The story of an educational innovation: the MA in Catholic School Leadership at St Mary's University College, Twickenham, 1997–2013. Principles, pedagogy and research studies

Within the context of a national shortage of high quality applicants for senior leadership posts, there is a need to nurture future leaders in Catholic primary and secondary schools and to promote their professional development and formation. If they are to be fully effective, teachers in Catholic schools need continually to develop their leadership skills and, crucially, they need to understand how they can best support the educational mission of the Catholic Church. This paper tells the story of the establishment and continuing development of a unique M-level programme, which sets out to combine a Catholic philosophy of education with the principles and practice of effective school leadership

Quantifying the impacts of invasive non-native species using key functional traits

Invasive non-native species place high pressures on native communities and can result in ecological impacts often associated with differences in key functional behaviours that mediate top-down and bottom-up forces. In this thesis, I use two model systems, the UK Coccinellidae system and the UK freshwater amphipod system, to quantify per-capita differences between native and invasive non-native species. I scale these studies up to more complex ecological communities and attempt to account for additional environmental pressures (e.g. pathogenic infection). First, I present a laboratory experiment to quantify the per-capita differences in predatory behaviour between native and invasive non-native Coccinellidae with a pathogen (Beauveria bassiana) exposure treatment. H. axyridis was the most effcient predator and pathogenic infection reduced the forage ability in all species. Second, I used existing H. axyridis distribution and aphid abundance data to quantify H. axyridis' impact through top-down forces. The arrival of H. axyridis is correlated with significant changes in aphid abundance and, of the 14 species studied, five declined in abundance, four increased, while the remaining five showed no significant change. Third, I measured the per-capita differences in detrital processing rates between native and invasive freshwater amphipods when provided with three diets of differing resource quality and maintained at three temperatures. The rates of detrital processing varied between the native and invasive non-native species and between the temperature and resource quality treatments. Fourth, I applied native and invasive amphipods at two density treatments (high and low) to a field mesocosm experiment to measure how the per-capita differences impacted more complex ecological systems. The presence of invasive amphipods changed the macroinvertebrate community composition and ecosystem functioning. I finish by highlighting that our understanding as to how the pressures of invasive non-native species interact with additional environmental stressors remains limited and an area that warrants further investigation

Exploring drivers of within-field crop yield variation using a national precision yield network

1. While abiotic drivers of yields represent important limiting factors to crop productivity, the role of biotic drivers that could be directly managed by farmers (e.g. agri-environment schemes supporting key ecosystem services) remains poorly understood. Precision yield mapping provides an opportunity to understand the factors that limit agricultural yield through the interpretation of high-resolution cropping data. This has the potential to inform future precision agricultural management, such as the targeted application of agrochemicals, promoting increased sustainability in modern agricultural systems. 2. We used precision yield measurements from a network of 1174 fields in England (2006–2020) to identify drivers of within-field yield variation in winter wheat and oilseed rape. Potential drivers included climate, topography and landscape composition and configuration. We then explored relationships between in-field yield patterns and local landscape context, including the presence of features associated with ecosystem benefits. 3. Proximity to the field edge was associated with reduced yields in 85% of wheat and 87% of oilseed fields. This translating to an approximate reduction of 10% in wheat and 18% in oilseed yields lost due to field edge effects. 4. We found evidence that reduced yields at the field edges were associated with biotic features of the surrounding landscape, including the occurrence of semi-natural habitats. Specifically, agri-environment scheme (AES) presence increased the rate at which yields at field edges approach those of the field centres. This suggests that AES occurrence within a landscape (rather than field adjacent) may increase edge effects. However, these trends are unclear and suggest interactions between drivers and the spatial and temporal scale of investigation. 5. Synthesis and applications. While we found evidence of landscape context mitigating against field edge effects, these were counterintuitive. For example, AES at a landscape scale appeared to increase the severity of edge effects. This study highlights a lack of environmental data at sufficiently high spatiotemporal resolution to match that of precision agriculture data. This mismatch is hindering the effective integration of precision agriculture data in an environmental policy and/or management context and potentially leading to unnecessarily poorly informed decisions related to AES deployment. This may limit environmental and economic benefits

