the adaptive capacity of species to changing environmental conditions and for enhancing local bioeconomies.