Shoot dieback in clipped young Golden Leyland (Cupressocyparis leylandii) trees - a physiological mechanism?

Shoot dieback is a problem in frequently trimmed Leyland hedges and is increasingly affecting gardeners’ choice of hedge trees, having a negative effect on a conifer nursery industry. Some damage can be attributed to the feeding by aphids, but it is unclear if there are also underlying physiological causes. In this study, we tested the hypothesis that shoot-clipping of conifer trees during adverse growing conditions (i.e. high air temperature and low soil moisture) could be leading to shoot ‘dieback’. Three-year-old Golden Leyland Cypress (x Cupressocyparis leylandii ‘Excalibur Gold’) plants were subjected to either a well-watered or droughted irrigation regime and placed in either a ‘hot’ (average day temperature = 40°C) or a ‘cool’ (average day temperature = 27°C) glasshouse compartment. Half of the plants from each glasshouse were clipped on Day 14 and again on Day 50. Measurements of soil moisture content (SMC), net CO2 assimilation rate (A), stomatal conductance (gs), branchlet xylem water potential (XWP), plant height and foliage colour were made. Within the clipped and unclipped treatments of both glasshouse compartments, plants from the droughted regime had significantly lower values for A, gs and XWP than those from the well-watered regime. However, there was no difference in these parameters between the hot and cool glasshouse compartments. The trends seen for A, gs and XWP of all treatments generally mirrored changes in SMC indicating a direct effect of water supply on these parameters. By the end of the experiment the overall foliage colour of plants from the hot glasshouse was darker than that of plants from the cool glasshouse and the overall foliage colour was also darker following shoot clipping. In general, shoot clipping led to increases in A, gs XWP and SMC. This may be due to the reduction in total leaf area leading to a greater supply of water for the remaining leaves. No shoot ‘dieback’ was observed in any treatment in response to drought stress or shoot-clipping.

Bergmann's rule and body size in mammals

Aim: To describe the geographical pattern of mean body size of the non-volant mammals of the Nearctic and Neotropics and evaluate the influence of five environmental variables that are likely to affect body size gradients. Location: The Western Hemisphere. Methods: We calculated mean body size (average log mass) values in 110 × 110 km cells covering the continental Nearctic and Neotropics. We also generated cell averages for mean annual temperature, range in elevation, their interaction, actual evapotranspiration, and the global vegetation index and its coefficient of variation. Associations between mean body size and environmental variables were tested with simple correlations and ordinary least squares multiple regression, complemented with spatial autocorrelation analyses and split-line regression. We evaluated the relative support for each multiple-regression model using AIC. Results: Mean body size increases to the north in the Nearctic and is negatively correlated with temperature. In contrast, across the Neotropics mammals are largest in the tropical and subtropical lowlands and smaller in the Andes, generating a positive correlation with temperature. Finally, body size and temperature are nonlinearly related in both regions, and split-line linear regression found temperature thresholds marking clear shifts in these relationships (Nearctic 10.9 °C; Neotropics 12.6 °C). The increase in body sizes with decreasing temperature is strongest in the northern Nearctic, whereas a decrease in body size in mountains dominates the body size gradients in the warmer parts of both regions. Main conclusions: We confirm previous work finding strong broad-scale Bergmann trends in cold macroclimates but not in warmer areas. For the latter regions (i.e. the southern Nearctic and the Neotropics), our analyses also suggest that both local and broad-scale patterns of mammal body size variation are influenced in part by the strong mesoscale climatic gradients existing in mountainous areas. A likely explanation is that reduced habitat sizes in mountains limit the presence of larger-sized mammals.

Managing groups of files in a rule oriented data management system (iRODS)

The iRODS system, created by the San Diego Supercomputing Centre, is a rule oriented data management system that allows the user to create sets of rules to define how the data is to be managed. Each rule corresponds to a particular action or operation (such as checksumming a file) and the system is flexible enough to allow the user to create new rules for new types of operations. The iRODS system can interface to any storage system (provided an iRODS driver is built for that system) and relies on its’ metadata catalogue to provide a virtual file-system that can handle files of any size and type. However, some storage systems (such as tape systems) do not handle small files efficiently and prefer small files to be packaged up (or “bundled”) into larger units. We have developed a system that can bundle small data files of any type into larger units - mounted collections. The system can create collection families and contains its’ own extensible metadata, including metadata on which family the collection belongs to. The mounted collection system can work standalone and is being incorporated into the iRODS system to enhance the systems flexibility to handle small files. In this paper we describe the motivation for creating a mounted collection system, its’ architecture and how it has been incorporated into the iRODS system. We describe different technologies used to create the mounted collection system and provide some performance numbers.

Robust neurofuzzy rule base knowledge extraction and estimation using subspace decomposition combined with regularization and D-optimality

A new robust neurofuzzy model construction algorithm has been introduced for the modeling of a priori unknown dynamical systems from observed finite data sets in the form of a set of fuzzy rules. Based on a Takagi-Sugeno (T-S) inference mechanism a one to one mapping between a fuzzy rule base and a model matrix feature subspace is established. This link enables rule based knowledge to be extracted from matrix subspace to enhance model transparency. In order to achieve maximized model robustness and sparsity, a new robust extended Gram-Schmidt (G-S) method has been introduced via two effective and complementary approaches of regularization and D-optimality experimental design. Model rule bases are decomposed into orthogonal subspaces, so as to enhance model transparency with the capability of interpreting the derived rule base energy level. A locally regularized orthogonal least squares algorithm, combined with a D-optimality used for subspace based rule selection, has been extended for fuzzy rule regularization and subspace based information extraction. By using a weighting for the D-optimality cost function, the entire model construction procedure becomes automatic. Numerical examples are included to demonstrate the effectiveness of the proposed new algorithm.