Population Survey of Bornean White-Bearded Gibbon, Hylobates albibarbis,in Two Selective Logging Concessions in Central Kalimantan and West Kalimantan.

We surveyed Bornean white-bearded gibbon, Hylobates albibarbis, population densities in lowland and hill dipterocarp forests within the Sari Bumi Kusama and Suka Jaya Makmur logging concessions. These surveys were conducted from 20 to 30 March 2018 in Sari Bumi Kusuma areas and 14 to 24 April 2018 in Suka Jaya Makmur. We used a fixed-point count method to detect and plot morning song bouts of gibbon groups at systematically placed listening posts. At each location in Sari Bumi Kusuma and Suka Jaya Makmur, 18 listening posts were established, and 2 observers noted and plotted gibbon morning great calls. Gibbon group density was higher at Suka Jaya Makmur than at Sari Bumi Kusuma; we estimated that there were 2.29 groups per square kilometre in the former area and 1.86 groups/km2 in the latter. Since Bornean white-bearded gibbons are strictly protected by the Government of Indonesia and listed as an Endangered species by the International Union for Conservation of Nature (IUCN), we need better management practices to protect the populations of this species within production forests (outside protected areas).

Somatic mutation rates scale with time not growth rate in long-lived tropical trees.

The rates of appearance of new mutations play a central role in evolution. However, mutational processes in natural environments and their relationship with growth rates are largely unknown, particular in tropical ecosystems with high biodiversity. Here, we examined the somatic mutation landscapes of two tropical trees, Shorea laevis (slow-growing) and S. leprosula (fast-growing), in central Borneo, Indonesia. Using newly constructed genomes, we identified a greater number of somatic mutations in tropical trees than in temperate trees. In both species, we observed a linear increase in the number of somatic mutations with physical distance between branches. However, we found that the rate of somatic mutation accumulation per meter of growth was 3.7-fold higher in S. laevis than in S. leprosula. This difference in the somatic mutation rate was scaled with the slower growth rate of S. laevis compared to S. leprosula, resulting in a constant somatic mutation rate per year between the two species. We also found that somatic mutations are neutral within an individual, but those mutations transmitted to the next generation are subject to purifying selection. These findings suggest that somatic mutations accumulate with absolute time and older trees have a greater contribution towards generating genetic variation.

Comparing modeling methods of genomic prediction for growth traits of a tropical timber species, Shorea macrophylla.

Introduction: Shorea macrophylla is a commercially important tropical tree species grown for timber and oil. It is amenable to plantation forestry due to its fast initial growth. Genomic selection (GS) has been used in tree breeding studies to shorten long breeding cycles but has not previously been applied to S. macrophylla. Methods: To build genomic prediction models for GS, leaves and growth trait data were collected from a half-sib progeny population of S. macrophylla in Sari Bumi Kusuma forest concession, central Kalimantan, Indonesia. 18037 SNP markers were identified in two ddRAD-seq libraries. Genomic prediction models based on these SNPs were then generated for diameter at breast height and total height in the 7th year from planting (D7 and H7). Results and discussion: These traits were chosen because of their relatively high narrow-sense genomic heritability and because seven years was considered long enough to assess initial growth. Genomic prediction models were built using 6 methods and their derivatives with the full set of identified SNPs and subsets of 48, 96, and 192 SNPs selected based on the results of a genome-wide association study (GWAS). The GBLUP and RKHS methods gave the highest predictive ability for D7 and H7 with the sets of selected SNPs and showed that D7 has an additive genetic architecture while H7 has an epistatic genetic architecture. LightGBM and CNN1D also achieved high predictive abilities for D7 with 48 and 96 selected SNPs, and for H7 with 96 and 192 selected SNPs, showing that gradient boosting decision trees and deep learning can be useful in genomic prediction. Predictive abilities were higher in H7 when smaller number of SNP subsets selected by GWAS p-value was used, However, D7 showed the contrary tendency, which might have originated from the difference in genetic architecture between primary and secondary growth of the species. This study suggests that GS with GWAS-based SNP selection can be used in breeding for non-cultivated tree species to improve initial growth and reduce genotyping costs for next-generation seedlings.