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Overview of the project Approximately £400 million is invested annually in agri environment schemes in England, designed to compensate farmers for loss of production (income foregone) and additional costs, to meet environmental objectives. A further £1.8 billion in subsidies is paid to comply with environmental conditions of cross compliance and greening. In December 2019, it was estimated there were ca. 50,000 agri-environment agreements covering 1.8 million hectares. Current and previous monitoring of agri-environment schemes is diverse and varied, and conducted at scales of specific management options, whole AES agreements and 1 km squares covering multiple agreements. The Monitoring and Evaluation Programme of these schemes aims to deliver evidence to achieve the following: • Evaluate the delivery of agri-environment schemes and their effectiveness in achieving their intended policy objectives; • Inform current and future agri-environment policy, scheme delivery and development; • Fulfil domestic and European reporting requirements. The existing Monitoring and Evaluation Programme (2015-2020) for Agri-environment schemes (AES) in England includes four elements: integrated monitoring, landscape scale, thematic, and evaluation and synthesis. To date, monitoring has been primarily based on tried-and-tested approaches, which provide widely recognised metrics of quality, condition and species-resolution biodiversity data, and that have counterfactuals often derived from national monitoring schemes to enable evaluation of AES. Developing technologies will need to integrate with existing tried-and-tested approaches but offer huge potential for more extensive and efficient evaluation of AES. In this project we focus on technologies with greatest potential to deliver enhanced and more cost-effective monitoring of environmental ‘outcomes’ of direct land management and interventions, in support of the Environmental Land Management Scheme (ELMS). The technology areas covere

© 2019, © 2019 Taylor & Francis Group, LLC. Intimate partner violence (IPV) is a crime encompassing physical, psychological, financial, emotional, and sexual abuse by a current or former partner. The presence of love in abusive relationships tends to be marginalized in healthcare discourses. The authors' aim in this qualitative systematic literature review was to explore the interplay between IPV and romantic love and their impacts on women. The review provides a rare (but much needed) explanation and acknowledgement that love does sometimes exist in abusive relationships. These insights will assist healthcare workers in offering empathic care to women, based on understandings of the complex and highly unsettled nature of love in abusive relationships.

There is no sign of saturation in accumulation of alien species (AS) introductions worldwide, additionally the rate of spread for some species has also been shown to be increasing. However, the challenges of gathering information on AS are recognized. Recent developments in citizen science (CS) provide an opportunity to improve data flow and knowledge on AS while ensuring effective and high quality societal engagement with the issue of IAS (Invasive Alien Species). Advances in technology, particularly on-line recording and smartphone apps, along with the development of social media, have revolutionized CS and increased connectivity while new and innovative analysis techniques are emerging to ensure appropriate management, visualization, interpretation and use and sharing of the data. In early July 2018 we launched a European CO-operation in Science and Technology (COST) Action to address multidisciplinary research questions in relation to developing and implementing CS, advancing scientific understanding of AS dynamics while informing decision-making specifically implementation of technical requirements of relevant legislation such as the EU Regulation 1143/2014 on IAS. It will also support the EU biodiversity goals and embedding science within society. The Action will explore and document approaches to establishing a European-wide CS AS network. It will embrace relevant innovations for data gathering and reporting to support the implementation of monitoring and surveillance measures, while ensuring benefits for society and citizens, through an AS CS European network. The Action will, therefore, increase levels of participation and quality of engagement with current CS initiatives, ensuring and evaluating educational value, and improve the value outcomes for potential users including citizens, scientists, alien species managers, policy-makers, local authorities, industry and other stakeholders.

In this thesis we examine purchase exchanges between high status buyers and suppliers of lower status in luxury markets. While extant literature has treated status inequalities as an objective distinction in status rank, Study 1 on boat builders presents a novel approach towards conceptualising emerging status inequalities on the basis of the buyer’s varying status salience at the impending exchange. Given contingent factors (purchase visibility; buyer status position; type of market uncertainty), we demonstrate this from the buyer’s willingness to trade-off incumbent supplier status for improved product attributes from a lower status supplier. Studies 2 and 3 address end-consumer evaluations of high status cars equipped with components of varying visibility from lower status suppliers. Results in general portray the end-consumers’ higher preference towards acquiring improved components regardless of supplier status. When comparing lower status suppliers of unknown and of known reputation, consumers manifested a more favourable attitude towards high status cars which include low visibility components from new entrant suppliers. We moreover demonstrate effects of potential status transfer in the affiliation. In spite of their newness, unknown suppliers of ingredients of high visibility, benefit from a higher perceived reputation than similar suppliers of low visibility ingredients.

