Regeneration ecology and woody species diversity in the remnant natural forest at Wondo Genet, Ethiopia – Potential for landscape restoration

Regeneration ecology, diversity of native woody species and its potential for landscape restoration was studied in the remnant natural forest at the College of Forestry and Natural Resources at Wondo Genet, Ethiopia. The type of forest is Afromontane rainforest , with many valuable tree species like Aningeria adolfi-friederici, and it is an important provider of ecological, social and economical services for the population that lives in this area. The study contains two parts, natural regeneration studies (at the natural forest) and interviews with farmers in the nearby village of the remnant patch. The objective of the first part was to investigate the floristic composition, densitiy and regeneration profiles of native woody species in the forest, paying special attention to woody species that are considered the most relevant (socio-economic). The second part provided information on woody species preferred by the farmers and on multiple uses of the adjacent natural forest, it also provided information and analysed perceptions on forest degradation. Systematic plot sampling was used in the forest inventory. Twenty square plots of 20 x 20 m were assessed, with 38 identified woody species (the total number of species was 45), representing 26 families. Of these species 61% were trees, 13% shrubs, 11% lianas and 16% species that could have both life forms. An analysis of natural regeneration of five important tree species in the natural forest showed that Aningeria adolfi-friederici had the best regeneration results. An analysis of population structure (as determined by height classes) of two commercially important woody species in the forest, Aningeria adolfi-friederici and Podocarpus falcatus, showed a marked difference: Aningeria had a typical “reversed J” frequency distribution, while Podocarpus showed very low values in all height classes. Multi dimensional scaling (MDS) was used to map the sample plots according to their similarity in species composition, using the Sørensen quantitative index, coupled with indicator species analysis .Three groups were identified with respective indicator species: Group 1 – Adhatoda schimperiana, Group 2 – Olea hochstetteri , Group 3 – Acacia senegal and Aningeria adolfi-friederici. Thirty questionnaire interviews were conducted with farmers in the village of Gotu Onoma that use the nearby remant forest patch. Their tree preferences were exotic species such as Eucalyptus globulus for construction and fuelwood and Grevillea robusta for shade and fertility. Considering forest land degradation farmers were aware of the problem and suggested that the governmental institutions address the problem by planting more Eucalyptus globulus. The natural forest seemed to have moderate levels of disturbance and it was still floristically diverse. However, the low rate of natural regeneration of Podocarpus falcatus suggested that this species is threatened and must be a priority in conservation actions. Plantations and agroforestry seem to be possible solutions for rehabilitation of the surrounding degraded lands, thereby decreasing the existent pressure in the remnant natural forest.

Methods and applications for improving parameter prediction models for stand structures in Finland

This thesis report attempts to improve the models for predicting forest stand structure for practical use, e.g. forest management planning (FMP) purposes in Finland. Comparisons were made between Weibull and Johnson s SB distribution and alternative regression estimation methods. Data used for preliminary studies was local but the final models were based on representative data. Models were validated mainly in terms of bias and RMSE in the main stand characteristics (e.g. volume) using independent data. The bivariate SBB distribution model was used to mimic realistic variations in tree dimensions by including within-diameter-class height variation. Using the traditional method, diameter distribution with the expected height resulted in reduced height variation, whereas the alternative bivariate method utilized the error-term of the height model. The lack of models for FMP was covered to some extent by the models for peatland and juvenile stands. The validation of these models showed that the more sophisticated regression estimation methods provided slightly improved accuracy. A flexible prediction and application for stand structure consisted of seemingly unrelated regression models for eight stand characteristics, the parameters of three optional distributions and Näslund s height curve. The cross-model covariance structure was used for linear prediction application, in which the expected values of the models were calibrated with the known stand characteristics. This provided a framework to validate the optional distributions and the optional set of stand characteristics. Height distribution is recommended for the earliest state of stands because of its continuous feature. From the mean height of about 4 m, Weibull dbh-frequency distribution is recommended in young stands if the input variables consist of arithmetic stand characteristics. In advanced stands, basal area-dbh distribution models are recommended. Näslund s height curve proved useful. Some efficient transformations of stand characteristics are introduced, e.g. the shape index, which combined the basal area, the stem number and the median diameter. Shape index enabled SB model for peatland stands to detect large variation in stand densities. This model also demonstrated reasonable behaviour for stands in mineral soils.

Elements of Volatility at High Frequency

Volatility is central in options pricing and risk management. It reflects the uncertainty of investors and the inherent instability of the economy. Time series methods are among the most widely applied scientific methods to analyze and predict volatility. Very frequently sampled data contain much valuable information about the different elements of volatility and may ultimately reveal the reasons for time varying volatility. The use of such ultra-high-frequency data is common to all three essays of the dissertation. The dissertation belongs to the field of financial econometrics. The first essay uses wavelet methods to study the time-varying behavior of scaling laws and long-memory in the five-minute volatility series of Nokia on the Helsinki Stock Exchange around the burst of the IT-bubble. The essay is motivated by earlier findings which suggest that different scaling laws may apply to intraday time-scales and to larger time-scales, implying that the so-called annualized volatility depends on the data sampling frequency. The empirical results confirm the appearance of time varying long-memory and different scaling laws that, for a significant part, can be attributed to investor irrationality and to an intraday volatility periodicity called the New York effect. The findings have potentially important consequences for options pricing and risk management that commonly assume constant memory and scaling. The second essay investigates modelling the duration between trades in stock markets. Durations convoy information about investor intentions and provide an alternative view at volatility. Generalizations of standard autoregressive conditional duration (ACD) models are developed to meet needs observed in previous applications of the standard models. According to the empirical results based on data of actively traded stocks on the New York Stock Exchange and the Helsinki Stock Exchange the proposed generalization clearly outperforms the standard models and also performs well in comparison to another recently proposed alternative to the standard models. The distribution used to derive the generalization may also prove valuable in other areas of risk management. The third essay studies empirically the effect of decimalization on volatility and market microstructure noise. Decimalization refers to the change from fractional pricing to decimal pricing and it was carried out on the New York Stock Exchange in January, 2001. The methods used here are more accurate than in the earlier studies and put more weight on market microstructure. The main result is that decimalization decreased observed volatility by reducing noise variance especially for the highly active stocks. The results help risk management and market mechanism designing.