The European Union, A Healthy Negotiator? A Study on its Unity in External Representation and Performance in the World Health Organization

In this article the effectiveness of the member states of the European Union (EU) and the European Commission in negotiations taking place in the World Health Organisation (WHO) is analysed and related to its ability to act as a united bloc. EU unity in external representation is taken to result from European Community (EC) competence, preference homogeneity and processes of socialisation among EU member states representatives. A comparison is made between the negotiations on global strategies on diet, physical activity and health (DPAS, 2004) and on public health, innovation and intellectual property (PHI, 2008). In the DPAS, member states operated primarily on the basis of national positions, whereas in the PHI they operated on the basis of a coordinated position brought forward by the EU presidency and European Commission. In both cases the EU (or a majority of EU member states) was moderately successful in obtaining its objectives in the negotiations. More unity in external representation originated from the Commission claiming EC competence, a pro-active EU presidency and a process of intensive EU coordination becoming gradually institutionalised. Member states' representatives identified economies of scale in conducting a unified external representation, although their initial preferences were rather different. Identified drawbacks included the extensive time spent in EU coordination, the dependency on the intention and qualities of the lead negotiator, and the EUs difficulties with reacting swiftly to new issues coming up in the negotiations

Impact of spatial variability and sampling design on model performance

Soil characteristics and species distributions often display a spatial variability at different scales. In case measurements are costly in labor time or money a choice has to be made between a high sampling resolution at small scales with low spatial cover of the study area or a lower sampling resolution at the small scales resulting in local data uncertainties but a better spatial cover of the whole area. This dilemma is often faced in the design of field sampling campaigns for large scale studies. When the gathered field data are subsequently used for modelling purposes the choice of sampling design and resulting data quality influence the model performance criteria. We studied this influence with a virtual model study based on a large dataset of field information on spatial variation of earthworms at different scales. We built a virtual map of anecic earthworm distributions over the Weiherbach catchment (Baden-Württemberg). The field scale abundance of earthworms was estimated using a catchment scale model based on 65 field measurements. Subsequently the high small scale variability was added using semi-variograms, based on five fields with a total of 430 measurements divided in a spatially nested sampling design over these fields, to estimate the nugget, range and standard deviation of measurements within the fields. With the produced maps, we performed virtual samplings of one up to 50 random points per field. We then used these data to rebuild the catchment scale models of anecic earthworm abundance with the same model parameters as in the work by Palm et al. (2013). The results of the models show clearly that a large part of the non-explained deviance of the models is due to the very high small scale variability in earthworm abundance: the models based on single virtual sampling points on average obtain an explained deviance of 0.20 and a correlation coefficient of 0.64. With increasing sampling points per field, we averaged the measured abundance of the sampling within each field to obtain a more representative value of the field average. Doubling the samplings per field strongly improved the model performance criteria. The relationship between number of samplings and performance criteria can be described with a saturation curve. We will discuss the impact of data variability at sampling scale on model performance and the implications for sampling design and assessment of model results as well as ecological inferences

On the concept of animal innovation and the challenge of studying innovation in the wild

The commentaries have both drawn out the implications of, and challenged, our definition and operationalization of innovation. In this response, we reply to these concerns, discuss the differences between our operationalization and the preexisting operationalization if innovation, and make suggestions for the advancement of the challenging and exciting field of animal innovatio

Animal innovation defined and operationalized

Innovation is a key component of most definitions of culture and intelligence. Additionally, innovations may affect a species' ecology and evolution. Nonetheless, conceptual and empirical work on innovation has only recently begun. In particular, largely because the existing operational definition (first occurrence in a population) requires long-term studies of populations, there has been no systematic study of innovation in wild animals. To facilitate such study, we have produced a new definition of innovation: Innovation is the process that generates in an individual a novel learned behavior that is not simply a consequence of social learning or environmental induction. Using this definition, we propose a new operational approach for distinguishing innovations in the field. The operational criteria employ information from the following sources: (1) the behavior's geographic and local prevalence and individual frequency; (2) properties of the behavior, such as the social role of the behavior, the context in which the behavior is exhibited, and its similarity to other behaviors; (3) changes in the occurrence of the behavior over time; and (4) knowledge of spontaneous or experimentally induced behavior in captivity. These criteria do not require long-term studies at a single site, but information from multiple populations of a species will generally be needed. These criteria are systematized into a dichotomous key that can be used to assess whether a behavior observed in the field is likely to be an innovatio

ADAPTr Exhibition

The book is one of the outcomes of the grant (funded by the EU seventh framework Programme grant number 317325. Period of grant 01.01.2013 to 31.12.20160. It describes the exhibition held in Ambika P3. It includes a double page statement from each of the seven partners and from each of the 42 research fellows employed under the scheme. There are four new essays (Prof Richard Blythe, Prof Kester Rattenbury, Prof Leon van Schaik, Dr Fleur Watson) a preface by Prof John Verbeke, and introduction by Prof Katharine Heron. It is included on the ADAPTr website and submitted to the EU as one of the deliverable outputs

