Understanding the global biodiversity crisis requires regular monitoring and reporting. Scientists use a combination of biodiversity data and statistical methods for this purpose. Biodiversity data, however, are not often representative samples of reality. Other research areas have been dealing with similar issues for many years, such as when political scientists try to predict election outcomes from unrepresentative public polling. Accounting for such evidence quality issues is an essential part of the maturation of the use of "big data" in ecology, particularly as research outputs are increasingly being called upon to evaluate both international targets (e.g. those linked to the Convention on Biological Diversity) and national government policies. For example, the forthcoming UK Environment Act is planning to use ecological indicators to both set, and evaluate progress towards, targets relating to the state of the environment. Whilst such indicators have long been used as "official statistics" to inform government, this direct link to legislation is new. Given all the subsequent decisions that this usage might entail (e.g. funding for conservation), accurate appraisals of our environment, including adjustments for unrepresentative sampling, are clearly essential. At the same time, the growth of digital communication and IT has created opportunities to visualise and disseminate patterns in data like never before. Even within the recent past the COVID pandemic has increased the rate at which the public are presented with charts and data. Parallel to this, there has been a steady growth in public interest in the environment, with organisations such as the Intergovernmental Science-Policy Platform on Biodiversity and Ecosystem Services (IPBES) and environmental charities now keen to summarise and present the "state of nature" to the public to bolster their understanding of ecological issues. Trends in quantities that are considered to indicate the health of some part of our environment are a significant part of this, and are regularly published, promoted, and extensively shared. Such trends are often used as "ecological indicators", i.e. numbers that directly indicate some change in our environment that we wish to manage or simply understand, an area with a long history of research in ecology. Communicating uncertainty around such metrics is a fundamental part of keeping the public informed about the true state of scientists' knowledge about biodiversity change. What is not often considered, however, is the quality of the evidence used to create such statistics. In the UK, most biodiversity indicators are based on amateur naturalist activity, which, whilst frequently of very high quality, is not often the result of random sampling. Globally, data are highly heterogeneous, and even professional monitoring data become unrepresentative at this scale (i.e. there is no overall random sample of earth's biodiversity). However, the robust estimation of time trends in species' distributions or abundances requires representative data. This is ultimately a statistical problem, common to all sciences that wish to understand reality from samples. Random samples are at the heart of strong statistical inference, and so departures from this condition should give us pause for thought. Luckily, statisticians have put much effort into considering how nonrandom samples can be made more reliable, and a rich collection of advice and technical methods from other research areas is available to this end. Our project will investigate this set of techniques to highlight ways in which the ecological evidence base underpinning our knowledge of the current biodiversity crisis can be improved, and how this uncertainty can be accurately and clearly communicated to policymakers and the public.

The aim of this fellowship programme is to design a socially responsible collective governance for Smart City commons: shared pool of urban resources (transport, parking space, energy) managed and regulated digitally. Smart City commons exhibit unprecedented complexity and uncertainties: transport systems integrate electric, shared and autonomous vehicles, while distributed energy resources highly penetrate energy systems. How can we manage Smart City commons in a sustainable and socially responsible way to tackle long-standing problems such as traffic jams, overcrowded parking spaces or blackouts? Failing to digitally coordinate collective decisions promptly and at large-scale has tremendous economic, social and environmental impact. Coordinated decisions require a digital (r)evolution, a new paradigm on where we decide, how we decide and what we decide. But which are limiting factors? 1.Online decision-making often disconnects citizens from the physical urban space for which decisions are made: choices are less informed and vulnerable to social media misinformation, while decision outcomes may show lower legitimation. What if collective choices could be made more locally as digital geolocated testimonies, creating opportunities for community interactions and deliberation? 2.Voting system design is another origin of poor collective decisions, with majority voting often failing to achieve consensus or fair and legitimate outcomes. What if we expanded the design space of voting systems with alternative voting methods, e.g. preferential, to encompass social values? While such methods have so far been costly and limited to low-cognitive exercises, negating their social value over majority voting, decision-support systems based on artificial intelligence (AI) emerge as game-changer. 3.With an immense computational and communication complexity, large-scale coordination of inter-dependent collective decisions remains a timely grand challenge. What if coordination could be digitally assisted and emerge as a result of smart aggregate information exchange, achieving privacy and efficiency? To address these challenges, I will combine Internet of Things, human-centred AI and blockchain technology with social choice theory and mechanism design. Using IoT devices, urban points of interest can be turned into digital voting centres within which conditions for a more informed decision-making will be verified in the blockchain, e.g. proving citizens' location. A novel ontology of voting features will provide the basis to predict voting methods that generate fair and legitimate outcomes. Using collective and active reinforcement learning techniques on the blockchain, human and machine collective intelligence will be combined to achieve a trustworthy coordination of collective decisions at large scale. In collaboration with high-profile partners from government/industry, I will demonstrate the applicability of these approaches via 4 innovative impact cases. 1.Using the developed solutions, citizens will geolocate problems and vote for transport planning solutions. 2.They will also vote on spot to implement participatory budgeting projects. 3.A smart parking system will be enhanced with load-balancing capabilities to alleviate crowded and polluted city centres. 4.Via citizens' coordination of transport modality, an urban traffic control system will be optimized for an equitable shift to public/sharing transport, while preserving low-carbon transport zones. These Smart City blueprints will open up new avenues for deeper understanding of digitally assisted collective governance. To master this inter-disciplinary research area and develop myself into a future leader, I will visit world-class leaders and, together with my team, enrol in novel training activities. Two esteemed mentors and an advisory board will further support me. I will engage with the broader community of citizens and policy-makers by organizing workshops and hackathons.