Mangifera pajang (family: Anacardiaceae; local name: bambangan) and Artocarpus odoratissimus (familiy: Moraceae; local name: tarap) are popular edible fruits in Sabah, Malaysia. The flesh, kernel and peel from M. pajang; seed and flesh from A. odoratissimus were analysed for total antioxidant activity, total polyphenol, total flavonoid and total anthocyanins contents. M. pajang kernel extract displayed the highest free radical scavenging and ferric reducing activities. Total phenolic content of the samples were in the range of 5.96-103.3 mg gallic acid equivalent/g. M. pajang kernel and M. pajang flesh contained the highest and lowest total flavonoid content with the values of 10.98 and 0.07 mg rutin equivalent/g, respectively. The antioxidant activities of extracts were significantly correlated with the total phenolic and flavonoid content (but not the anthocyanins content). The phytochemicals and antioxidant properties of M. pajang and A. odoratissimus, especially their by-products (kernel/seed), indicate that they may impart health benefits when consumed and should be regarded as a valuable source of antioxidant-rich nutraceuticals.

The ant communities of the leaf litter were studied along an elevational gradient on Mount Kinabalu in primary rain forest systems ranging from dipterocarp hill forest to dwarf forest of the highest altitudes (560, 800, 1130, 1360, 1530, 1740, 1930, 2025, 2300, 2600 m a.s.l.). The litter ant fauna along the gradient included 283 species of 55 genera. The number of ant species in the leaf litter decreased exponentially without evidence of a peak in species richness at mid-elevations. This result is in contrast to many findings on altitudinal gradients in ants and other animal groups. Most ant species have a very limited altitudinal range leading to high turnover values when comparing communities of different altitudes. Of the ant species, 74% were even restricted to one site. As evident from this study, altitudinal ranges of species are very narrow. Elevational gradients are therefore extremely species-rich and might serve as a prime example of hot spots of biodiversity. This fact is of great concern when implementing conservation strategies.

We investigated the genetic structure within and among Bornean orang-utans (Pongo pygmaeus) in forest fragments of the Lower Kinabatangan flood plain in Sabah, Malaysia. DNA was extracted from hair and faecal samples for 200 wild individuals collected during boat surveys on the Kinabatangan River. Fourteen microsatellite loci were used to characterize patterns of genetic diversity. We found that genetic diversity was high in the set of samples (mean H(E) = 0.74) and that genetic differentiation was significant between the samples (average F(ST) = 0.04, P < 0.001) with F(ST) values ranging from low (0.01) to moderately large (0.12) values. Pairwise F(ST) values were significantly higher across the Kinabatangan River than between samples from the same river side, thereby confirming the role of the river as a natural barrier to gene flow. The correlation between genetic and geographical distance was tested by means of a series of Mantel tests based on different measures of geographical distance. We used a Bayesian method to estimate immigration rates. The results indicate that migration is unlikely across the river but cannot be completely ruled out because of the limited F(ST) values. Assignment tests confirm the overall picture that gene flow is limited across the river. We found that migration between samples from the same side of the river had a high probability indicating that orang-utans used to move relatively freely between neighbouring areas. This strongly suggests that there is a need to maintain migration between isolated forest fragments. This could be done by restoring forest corridors alongside the river banks and between patches.

Key message The optimization of training populations and the use of diagnostic markers as fixed effects increase the predictive ability of genomic prediction models in a cooperative wheat breeding panel. Abstract Plant breeding programs often have access to a large amount of historical data that is highly unbalanced, particularly across years. This study examined approaches to utilize these data sets as training populations to integrate genomic selection into existing pipelines. We used cross-validation to evaluate predictive ability in an unbalanced data set of 467 winter wheat ( Triticum aestivum L.) genotypes evaluated in the Gulf Atlantic Wheat Nursery from 2008 to 2016. We evaluated the impact of different training population sizes and training population selection methods (Random, Clustering, PEVmean and PEVmean1) on predictive ability. We also evaluated inclusion of markers associated with major genes as fixed effects in prediction models for heading date, plant height, and resistance to powdery mildew (caused by Blumeria graminis f. sp. tritici) . Increases in predictive ability as the size of the training population increased were more evident for Random and Clustering training population selection methods than for PEVmean and PEVmean1. The selection methods based on minimization of the prediction error variance (PEV) outperformed the Random and Clustering methods across all the population sizes. Major genes added as fixed effects always improved model predictive ability, with the greatest gains coming from combinations of multiple genes. Maximum predictabilities among all prediction methods were 0.64 for grain yield, 0.56 for test weight, 0.71 for heading date, 0.73 for plant height, and 0.60 for powdery mildew resistance. Our results demonstrate the utility of combining unbalanced phenotypic records with genome-wide SNP marker data for predicting the performance of untested genotypes. Electronic supplementary material The online version of this article (10.1007/s00122-019-03276-6) contains supplementary material, which is available to authorized users.

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