Risk perceptions of cyber-security and precautionary behaviour

A quantitative empirical online study examined a set of 16 security hazards on the Internet and two comparisons in 436 UK- and US students, measuring perceptions of risk and other risk dimensions. First, perceived risk was highest for identity theft, keylogger, cyber-bullying and social engineering. Second, consistent with existing theory, significant predictors of perceived risk were voluntariness, immediacy, catastrophic potential, dread, severity of consequences and control, as well as Internet experience and frequency of Internet use. Moreover, control was a significant predictor of precautionary behaviour. Methodological implications emphasise the need for non-aggregated analysis and practical implications emphasise risk communication to Internet users

Exploring the data divide through a social practice lens : A qualitative study of UK cattle farmers

Appropriate management decisions are key for sustainable and profitable beef and dairy farming. Data-driven technologies aim to provide information which can improve farmers’ decision-making practices. However, data-driven technologies have resulted in the emergence of a “data divide”, in which there is a gap between the generation and use of data. Our study aims to further understand the data divide by drawing on social practice theory to recognise the emergence, linkages, and reproduction of youngstock data practices on cattle farms in the UK. Eight focus groups with fifteen beef and nineteen dairy farmers were completed. The topics of discussion included data use, technology use, disease management in youngstock, and future goals for their farm. The transcribed data were analysed using reflexive thematic analysis with a social practice lens. Social practice theory uses practices as the unit of analysis, rather than focusing on individual behaviours. Practices are formed of three elements: meaning (e.g., beliefs), materials (e.g., objects), and competencies (e.g., skills) and are connected in time and space. We conceptualised the data divide as a disconnection of data collection practices and data use and interpretation practices. Consequently, we were able to generate five themes that represent these breaks in connection.Our findings suggest that a data divide exists because of meanings that de-stabilise practices, tensions in farmers’ competencies to perform practices, spatial and temporal disconnects, and lack of forms of feedback on data practices. The data preparation practice, where farmers had to merge different data sources or type up handwritten data, had negative meanings attached to it and was therefore sometimes not performed. Farmers tended to associate data and technology practices with larger dairy farms, which could restrict beef and small-scale dairy farms from performing these practices. Some farmers suggested that they lacked the skills to use technologies and struggled to transform their data into meaningful outputs. Data preparation and data use and interpretation practices were often tied to an office space because of the required infrastructure, but farmers preferred to spend time outdoors and with their animals. There appeared to be no normalisation of what data should be collected or what data should be analysed, which made it difficult for farmers to benchmark their progress. Some farmers did not have access to discussion groups or veterinarians who were interested in data and therefore could not get feedback on their data practices.These results suggest that the data divide exists because of three types of disconnect: a disconnect between elements within a practice because of tensions in competencies or negative meanings to perform a practice; a disconnect between practices because of temporal or spatial differences; and a break in the reproduction of practices because of lack of feedback on their practices. Data use on farms can be improved through transformation of practices by ensuring farmers have input in the design of technologies so that they align with their values and competencies

Impact of Temporal Macropore Dynamics on Infiltration : Field Experiments and Model Simulations

Macropores greatly affect water and solute transport in soils. Most macropores are of biogenic origin; however, the resulting seasonal dynamics are often neglected. Our study aimed to examine temporal changes in biopore networks and the resulting infiltration patterns. We performed infiltration experiments with Brilliant Blue on pastureland in the Luxembourgian Attert catchment (spring, summer, and autumn 2015). We developed an image-processing scheme to identify and quantify changes in biopores and infiltration patterns. Subsequently, we used image-derived biopore metrics to parameterize the ecohydrological model echoRD (ecohydrological particle model based on representative domains), which includes explicit macropore flow and interaction with the soil matrix. We used the model simulations to check whether biopore dynamics affect infiltration. The observed infiltration patterns revealed variations in both biopore numbers and biopore–matrix interaction. The field-observed biopore numbers varied over time, mainly in the topsoil, with the largest biopore numbers in spring and the smallest in summer. The number of hydrologically effective biopores in the topsoil seems to determine the number and thereby the fraction of effective biopores in the subsoil. In summer, a strong biopore–matrix interaction was observed. In spring, the dominant process was rapid drainage, whereas in summer and autumn, most of the irrigated water was stored in the examined profiles. The model successfully simulated infiltration patterns for spring, summer, and autumn using temporally different macropore setups. Using a static macropore parameterization the model output deviated from the observed infiltration patterns, which emphasizes the need to consider macropores and their temporal dynamics in soil hydrological modeling

Forty-Year Analysis of Colonoscopic Surveillance Program for Neoplasia in Ulcerative Colitis: An Updated Overview

C.R.C. was funded by the Derek Willoughby Fund for Inflammatory Research. A.L.H. and T.A.G. were funded by Higher Education Funding Council of England