The RAINFOR database: monitoring forest biomass and dynamics

Problem: Data from over 100 permanent sample plots which have been studied for 10–20 years need a suitable system for storage which allows simple data manipulation and retrieval for analysis. Methods: A relational database linking tree records, taxonomic nomenclature and corresponding environmental data has been built in MS Access as part of the RAINFOR project. Conclusion: The database allows flexible and long‐term use of a large amount of data: more than 100 tree plots across Amazonia, incorporating over 80 000 records of individual trees and over 300 000 total records of tree diameter measurements from successive censuses. The database is designed to enable linkages to existing soil, floristic or plant‐trait databases. This database will be a useful tool for exploring the impact of environmental factors on forest structure and dynamics at local to continental scales, and long term changes in forest ecology. As an early example of its potential, we explore the impact of different methodological assumptions on estimates of tropical forest biomass and carbon storage.     

Scale sensitivity of synthetic long‐term vegetation time series derived through overlay of short‐term field records

Questions: Is change in cover of dominant species driving the velocity of succession or is it species turnover (1)? Is the length of the time‐step chosen in sampling affecting our recognition of the long‐term rate of change (2)1 Location: 74 permanent plots located in the Swiss National Park, SE Switzerland, ca. 1900 m a.s.l. Methods: We superimpose several time‐series from permanent plots to one single series solely based on compositional dissimilarity. As shown earlier (Wildi & Schütz 2000) this results in a synthetic series covering about 400 to 650 yr length. Continuous power transformation of cover‐percentage scores is used to test if the dominance or the presence‐absence of species is governing secondary succession from pasture to forest. The effect of time step length is tested by sub‐samples of the time series. Results: Altering the weight of presence‐absence versus dominance of species affects the emerging time frame, while altering time step length is uncritical. Where species turnover is fast, different performance scales yield similar results. When cover change in dominant species prevails, the solutions vary considerably. Ordinations reveal that the synthetic time series seek for shortest paths of the temporal pattern whereas in the real system longer lasting alternatives exist. Conclusions: Superimposing time series differs from the classical space‐for‐time substitution approach. It is a computation‐based method to investigate temporal patterns of hundreds of years fitting between direct monitoring (usually limited to decades) and the analysis of proxy‐data (for time spans of thousands of years and more).     

A New Rank Test Against Ordered Alternatives in K-Sample Problems

The distribution-free test against ordered alternatives proposed by Jonckheere (1954) is based on the Kendall's rank correlation coefficient τ. A new rank test is proposed and illustrated with a numerical example. The proposed test is based on Spearman's σ and has similar functional structure as the Kruskal-Wallis test. A useful by-product is a test for departure from a trend.     

Influences of temporal independence of data on modelling species distributions

Modelling species distributions has been widely used to understand present and future potential distributions of species, and can provide adaptation and mitigation information as references for conservation and management under climate change. However, various methods of data splitting to develop and validate functions of the models do not get enough attention, which may mislead the interpretation of predicted results. We used the Taiwanese endemic birds to test the influences of temporal independence of datasets on model performance and prediction. Training and testing data were considered to be independent if they were collected during different survey periods (1993–2004 and 2009–2010). The results indicated no significant differences of six model performance measures (AUC, kappa, TSS, accuracy, sensitivity, and specificity) among the combinations of training and testing datasets. Both species- and grid cell-based assessments differed significantly between predictions by the annual and pooled training data. We also found an average of 85.8% similarity for species presences and absences in different survey periods. The remaining dissimilarity was mostly caused by species observed in the late survey period but not in the early one. The method of data splitting, yielding training and testing data, is critical for resulting model species distributions. Even if similar model performance exists, different methods can lead to different species distributional maps. More attention needs to be given to this issue, especially when amplifying these models to project species distributions in a changing world.     

Gene assignment by means of somatic cell hybrids: a new method for the analysis of data

Summary A statistical approach to the interpretation of data from gene assignment with somatic cell hybrids is presented. The observed data are analysed under a variety of hypotheses. The fit to the hypotheses is compared by means of the likelihood obtained under a given hypothesis. Two of these hypotheses are related to fundamental questions: is a gene responsible for the enzyme observation and if so, is that gene located on a specific chromosome or could it change its position and be sometimes on chromosome j and, in another hybrid line, on chromosome k? The other hypotheses concern the assignment of the gene to just one of the chromosomes.To improve the traditional data analysis approach we considered additional information: the uncertainties and possible errors of laboratory methods in all our calculations and the length of the donor chromosomes in connection with one specific hypothesis.This method allows us to account for the reliability of the investigation methods and the nature of the hybrid lines involved. Data can be evaluated at different error probabilities within a realistic range in order to compare and discuss results.     

Processing of multi-dimensional NMR data with the new software PROSA

Summary The new program PROSA is an efficient implementation of the common data-processing steps for multi-dimensional NMR spectra. PROSA performs linear prediction, digital filtering, Fourier transformation, automatic phase correction, and baseline correction. High efficiency is achieved by avoiding disk storage of intermediate data and by the absence of any graphics display, which enables calculation in the batch mode and facilitates porting PROSA on a variety of different computer systems; including supercomputers. Furthermore, all time-consuming routines are completely vectorized. The elimination of a graphics display was made possible by the use of a new, reliable automatic phase-correction routine. CPU times for complete processing of a typical heteronuclear three-dimensional NMR data set of a protein vary between less than 1 min on a NEC SX3 supercomputer and 40 min on a Sun-4 computer system.     

Dissimilarity for partially ranked data and its application to cover-abundance data

Cover-abundance estimates are commonly employed in phytosociological investigations to record the performance of species. Because the coded values are on an ordinal scale of measure, various authors have suggested that some transformation is necessary before such values can be used for classification and ordination. However, it is not clear that transformation is a sufficient treatment, and it would seem preferable to use ordinal data directly. In this paper we examine such direct use of partial rankings and show that several dissimilarity measures can be defined for this case without invoking any transformations. They include dissimilarity measures associated with various rank correlation measures and with distances between strings; all the measure are variant forms of Hausdorf's interset distance. Certain other kinds of data, such as those employing dominant and subdominant species and the dry-weight-rank estimation of biomass, are also on an ordinal scale and could be analysed using similar techniques.To illustrate the approach, a string dissimilarity measure is used to analyse a set of data from Slovakian grasslands which appear to reflect a simple gradient. The original data were recorded with 10 classes of performance and are analysed using hierarchical and nondeterministic, overlapping, classifications.