Assessment of inbreeding depression on progeny of Afrocarpus gracilior

Human activities such as agriculture, urbanization, and logging have caused widespread destruction of forests, leading to forest fragmentation. Fragmentation has been shown to reduce genetic diversity and increase inbreeding in forest populations, potentially leading to inbreeding depression manifested through decreased reproductive success and progeny vigor. The severity of these impacts, however, varies among species and is largely influenced by their mating systems. This study examines the effects of forest fragmentation on Afrocarpus gracilior, a dioecious, wind-pollinated conifer, by assessing genetic diversity, reproductive success, and early progeny fitness. Our analysis revealed alarmingly low genetic diversity and high genetic drift, especially in small and isolated populations. Consistent with these findings, reduced progeny fitness was observed, with small populations showing 53% lower germination rates, 33% reduced acclimatization, 30% slower diameter growth, 41% reduced height growth, and an 80% increase in leaf scorch. Correlation analysis further confirmed a strong relationship between the genetic diversity and progeny fitness traits. These findings suggest that inbreeding depression severely affects the fitness of progeny from small and isolated populations of A. gracilior, posing a serious threat to their long-term survival. The implications for conservation and restoration efforts are immense, underscoring the need to prioritize genetically diverse populations for conservation and strategically procure seeds to support the survival of this species.

Methods
The data was collected through field inventory, seed sample collection, seed processing, physical examination, in vitro germination, and early progeny vigor assessment in a lath house. We measured the number of intact seeds, seed weight, in vitro germination, acclimatization or survival in the lath house, seedling diameter and height growth, and the extent of leaf scorch. We entered the data into an MS Excel spreadsheet, saved it in CSV format, and prepared it for analysis. Additionally, we used data on genetic diversity parameters obtained from a related work by the authors. For analysis, we used the R statistical software to conduct analysis of variance (ANOVA), correlation analysis, and plotting for visualization. The scripts used to generate the results in R are also included in this dataset.