We offer a new framework for cellular automata modeling to describe and predict vegetation dynamics. The model can simulate community composition and spatial patterns by following a set of probabilistic rules generated from empirical data on plant neighborhood dynamics. Based on published data (Lippe et al. 1985), we apply the model to simulate Atlantic Heathland vegetation dynamics and compare the outcome with previous models described for the same site. Our results indicate reasonable agreement between simulated and real data and with previous models based on Markov chains or on mechanistic spatial simulation, and that spatial models may detect similar species dynamics given by non-spatial models. We found evidence that a directional vegetation dynamics may not correspond to a monotonic increase in community spatial organization. The model framework may as well be applied to other systems.
To avoid the problems associated with the Euclidean distance for the calculation of plot-to-plot dissimilarity, a variety of alternative measures have been proposed. Among them, the chord and the Hellinger distances are both obtained by first transforming separately the species abundances in each plot vector and then by calculating the Euclidean distance on the chord-transformed or the Hellinger-transformed data. However, although both measures are routinely used by ecologists as substitutes for the Euclidean distance, they have very different properties. In this paper, using a modified version of Dalton's principle of transfers, I will show that, unlike the Euclidean distance, the chord and the Hellinger distances are not monotonic to changes in absolute abundances. Therefore, they are not interchangeable with the Euclidean distance. The moral of this story is that although dissimilarity may appear an intuitively simple concept, the properties of even the best-known indices are not fully understood. Therefore, a clear understanding of old and new coefficients is needed to evaluate their ability to highlight relevant aspects of compositional dissimilarity among plots.
Authors:Pál Jakusch, Tímea Kocsis, Ilona Kovácsné Székely, and István Gábor Hatvani
The aim of the present study is to extend the applicability of MRI measurements similar to those used in human diagnostics to the examination of water barriers in living plants, thus broadening their use in natural sciences. The cucumber, Cucumis sativus, and Phillyrea angustifolia, or false olive, were chosen as test plants. The MRI measurements were carried out on three samples of each plant in the same position vis-a-vis the MRI apparatus using a Siemens Avanto MRI scanner. Two different relaxation times were employed, T1, capable of histological mapping, and T2, used for the examination of water content. In the course of the analysis, it was found that certain histological formations and branching cause modifications to the intensity detected with relaxation time T2. Furthermore, these positions can also be found in T1 measurements. A monotonic correlation (cucumber: ρ = 0.829; false olive: ρ = –0.84) was observed between the T1 and T2 measurements. In the course of the statistical analysis of the signal intensities of the xylems it was concluded that they cannot be regarded as independent in a statistical sense; these changes rather depend on the anatomic structure of the plant, as the intensity profile is modified by nodes, leaves and branches. This serves as a demonstration of the applicability of MRI to the measurement of well know plant physiological processes. The special parametrization required for this equipment, which is usually used in human diagnostics, is also documented in the present study.