Causal relationships vs. emergent patterns in the global controls of fire frequency

Global controls on month-by-month fractional burnt area (2000–2005) were investigated by fitting a generalised linear model (GLM) to Global Fire Emissions Database (GFED) data, with 11 predictor variables representing vegetation, climate, land use and potential ignition sources. Burnt area is shown to increase with annual net primary production (NPP), number of dry days, maximum temperature, grazing-land area, grass/shrub cover and diurnal temperature range, and to decrease with soil moisture, cropland area and population density. Lightning showed an apparent (weak) negative influence, but this disappeared when pure seasonal-cycle effects were taken into account. The model predicts observed geographic and seasonal patterns, as well as the emergent relationships seen when burnt area is plotted against each variable separately. Unimodal relationships with mean annual temperature and precipitation, population density and gross domestic product (GDP) are reproduced too, and are thus shown to be secondary consequences of correlations between different controls (e.g. high NPP with high precipitation; low NPP with low population density and GDP). These findings have major implications for the design of global fire models, as several assumptions in current models – most notably, the widely assumed dependence of fire frequency on ignition rates – are evidently incorrect.

Blueberry-induced changes in spatial working memory correlate with changes in hippocampal CREB phosphorylation and brain-derived neurotrophic factor (BDNF) levels

Phytochemical-rich foods have been shown to be effective at reversing age-related deficits in memory in both animals and humans. We show that a supplementation with a blueberry diet (2% w/w) for 12 weeks improves the performance of aged animals in spatial working memory tasks. This improvement emerged within 3 weeks and persisted for the remainder of the testing period. Memory performance correlated well with the activation of cAMP-response element-binding protein (CREB) and increases in both pro- and mature levels of brain-derived neurotrophic factor (BDNF) in the hippocampus. Changes in CREB and BDNF in aged and blueberry-supplemented animals were accompanied by increases in the phosphorylation state of extracellular signal-related kinase (ERK1/2), rather than that of calcium calmodulin kinase (CaMKII and CaMKIV) or protein kinase A. Furthermore, age and blueberry supplementation were linked to changes in the activation state of Akt, mTOR, and the levels of Arc/Arg3.1 in the hippocampus, suggesting that pathways involved in de novo protein synthesis may be involved. Although causal relationships cannot be made among supplementation, behavior, and biochemical parameters, the measurement of anthocyanins and flavanols in the brain following blueberry supplementation may indicate that changes in spatial working memory in aged animals are linked to the effects of flavonoids on the ERK-CREB-BDNF pathway. (c) 2008 Elsevier Inc. All Fights reserved.

Future directions for child anxiety theory and treatment

The aim of this introductory paper, and of this special issue of Cognition and Emotion, is to stimulate debate about theoretical issues that will inform child anxiety research in the coming years. Papers included in this special issue have arisen from an Economic and Social Research Council (ESRC, UK) funded seminar series, which we called Child Anxiety Theory and Treatment (CATTS). We begin with an overview of the CATTS project before discussing (1) the application of adult models of anxiety to children, and (2) the role of parents in child anxiety. We explore the utility of adult models of anxiety for child populations before discussing the problems that are associated with employing them uncritically in this context. The study of anxiety in children provides the opportunity to observe the trajectory of anxiety and to identify variables that causally influence its development. Parental influences are of particular interest and new and imaginative strategies are required to isolate the complex network of causal relationships therein. We conclude by suggesting that research into the causes and developmental course of anxiety in children should be developed further. We also propose that, although much is known about the role of parents in the development of anxiety, it would be useful for research in this area to move towards an examination of the specific processes involved. We hope that these views represent a constructive agenda for people in the field to consider when planning future research.

Determinants of biosecurity behaviour of British cattle and sheep farmers: a behavioural economics analysis

The paper analyses the impact of a priori determinants of biosecurity behaviour of farmers in Great Britain. We use a dataset collected through a stratified telephone survey of 900 cattle and sheep farmers in Great Britain (400 in England and a further 250 in Wales and Scotland respectively) which took place between 25 March 2010 and 18 June 2010. The survey was stratified by farm type, farm size and region. To test the influence of a priori determinants on biosecurity behaviour we used a behavioural economics method, structural equation modelling (SEM) with observed and latent variables. SEM is a statistical technique for testing and estimating causal relationships amongst variables, some of which may be latent using a combination of statistical data and qualitative causal assumptions. Thirteen latent variables were identified and extracted, expressing the behaviour and the underlying determining factors. The variables were: experience, economic factors, organic certification of farm, membership in a cattle/sheep health scheme, perceived usefulness of biosecurity information sources, knowledge about biosecurity measures, perceived importance of specific biosecurity strategies, perceived effect (on farm business in the past five years) of welfare/health regulation, perceived effect of severe outbreaks of animal diseases, attitudes towards livestock biosecurity, attitudes towards animal welfare, influence on decision to apply biosecurity measures and biosecurity behaviour. The SEM model applied on the Great Britain sample has an adequate fit according to the measures of absolute, incremental and parsimonious fit. The results suggest that farmers’ perceived importance of specific biosecurity strategies, organic certification of farm, knowledge about biosecurity measures, attitudes towards animal welfare, perceived usefulness of biosecurity information sources, perceived effect on business during the past five years of severe outbreaks of animal diseases, membership in a cattle/sheep health scheme, attitudes towards livestock biosecurity, influence on decision to apply biosecurity measures, experience and economic factors are significantly influencing behaviour (overall explaining 64% of the variance in behaviour).

The climate and environment of Byzantine Anatolia: integrating science, history, and archaeology

The integration of high-resolution archaeological, textual, and environmental data with longer-term, low-resolution data affords greater precision in identifying some of the causal relationships underlying societal change. Regional and microregional case studies about the Byzantine world—in particular, Anatolia, which for several centuries was the heart of that world—reveal many of the difficulties that researchers face when attempting to assess the influence of environmental factors on human society. The Anatolian case challenges a number of assumptions about the impact of climatic factors on socio-political organization and medium-term historical evolution, highlighting the importance of further collaboration between historians, archaeologists, and climate scientists.

Towards expressive rule induction on IP network event streams

In order to gain insights into events and issues that may cause errors and outages in parts of IP networks, intelligent methods that capture and express causal relationships online (in real-time) are needed. Whereas generalised rule induction has been explored for non-streaming data applications, its application and adaptation on streaming data is mostly undeveloped or based on periodic and ad-hoc training with batch algorithms. Some association rule mining approaches for streaming data do exist, however, they can only express binary causal relationships. This paper presents the ongoing work on Online Generalised Rule Induction (OGRI) in order to create expressive and adaptive rule sets real-time that can be applied to a broad range of applications, including network telemetry data streams.