A framework for improved predictions of the climate impacts on potential yields of UK winter wheat and its applicability to other UK crops

•Changes in the frequency of extreme weather events related to climate change potentially pose significant challenges to UK agricultural production. There is a need for improved climate change risk assessments to support adaptation strategies and to ensure security of food production in future. •We describe an innovative and practical framework for spatially explicit modelling of climate change impacts on crop yields, based on the UKCP18 climate projections. Our approach allows the integration of relatively simple crop growth models with high spatial and temporal resolution Earth Observation datasets, describing changes in crop growth parameters within year and over the longer term. We focus on modelling winter wheat, a commercially important crop. We evaluate the results of the model against precision yield data collected from 719 fields. We show that the assimilation of leaf area index data from Sentinel-2 satellite observations improves the agreement of the modelled yields with those observed. Our national-scale results indicate that wheat production initially becomes more favourable under climate change across much of the UK with the projected increase in temperature. From 2050 onwards, yields increase northwards, whilst they decline in South East England as the decrease in precipitation offsets the benefits of rising temperature. •Our framework can readily accommodate growth models for other crops and LAI retrievals from other satellite sensors. The ability to explore impacts of crop yields at fine spatial resolutions is an important part of assessing the potential risks of climate change to UK agriculture and of designing more climate resilient agricultural systems

Mapping the ratio of agricultural inputs to yields reveals areas with potentially less sustainable farming

Fertilisers and pesticides are major sources of the environmental harm that results from farming, yet it remains difficult to target reductions in their impacts without compromising food production. We suggest that calculating the ratio of agrochemical inputs to yield can provide an indication of the potential sustainability of farmland, with those areas that have high input relative to yield being considered as less sustainable. Here we design an approach to characterise such Input to Yield Ratios (IYR) for four inputs that can be plausibly linked to environmental impacts: the cumulative risk resulting from pesticide exposure for honeybees and for earthworms, and the amount of nitrogen or phosphorus fertiliser applied per unit area. We capitalise on novel national-scale data to assess IYR for wheat farming across all of England. High-resolution spatial patterns of IYR differed among the four inputs, but hotspots, where all four IYRs were high, were in key agricultural regions not usually characterised as having low suitability for cropping. By scaling the magnitude of each input against crop yield, the IYR does not penalise areas of high yield with higher inputs (important for food production), or areas with low yields but which are achieved with low inputs (important as low impact areas). Instead, the IYR provides a globally applicable framework for evaluating the broad patterns of trade-offs between production and environmental risk, as an indicator of the potential for harm, over large scales. Its use can thus inform targeting to improve agricultural sustainability, or where one might switch to other land uses such as ecosystem restoration

Water quality is a poor predictor of recreational hotspots in England

Maintaining and improving water quality is key to the protection and restoration of aquatic ecosystems, which provide important benefits to society. In Europe, the Water Framework Directive (WFD) defines water quality based on a set of biological, hydro-morphological and chemical targets, and aims to reach good quality conditions in all river bodies by the year 2027. While recently it has been argued that achieving these goals will deliver and enhance ecosystem services, in particular recreational services, there is little empirical evidence demonstrating so. Here we test the hypothesis that good water quality is associated with increased utilization of recreational services, combining four surveys covering walking, boating, fishing and swimming visits, together with water quality data for all water bodies in eight River Basin Districts (RBDs) in England. We compared the percentage of visits in areas of good water quality to a set of null models accounting for population density, income, age distribution, travel distance, public access, and substitutability. We expect such association to be positive, at least for fishing (which relies on fish stocks) and swimming (with direct contact to water). We also test if these services have stronger association with water quality relative to boating and walking alongside rivers, canals or lakeshores. In only two of eight RBDs (Northumbria and Anglian) were both criteria met (positive association, strongest for fishing and swimming) when comparing to at least one of the null models. This conclusion is robust to variations in dataset size. Our study suggests that achieving the WFD water quality goals may not enhance recreational ecosystem services, and calls for further empirical research on the connection between water quality and ecosystem services

