Biodiversity indicators from nonprobability samples: Interdisciplinary learning for science and society

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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.