Effects of living crown reduction on needle element status of Scots pine

A wide range of biotic and abiotic factors, operating over different time perspectives and intensities, cause defoliation and a rapid decrease in the crown size of trees. Scleroderris canker disease [Gremmeniella abietina (Lagerb.) Morelet] has caused widespread crown reduction and tree mortality in Scots pine (Pinus sylvestris L) in forests in Scandinavia during the last three decades. In the 1980's, attempts were made to show, on the basis of the higher foliar N and S concentrations of affected pines in the diseased area, that sulphur and nitrogen deposition predispose trees to G. abietina. Unfortunately, in many studies on defoliated trees, exceptionally high or low needle mineral nutrient concentrations are still often interpreted as one of the causes of tree injury and not, conversely, as the result. In this thesis, three different field experiments, with foliar analysis as the main study method, were conducted in order to asses the possible long-term effects of living crown reduction on the needle nutrient concentrations of Scots pine trees in southern Finland. The crown ratio and length of the living crown were used to estimate the amount of defoliation in the reduced canopies. The material for the partial studies was collected and a total of 968 foliar samples were analysed individually (15-17 elements/sample) on a total of 488 sample trees (140 diseased, 116 pruned and 232 control trees) during the years 1987-1996 in 13 Scots pine stands. All the three experiments of this thesis provided significant evidence that severe, disease-induced defoliation or artificial pruning of the living branches can induce long-lasting nutritional changes in the foliage of the recovering trees under the typical growing conditions for Scots pine. The foliar concentrations of all the 17 mineral nutrients/elements analysed were affected, to a varying degree, by artificial pruning during the following three years. Although Scots pine, as an evergreen conifer, is considered to have low induced chemical responses to defoliation, this study proved experimentally under natural forest conditions that severe artificial pruning or disease-induced defoliation of Scots pine trees may induce biologically significant changes in the concentrations of most of the important macro- and micronutrients, as well as of carbon, in refoliated needles. Concerning the studies in this thesis, I find the results significant in providing new information about the long-term effects of rapid living crown reduction on the foliar nutrient and element status of Scots pine trees. Key words: Foliar analysis, defoliation, needle loss, pruning, nutrients, Pinus sylvestris, Gremmeniella abietina

Efficient search for statistically significant dependency rules in binary data

Analyzing statistical dependencies is a fundamental problem in all empirical science. Dependencies help us understand causes and effects, create new scientific theories, and invent cures to problems. Nowadays, large amounts of data is available, but efficient computational tools for analyzing the data are missing. In this research, we develop efficient algorithms for a commonly occurring search problem - searching for the statistically most significant dependency rules in binary data. We consider dependency rules of the form X->A or X->not A, where X is a set of positive-valued attributes and A is a single attribute. Such rules describe which factors either increase or decrease the probability of the consequent A. A classical example are genetic and environmental factors, which can either cause or prevent a disease. The emphasis in this research is that the discovered dependencies should be genuine - i.e. they should also hold in future data. This is an important distinction from the traditional association rules, which - in spite of their name and a similar appearance to dependency rules - do not necessarily represent statistical dependencies at all or represent only spurious connections, which occur by chance. Therefore, the principal objective is to search for the rules with statistical significance measures. Another important objective is to search for only non-redundant rules, which express the real causes of dependence, without any occasional extra factors. The extra factors do not add any new information on the dependence, but can only blur it and make it less accurate in future data. The problem is computationally very demanding, because the number of all possible rules increases exponentially with the number of attributes. In addition, neither the statistical dependency nor the statistical significance are monotonic properties, which means that the traditional pruning techniques do not work. As a solution, we first derive the mathematical basis for pruning the search space with any well-behaving statistical significance measures. The mathematical theory is complemented by a new algorithmic invention, which enables an efficient search without any heuristic restrictions. The resulting algorithm can be used to search for both positive and negative dependencies with any commonly used statistical measures, like Fisher's exact test, the chi-squared measure, mutual information, and z scores. According to our experiments, the algorithm is well-scalable, especially with Fisher's exact test. It can easily handle even the densest data sets with 10000-20000 attributes. Still, the results are globally optimal, which is a remarkable improvement over the existing solutions. In practice, this means that the user does not have to worry whether the dependencies hold in future data or if the data still contains better, but undiscovered dependencies.