Machine learning in marine ecology: an overview of techniques and applications

Machine learning covers a large set of algorithms that can be trained to identify patterns in data. Thanks to the increase in the amount of data and computing power available, it has become pervasive across scientific disciplines. We first highlight why machine learning is needed in marine ecology. Then we provide a quick primer on machine learning techniques and vocabulary. We built a database of ∼1000 publications that implement such techniques to analyse marine ecology data. For various data types (images, optical spectra, acoustics, omics, geolocations, biogeochemical profiles, and satellite imagery), we present a historical perspective on applications that proved influential, can serve as templates for new work, or represent the diversity of approaches. Then, we illustrate how machine learning can be used to better understand ecological systems, by combining various sources of marine data. Through this coverage of the literature, we demonstrate an increase in the proportion of marine ecology studies that use machine learning, the pervasiveness of images as a data source, the dominance of machine learning for classification-type problems, and a shift towards deep learning for all data types. This overview is meant to guide researchers who wish to apply machine learning methods to their marine datasets.Machine learning in marine ecology: an overview of techniques and applicationspublishedVersio

Reputation and identity conflict in management consulting

This is the author accepted manuscript. The final version is available from SAGE Publications via the DOI in this record.Based on a case study of a large consulting firm, this paper makes two contributions to the literature on reputation and identity by examining how an organization responds when its identity is substantially misaligned with the experience and perceptions of external stakeholders that form the basis of reputational judgments. First, rather than triggering some form of identity adaptation, it outlines how other forms of identity can come into play to remediate this gap, buffering the organization’s identity from change. This shift to other individual identities is facilitated by a low organizational identity context even when the identity of the firm is coherent and strong. The second contribution concerns the conceptualization of consulting and other professional service firms. We explain how reputation and identity interact in the context of the distinctive organizational features of these firms. Notably, their loosely coupled structure and the central importance of expert knowledge claims enable individual consultants both to reinforce and supplement corporate reputation via individual identity work

The Science Performance of JWST as Characterized in Commissioning

This paper characterizes the actual science performance of the James Webb Space Telescope (JWST), as determined from the six month commissioning period. We summarize the performance of the spacecraft, telescope, science instruments, and ground system, with an emphasis on differences from pre-launch expectations. Commissioning has made clear that JWST is fully capable of achieving the discoveries for which it was built. Moreover, almost across the board, the science performance of JWST is better than expected; in most cases, JWST will go deeper faster than expected. The telescope and instrument suite have demonstrated the sensitivity, stability, image quality, and spectral range that are necessary to transform our understanding of the cosmos through observations spanning from near-earth asteroids to the most distant galaxies.Comment: 5th version as accepted to PASP; 31 pages, 18 figures; https://iopscience.iop.org/article/10.1088/1538-3873/acb29

Machine learning in marine ecology: an overview of techniques and applications

Machine learning covers a large set of algorithms that can be trained to identify patterns in data. Thanks to the increase in the amount of data and computing power available, it has become pervasive across scientific disciplines. We first highlight why machine learning is needed in marine ecology. Then we provide a quick primer on machine learning techniques and vocabulary. We built a database of ∼1000 publications that implement such techniques to analyse marine ecology data. For various data types (images, optical spectra, acoustics, omics, geolocations, biogeochemical profiles, and satellite imagery), we present a historical perspective on applications that proved influential, can serve as templates for new work, or represent the diversity of approaches. Then, we illustrate how machine learning can be used to better understand ecological systems, by combining various sources of marine data. Through this coverage of the literature, we demonstrate an increase in the proportion of marine ecology studies that use machine learning, the pervasiveness of images as a data source, the dominance of machine learning for classification-type problems, and a shift towards deep learning for all data types. This overview is meant to guide researchers who wish to apply machine learning methods to their marine datasets