A functional approach to predicting shifts in weed floras in response to management or environmental change requires the combination of data on weed traits with analytical frameworks that capture the ...filtering effect of selection pressures on traits. A weed traits database (WTDB) was designed, populated and analysed, initially using data for 19 common European weeds, to begin to consolidate trait data in a single repository. The initial choice of traits was driven by the requirements of empirical models of weed population dynamics to identify correlations between traits and model parameters. These relationships were used to build a generic model, operating at the level of functional traits, to simulate the impact of increasing herbicide and fertiliser use on virtual weeds along gradients of seed weight and maximum height. The model generated ‘fitness contours’ (defined as population growth rates) within this trait space in different scenarios, onto which two sets of weed species, defined as common or declining in the UK, were mapped. The effect of increasing inputs on the weed flora was successfully simulated; 77% of common species were predicted to have stable or increasing populations under high fertiliser and herbicide use, in contrast with only 29% of the species that have declined. Future development of the WTDB will aim to increase the number of species covered, incorporate a wider range of traits and analyse intraspecific variability under contrasting management and environments.
Cognitive decline, especially the slowing of information processing speed, is associated with normal ageing. This decline may be due to brain cortico-cortical disconnection caused by age-related ...white matter deterioration. We present results from a large, narrow age range cohort of generally healthy, community-dwelling subjects in their seventies who also had their cognitive ability tested in youth (age 11 years). We investigate associations between older age brain white matter structure, several measures of information processing speed and childhood cognitive ability in 581 subjects. Analysis of diffusion tensor MRI data using Tract-based Spatial Statistics (TBSS) showed that all measures of information processing speed, as well as a general speed factor composed from these tests (
g
speed
), were significantly associated with fractional anisotropy (FA) across the white matter skeleton rather than in specific tracts. Cognitive ability measured at age 11 years was not associated with older age white matter FA, except for the
g
speed
-independent components of several individual processing speed tests. These results indicate that quicker and more efficient information processing requires global connectivity in older age, and that associations between white matter FA and information processing speed (both individual test scores and
g
speed
), unlike some other aspects of later life brain structure, are generally not accounted for by cognitive ability measured in youth.
Understanding how plant species with similar resource requirements co‐exist has been a long‐standing ecological question with several theoretical explanations. One potential mechanism is the storage ...effect hypothesis. According to this hypothesis, species co‐exist because they differ in when they are most actively using resource and, therefore, respond differently to environmental perturbation. The hypothesis is based on two main assumptions: (i) two competitors have different responses to climate and (ii) the responses to climate are mediated by changes in the relative importance of intra‐ and interspecific competition. The hypothesis could provide useful insights into the role of climate in maintaining weed species diversity and potential shifts in dominant species under climate change. This study tested the basic principles of the storage effect hypothesis on weed communities using data from the Broadbalk long‐term fertiliser experiment. Relative abundance of weeds in 10 plots with contrasting fertility but no herbicides was assessed for 21 years. Multivariate analyses and generalised additive mixed models were used to analyse the data. The following pairs of species were found to be adapted to similar fertiliser levels, but diverged in their response to climate: (i) Papaver rhoeas–Tripleurospermum inodorum, (ii) Medicago lupulina–Vicia sativa and (iii) Scandix pecten‐veneris–Ranunculus arvensis. Contrasting responses to spring temperature within these species pairs modified the competition balance providing evidence for the storage effect hypothesis and helping to explain weed co‐existence in the Broadbalk experiment.
The nature of the basins of attraction of a Hopfield network is as important as the capacity. Here a new learning rule is re-introduced. This learning rule has a higher capacity than that of the Hebb ...rule, and still keeps important functionality, such as incrementality and locality, which the pseudo-inverse lacks. However the basins of attraction of the fixed points of this learning rule have not yet been studied. Three important characteristics of basins of attraction are considered: indirect and direct basins of attraction, distribution of sizes of basins of attraction and the shape of the basins of attraction. The results for the new learning rule are compared with those of the Hebb rule. The size of direct and indirect basins of attractions are generally larger for the new rule than for the Hebb rule, the distribution of sizes is more even, and the shape of the basins more round.
A novel conceptual framework is presented that proposes to apply trait-based approaches to predicting the impact of environmental change on ecosystem service delivery by multi-trophic systems. ...Development of the framework was based on an extension of the response—effect trait approach to capture functional relationships that drive trophic interactions. The framework was populated with worked examples to demonstrate its flexibility and value for linking disparate data sources, identifying knowledge gaps and generating hypotheses for quantitative models.
In this paper, we describe a novel application of sigma-point methods to continuous-discrete filtering. The nonlinear continuous-discrete filtering problem is often computationally intractable to ...solve. Assumed density filtering methods attempt to match statistics of the filtering distribution to some set of more tractable probability distributions. Filters such as these are usually decompose the problem into two sub-problems. The first of these is a prediction step, in which one uses the known dynamics of the signal to predict its state at time tk+1 given observations up to time tk. In the second step, one updates the prediction upon arrival of the observation at time tk+1. The aim of this paper is to describe a novel method that improves the prediction step. We decompose the Brownian motion driving the signal in a generalised Fourier series, which is truncated after a number of terms. This approximation to Brownian motion can be described using a relatively small number of Fourier coefficients, and allows us to compute statistics of the filtering distribution with a single application of a sigma-point method. Assumed density filters that exist in the literature usually rely on discretisation of the signal dynamics followed by iterated application of a sigma point transform (or a limiting case thereof). Iterating the transform in this manner can lead to loss of information about the filtering distribution in highly non-linear settings. We demonstrate that our method is better equipped to cope with such problems.
Large astronomical data bases obtained from sky surveys such as the SuperCOSMOS Sky Survey (SSS) invariably suffer from spurious records coming from the artefactual effects of the telescope, ...satellites and junk objects in orbit around the Earth and physical defects on the photographic plate or CCD. Though relatively small in number, these spurious records present a significant problem in many situations, where they can become a large proportion of the records potentially of interest to a given astronomer. Accurate and robust techniques are needed for locating and flagging such spurious objects, and we are undertaking a programme investigating the use of machine learning techniques in this context. In this paper we focus on the four most common causes of unwanted records in the SSS: satellite or aeroplane tracks, scratches, fibres and other linear phenomena introduced to the plate, circular haloes around bright stars due to internal reflections within the telescope and diffraction spikes near to bright stars. Appropriate techniques are developed for the detection of each of these. The methods are applied to the SSS data to develop a data set of spurious object detections, along with confidence measures, which can allow these unwanted data to be removed from consideration. These methods are general and can be adapted to other astronomical survey data.
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