Initial Screening of Fast-growing Tree Species Being Tolerant of Dry Tropical Peatlands in Central Kalimantan, Indonesia

An investigation of the recruit, survivorship and growth of naturally regenerating tree species on canal bank was conducted to select tree species which are suitable for preceding planting in drained and burnt peat swamp lands in Central Kalimantan, Indonesia. Top of the canal bank were open, with greater soil moisture deficit and higher soil temperatures than on the next intact forest floor. The abundant trees were asam-asam (Ploiarium alternifolium),garunggang (Cratoxylon arborescens) and tumih (Combretocarpus rotundatus). New regeneration of these trees on the canal bank was confirmed during this investigation and mortality was very low. These results indicated that P. alternifolium,C. arborescens and C. rotundatuswere tolerant of intensive radiation, soil drought and high soil temperatures during germination. The annual height increments were 189-232 cm y-1 (P. alternifolium),118-289 cm y-1 (C. arborescens)and 27-255 cm y-1 (C. rotundatus); thus, these three species could be classified as fast-growing with tolerance to open and dry conditions. Such characteristics were important to avoid competition with herbs, ferns,and/ or climbers. The results·suggest that P.alternifolium,C. arborescens and C. rotundatusare suitable for preceding planting for the rehabilitation of the disturbed peat swamp forests of Central Kalimantan

Juniper from Ethiopia contains a large-scale precipitation signal

Most semiarid regions are facing an increasing scarcity of woody vegetation due mainly to anthropogenic deforestation aggravated by climate changes. However, there is insufficient information to reconstruct past changes in climate and to evaluate the implications of future climate changes on the vegetation. Tree-ring analysis is a powerful tool for studying tree age, population dynamics, growth behavior, and climate-growth relationships among tropical tree species and for gaining information about the environmental forces driving growth change as well as for developing proxies for climate reconstruction. Wood anatomical and dendrochronological methods were used on Juniperus procera trees from two Ethiopian highland forests to check (i) whether tree-ring series of juniper are cross-datable and hence suitable for building tree-ring chronologies, and if so, (ii) which climate factors mainly drive wood formation in juniper from this region. Visible growth layers of the juniper wood were shown to be annual rings. Tree-ring sequences could be cross-dated between trees growing at the same site and between trees growing at sites 350 km apart. Evidence was found that annual growth of junipers is mainly controlled by one climatic factor, precipitation. This strong precipitation influence proves the potential of African juniper chronologies for accurate climate reconstruction and points out the relevance of building a network of juniper chronologies across East Africa

Tree size and herbivory determine below-canopy grass quality and species composition in savannahs

Large single-standing trees are rapidly declining in savannahs, ecosystems supporting a high diversity of large herbivorous mammals. Savannah trees are important as they support both a unique flora and fauna. The herbaceous layer in particular responds to the structural and functional properties of a tree. As shrubland expands stem thickening occurs and large trees are replaced by smaller trees. Here we examine whether small trees are as effective in providing advantages for grasses growing beneath their crowns as large trees are. The role of herbivory in this positive tree-grass interaction is also investigated. We assessed soil and grass nutrient content, structural properties, and herbaceous species composition beneath trees of three size classes and under two grazing regimes in a South African savannah. We found that grass leaf content (N and P) beneath the crowns of particularly large (ca. 3. 5 m) and very large trees (ca. 9 m) was as much as 40% greater than the same grass species not growing under a tree canopy, whereas nutrient contents of grasses did not differ beneath small trees

Growing a Tree in the Forest: Constructing Folksonomies by Integrating Structured Metadata

Many social Web sites allow users to annotate the content with descriptive metadata, such as tags, and more recently to organize content hierarchically. These types of structured metadata provide valuable evidence for learning how a community organizes knowledge. For instance, we can aggregate many personal hierarchies into a common taxonomy, also known as a folksonomy, that will aid users in visualizing and browsing social content, and also to help them in organizing their own content. However, learning from social metadata presents several challenges, since it is sparse, shallow, ambiguous, noisy, and inconsistent. We describe an approach to folksonomy learning based on relational clustering, which exploits structured metadata contained in personal hierarchies. Our approach clusters similar hierarchies using their structure and tag statistics, then incrementally weaves them into a deeper, bushier tree. We study folksonomy learning using social metadata extracted from the photo-sharing site Flickr, and demonstrate that the proposed approach addresses the challenges. Moreover, comparing to previous work, the approach produces larger, more accurate folksonomies, and in addition, scales better.Comment: 10 pages, To appear in the Proceedings of ACM SIGKDD Conference on Knowledge Discovery and Data Mining(KDD) 201

Alternating model trees

Model tree induction is a popular method for tackling regression problems requiring interpretable models. Model trees are decision trees with multiple linear regression models at the leaf nodes. In this paper, we propose a method for growing alternating model trees, a form of option tree for regression problems. The motivation is that alternating decision trees achieve high accuracy in classification problems because they represent an ensemble classifier as a single tree structure. As in alternating decision trees for classifi-cation, our alternating model trees for regression contain splitter and prediction nodes, but we use simple linear regression functions as opposed to constant predictors at the prediction nodes. Moreover, additive regression using forward stagewise modeling is applied to grow the tree rather than a boosting algorithm. The size of the tree is determined using cross-validation. Our empirical results show that alternating model trees achieve significantly lower squared error than standard model trees on several regression datasets