RESULTS: In addition to the Kinabatangan River, a known barrier for dispersal in tree shrews, the heterogeneous landscape along the riverbanks affected the genetic structure in this species. Specifically, while in larger connected forest fragments along the northern riverbank genetic connectivity was relatively undisturbed, patterns of genetic differentiation and the distribution of mitochondrial haplotypes in a local scale indicated reduced migration on the strongly fragmented southern riverside. Especially, oil palm plantations seem to negatively affect dispersal in T. longipes. Clear sex-biased dispersal was not detected based on relatedness, assignment tests, and haplotype diversity.
CONCLUSION: This study revealed the importance of landscape connectivity to maintain migration and gene flow between fragmented populations, and to ensure the long-term persistence of species in anthropogenically disturbed landscapes.
RESULTS: The kinship coefficient between individuals in this family ranged from 0.35 to 0.62. S/F and O/DM had the highest genomic heritability, whereas F/B and O/P had the lowest. The accuracies using 135 SSRs were low, with accuracies of the traits around 0.20. The average accuracy of machine learning methods was 0.24, as compared to 0.20 achieved by other methods. The trait with the highest mean accuracy was F/B (0.28), while the lowest were both M/F and O/P (0.18). By using whole genomic SNPs, the accuracies for all traits, especially for O/DM (0.43), S/F (0.39) and M/F (0.30) were improved. The average accuracy of machine learning methods was 0.32, compared to 0.31 achieved by other methods.
CONCLUSION: Due to high genomic resolution, the use of whole-genome SNPs improved the efficiency of GS dramatically for oil palm and is recommended for dura breeding programs. Machine learning slightly outperformed other methods, but required parameters optimization for GS implementation.