Alec’s fellowship project has two key stands: 1. Finding ways to assess different forms of evidence (e.g., the scientific literature and indigenous and local community knowledge) to help improve evidence-informed decision-making; and 2. Developing new approaches to assessing the reliability and relevance of evidence to different decision-making contexts. Alec’s work helps to ensure that in the future, evidence-informed decision-making combines diverse sources of knowledge for decision-making, whilst making sure to assess the reliability and relevance of each piece of evidence so we can provide tailored recommendations to decision-makers based on their local context. Alec is also particularly interested in how the effectiveness of conservation interventions varies geographically, taxonomically, and socioeconomically, and whether we can predict this – i.e., how can we predict whether a conservation intervention is likely to work in a given local context?
A key and rewarding part of his work is co-designing and developing online tools to help practitioners in the field to determine the best conservation interventions for them to use in their local patch for a given issue using a structured evidence-based process. This has involved a fruitful collaboration with over a dozen different conservation organisations in the UK and abroad called ‘Evidence Champions’ that promote and deliver evidence-based conservation. Alec is continuing to work to create decision support and evidence assessment tools that combine different forms of evidence and knowledge to make better evidence-based decisions in conservation. He works within the Conservation Evidence project and was recently part of the team that won the Vice-Chancellor’s Award for Research Impact and Engagement 2023 for their work on transforming conservation through promoting and facilitating evidence-based practice and decision-making.
A newer element of Alec’s research focuses on Artificial Intelligence and whether machine learning can help to accelerate the evidence synthesis pipeline, translating scientific evidence into useful recommendations for practice and policy more quickly and rigorously. He is also working on applying AI to invasive species surveillance to see whether Open Source Intelligence can help us better stop biological invasions before they become too difficult to control. Overall, Alec’s work is tied together by the unifying theme of applying the patchy global evidence base to inform more effective, local conservation actions.
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Dementia is a topic of considerable public interest. How empirical evidence has contributed to this societal awareness and indeed fear will be covered in this talk. It will span research from the 1980s when not much was understood about dementia up to contemporary perspectives. The focus will be on the epidemiological and public health evidence base, and how this relates to the results published from clinical and lab based research. The findings from UK and other high income countries of reduced age specific prevalence (%) will be explored, and the implications of results from brain based studies that dementia is not inevitable in the presence of ‘alzheimer’ type changes. The role of inequalities, risk varying across countries and time and our knowledge about protective factors have strengthened during recent years, and the balance of high risk with whole population approaches to reducing risk for society will be considered.
One of the many outstanding achievements of G I Taylor was the discovery of relatively simple statistical laws that apply to highly complex turbulent flows. The emergence of simple laws from complexity is well known in other branches of physics, for example the emergence of the laws of heat conduction from molecular dynamics. Complexity can also arise at large scales, and the structural vibration of an aircraft or a car can be a surprisingly difficult phenomenon to analyse, partly because millions of degrees of freedom may be involved, and partly because the vibration can be extremely sensitive to small changes or imperfections in the system. In this talk it is shown that the prediction of vibration levels can be much simplified by the derivation and exploitation of emergent laws, analogous to some extent to the heat conduction equations, but with an added statistical aspect, as in turbulent flow. The emergent laws are discussed and their application to the design of aerospace, marine, and automotive structures is described. As an aside it will be shown that the same emergent theory can be applied to a range of problems involving electromagnetic fields.
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