Intra- and interspecific variations in vacuolar flavonoids among Ficus species from the Budongo Forest, Uganda

Leaves of 14 species of Ficus growing in the Budongo Forest, Uganda, were analysed for vacuolar flavonoids. Three to six accessions were studied for each species to see whether there was intraspecific chemical variation. Thirty-nine phenolic compounds were identified or characterised, including 14 flavonol O-glycosides, six flavone O-glycosides and 15 flavone C-glycosides. In some species the flavonoid glycosides were acylated. Ficus thonningii contained in addition four stilbenes including glycosides. Most of the species could be distinguished from each other on the basis of their flavonoid profiles, apart from Ficus sansibarica and Ficus saussureana, which showed a very strong intraspecific variation. However, on the whole flavonoid profiles were sufficiently distinct to help in future identifications.

The Bayesian revolution in genetics

Bayesian statistics allow scientists to easily incorporate prior knowledge into their data analysis. Nonetheless, the sheer amount of computational power that is required for Bayesian statistical analyses has previously limited their use in genetics. These computational constraints have now largely been overcome and the underlying advantages of Bayesian approaches are putting them at the forefront of genetic data analysis in an increasing number of areas.

A data forest: multi-dimensional visualization

As Terabyte datasets become the norm, the focus has shifted away from our ability to produce and store ever larger amounts of data, onto its utilization. It is becoming increasingly difficult to gain meaningful insights into the data produced. Also many forms of the data we are currently producing cannot easily fit into traditional visualization methods. This paper presents a new and novel visualization technique based on the concept of a Data Forest. Our Data Forest has been designed to be used with vir tual reality (VR) as its presentation method. VR is a natural medium for investigating large datasets. Our approach can easily be adapted to be used in a variety of different ways, from a stand alone single user environment to large multi-user collaborative environments. A test application is presented using multi-dimensional data to demonstrate the concepts involved.

Data forest: a collaborative version

As we increase our ability to produce and store ever larger amounts of data, it is becoming increasingly difficult to understand what the data is trying to tell us. Not all the data we are currently producing can easily fit into traditional visualization methods. This paper presents a new and novel visualization technique based on the concept of a Data Forest. Our Data Forest has been developed to be utilised by virtual reality (VR) systems. VR is a natural information medium. This approach can easily be adapted to be used in collaborative environments. A test application has been developed to demonstrate the concepts involved and a collaborative version tested.

Biomass partitioning and growth efficiency in four naturally regenerated forest tree species

Current forest growth models and yield tables are almost exclusively based on data from mature trees, reducing their applicability to young and developing stands. To address this gap, young European beech, sessile oak, Scots pine and Norway spruce trees approximately 0 to 10 years old were destructively sampled in a range of naturally regenerated forest stands in Central Europe. Diameter at base and height were first measured in situ for up to 175 individuals per species. Subsequently, the trees were excavated and dry biomass of foliage, branches, stems and roots was measured. Allometric relations were then used to calculate biomass allocation coefficients (BAC) and growth efficiency (GE) patterns in young trees. We found large differences in BAC and GE between broadleaves and conifers, but also between species within these categories. Both BAC and GE are strongly age-specific in young trees, their rapidly changing values reflecting different growth strategies in the earliest stages of growth. We show that linear relationships describing biomass allocation in older trees are not applicable in young trees. To accurately predict forest biomass and carbon stocks, forest growth models need to include species and age specific parameters of biomass allocation patterns.

Protecting degraded rainforests: enhancement of forest carbon stocks under REDD+

The likely Reducing Emissions from Deforestation and Degradation (REDD+) mechanism includes strategies for the enhancement of forest carbon stocks. Recent concerns have been expressed that such enhancement, or restoration, of forest carbon could be counterproductive to biodiversity conservation, because forests are managed as “carbon farms” with the application of intensive silvicultural management that could homogenize diverse degraded rainforests. Restoration increases regeneration rates in degraded forest compared to naturally regenerating forest, and thus could yield significant financial returns for carbon sequestered. Here, we argue that such forest restoration projects are, in fact, likely to provide a number of benefits to biodiversity conservation including the retention of biodiversity, the prevention of forest conversion to agriculture, and employment opportunities for poor local communities. As with other forms of forest-based carbon offsets, there are possible moral hazard and leakage problems with restoration. However, due to the multiple benefits, we urge that enhancement of forest carbon stocks be detailed as a major component in the future negotiations of REDD+.