Motivation: Biological systems are complex and challenging to model and therefore model reuse is highly desirable. To promote model reuse, models should include both information about the specifics of simulations and the underlying biology in the form of metadata. The availability of computationally tractable metadata is especially important for the effective automated interpretation and processing of models. Metadata are typically represented as machine-readable annotations which enhance programmatic access to information about models. Rule-based languages have emerged as a modelling framework to represent the complexity of biological systems. Annotation approaches have been widely used for reaction-based formalisms such as SBML. However, rule-based languages still lack a rich annotation framework to add semantic information, such as machine-readable descriptions, to the components of a model. Results: We present an annotation framework and guidelines for annotating rule-based models, encoded in the commonly used Kappa and BioNetGen languages. We adapt widely adopted annotation approaches to rule-based models. We initially propose a syntax to store machine-readable annotations and describe a mapping between rule-based modelling entities, such as agents and rules, and their annotations. We then describe an ontology to both annotate these models and capture the information contained therein, and demonstrate annotating these models using examples. Finally, we present a proof of concept tool for extracting annotations from a model that can be queried and analyzed in a uniform way. The uniform representation of the annotations can be used to facilitate the creation, analysis, reuse and visualization of rule-based models. Although examples are given, using specific implementations the proposed techniques can be applied to rule-based models in general.

Motivation: Biological systems are complex and challenging to model and therefore model reuse is highly desirable. To promote model reuse, models should include both information about the specifics of simulations and the underlying biology in the form of metadata. The availability of computationally tractable metadata is especially important for the effective automated interpretation and processing of models. Metadata are typically represented as machine-readable annotations which enhance programmatic access to information about models. Rule-based languages have emerged as a modelling framework to represent the complexity of biological systems. Annotation approaches have been widely used for reaction-based formalisms such as SBML. However, rule-based languages still lack a rich annotation framework to add semantic information, such as machine-readable descriptions, to the components of a model. Results: We present an annotation framework and guidelines for annotating rule-based models, encoded in the commonly used Kappa and BioNetGen languages. We adapt widely adopted annotation approaches to rule-based models. We initially propose a syntax to store machine-readable annotations and describe a mapping between rule-based modelling entities, such as agents and rules, and their annotations. We then describe an ontology to both annotate these models and capture the information contained therein, and demonstrate annotating these models using examples. Finally, we present a proof of concept tool for extracting annotations from a model that can be queried and analyzed in a uniform way. The uniform representation of the annotations can be used to facilitate the creation, analysis, reuse and visualization of rule-based models. Although examples are given, using specific implementations the proposed techniques can be applied to rule-based models in general.

Constructing simulatable models for BioBricks by hand is a complex and time-consuming task. The time taken could be reduced by using Computer Aided Design (CAD) tools to aid in designing models, but these tools need to be augmented with domain-specific knowledge. Here we propose a standard for a RESTful (Richardson, 2007) API which facilitates the discovery and publication of models of functional biological units. This API is designed to produce parts models which can be automatically combined into complete, simulatable models of entire systems.

The distribution patterns of 391 rare and scarce British plants (species recorded in 100 or fewer 10 × 10 km squares) were characterized by their distributional area (area of occupancy at 1-km scale: AOO1) and levels of aggregation (as reflected in fractal dimensions measured across two scales: D110 and D10100). Eighteen plant traits were tested for relationships to AOO, and to fractal dimension while controlling for AOO. These included both directly heritable traits (e.g. life-form) and emergent properties that are, at most, indirectly heritable (e.g. typical local density). The latter set included an index of net distributional change and an index of range dynamism. Only two traits, habitat preference and local abundance, were significantly related to AOO1, but about half were associated with fractal dimension. Relatively aggregated fine-scale distributions (high D110) were related to high local abundance, lack of specialized, long-distance dispersal mechanisms, habitat preference and an increasing range size with relatively few local extinctions (i.e. a positive index of change with low dynamism). Relatively aggregated coarse-scale distributions (high D10100) were related to the use of insect pollinators, obligate outcrossing, habitat preference and relatively stable ranges (low dynamism). Multivariate analyses of subsets of conceptually related variables showed that few variables interacted to affect distributional variables. A highly significant negative relationship between dynamism and fractal dimension appears to be driven primarily by high rates of local extinction, leading to relatively scattered, diffuse range structures. Furthermore, it suggests that recent population trends may be inferred from snapshots of contemporary distribution patterns. The role and interpretation of phylogenetically informed analyses in studies such as this are debatable. However, we found similar relationships in both phylogenetically informed and conventional analyses for all v