Using species traits to understand the mechanisms driving pollination and pest control ecosystem services

Modern intensive agricultural practices characteristic of Western Europe and North America, such as high usage of agro-chemicals, are cited as key drivers of biodiversity declines. Declines in biodiversity are likely to impact on a number of natural processes termed ‘ecosystem services’, which include pollination and pest control that play an important role in agricultural production. Because of the negative effects of intensive agricultural practices, there has been a search for alternative systems of production. One approach is ecological intensification, where ecosystem services are maximised in agriculture as a way to offset anthropogenic inputs that can damage the wider environment. Key to the success of ecological intensification is gaining a mechanistic understanding of how biodiversity supports the functioning of ecosystem services, so management can be targeted to maximise service delivery. In order to ensure that food production is sustainable in the face of constantly changing environments it is also important to understand how biodiversity responds to stressors, such as insecticide use. This thesis focuses on using invertebrate species morphological and behavioural characteristics—referred to collectively as traits—to gain a mechanistic understanding of how different components of biodiversity support the functioning and resilience of pollination and pest control ecosystem services. Results highlight that trait approaches provide higher accuracy in predicting the functioning and resilience of natural pest control and pollination, than measures such as species richness. I also highlight that common environmental stressors such as insecticides and extreme heat have the potential to limit pest control and pollination ecosystem services, respectively. My results broadly demonstrate that utilising invertebrate species behavioural and morphological traits are beneficial in understanding the mechanisms driving pollination and pest control ecosystem services

EDITORIAL

Editorial for the journa

Inala traditions: People, places and history in urban Indigenous communities

Traditions in the contemporary urban Indigenous communities of Brisbane are examined to establish their precedents, meanings and innovation. The paper is based on current fieldwork in Inala, an outer suburb of Brisbane, the state capital of Queensland, Australia. Traditions of place ownership, kinship and belonging, social identity and gathering are examined revealing new and diverse ways of expressing cultural values, which had been disrupted by colonization. The research finds that despite innovation in traditions, many core values from the classical pre-invasion period are retained, and ‘invention’ of tradition is in fact a sedimentation onto existing cultural values and systems, rather than culture loss or degradation

Factors affecting UK physically disabled children and young people participating in mainstream out-of-school activities: the children, young people and family perspective

Aims of study: To ascertain what out-of-school activities C&YP currently engage in and to determine the facilitators and barriers to participatio

Factors affecting UK physically disabled children and young people participating in mainstream out-of-school activities: Focus on personal care and training

Aims of study: To ascertain what out-of-school activities C&YP currently engage in and to determine the facilitators and barriers to participatio

Monkey’s Guide to Healthy Living and NHS Services An evaluation of the implementation of resources designed to support the learning of primary school aged children in England

A team of researchers from the Faculty of Education, Health and Community recently carried out a national evaluation project on behalf of the NHS Institute for Innovation and Improvement. Colleagues from across the faculty were involved in evaluating the impact of resources provided to every primary school in England. The resources were designed to enable teachers and health professionals to work together to help children learn about the NHS and the range of services they could access if they required acute of emergency care

Analysis and monitoring of single HaCaT cells using volumetric Raman mapping and machine learning

No explorer reached a pole without a map, no chef served a meal without tasting, and no surgeon implants untested devices. Higher accuracy maps, more sensitive taste buds, and more rigorous tests increase confidence in positive outcomes. Biomedical manufacturing necessitates rigour, whether developing drugs or creating bioengineered tissues [1]–[4]. By designing a dynamic environment that supports mammalian cells during experiments within a Raman spectroscope, this project provides a platform that more closely replicates in vivo conditions. The platform also adds the opportunity to automate the adaptation of the cell culture environment, alongside spectral monitoring of cells with machine learning and three-dimensional Raman mapping, called volumetric Raman mapping (VRM). Previous research highlighted key areas for refinement, like a structured approach for shading Raman maps [5], [6], and the collection of VRM [7]. Refining VRM shading and collection was the initial focus, k-means directed shading for vibrational spectroscopy map shading was developed in Chapter 3 and exploration of depth distortion and VRM calibration (Chapter 4). “Cage” scaffolds, designed using the findings from Chapter 4 were then utilised to influence cell behaviour by varying the number of cage beams to change the scaffold porosity. Altering the porosity facilitated spectroscopy investigation into previously observed changes in cell biology alteration in response to porous scaffolds [8]. VRM visualised changed single human keratinocyte (HaCaT) cell morphology, providing a complementary technique for machine learning classification. Increased technical rigour justified progression onto in-situ flow chamber for Raman spectroscopy development in Chapter 6, using a Psoriasis (dithranol-HaCaT) model on unfixed cells. K-means-directed shading and principal component analysis (PCA) revealed HaCaT cell adaptations aligning with previous publications [5] and earlier thesis sections. The k-means-directed Raman maps and PCA score plots verified the drug-supplying capacity of the flow chamber, justifying future investigation into VRM and machine learning for monitoring single cells within the flow chamber

Applications of machine learning in spectroscopy

The way to analyze data in spectroscopy has changed substantially. At the same time, data science has evolved to the point where spectroscopy can find space to be housed, adapted and be functional. The integration of the two sciences has introduced a knowledge gap between data scientists who know about advanced machine learning techniques and spectroscopists who have a solid background in chemometrics. To reach a symbiosis, the knowledge gap requires bridging. This review article focuses on introducing data science subjects to non-specialist spectroscopists, or those unfamiliar with the subject. The article will explain concepts that are covered in machine learning, such as supervised learning, unsupervised learning, deep learning, and most importantly, the difference between machine learning and artificial intelligence. This article also includes examples of published spectroscopy research, in which some of the concepts explained here are applied. Machine learning together with spectroscopy can provide a useful, fast, and efficient tool to analyze samples of interest both for industrial and research purposes. © 2020 Taylor & Francis Group, LLC