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

了解全球生物多样性危机需要定期监测和报告。科学家为此目的综合使用生物多样性数据和统计方法。然而，生物多样性数据往往不能代表现实。其他研究领域多年来一直在处理类似的问题，例如政治学家试图从不具代表性的民意调查中预测选举结果。考虑到这种证据质量问题是生态学中“大数据”使用成熟的一个重要组成部分，特别是因为越来越多地要求研究成果用于评价国际目标（例如与《生物多样性公约》有关的目标）和国家政府政策。例如，即将出台的《联合王国环境法》计划使用生态指标来制定与环境状况有关的目标，并评估实现这些目标的进展情况。虽然这些指标长期以来一直被用作“官方统计数据”，向政府提供信息，但这种与立法的直接联系是新的。考虑到这种使用可能需要做出的所有后续决定（例如为保护提供资金），对我们的环境进行准确评估，包括对不具代表性的采样进行调整，显然是必不可少的。与此同时，数字通信和信息技术的发展创造了前所未有的可视化和传播数据模式的机会。即使在最近的过去，COVID大流行也增加了向公众展示图表和数据的频率。与此同时，公众对环境的兴趣稳步增长，生物多样性和生态系统服务政府间科学政策平台（IPBES）和环境慈善机构等组织现在热衷于总结并向公众展示“自然状态”，以加强他们对生态问题的理解。被认为表明我们环境某些部分健康状况的数量趋势是其中的重要组成部分，并定期发布，推广和广泛分享。这些趋势通常被用作“生态指标”，即直接表明我们希望管理或简单了解的环境变化的数字，这是一个具有悠久生态学研究历史的领域。就这些指标的不确定性进行沟通是让公众了解科学家对生物多样性变化的了解的真实状况的一个基本组成部分。然而，经常没有考虑到的是用于创建此类统计数据的证据的质量。在英国，大多数生物多样性指标都是基于业余博物学家的活动，虽然质量通常很高，但并不经常是随机抽样的结果。在全球范围内，数据是高度异质性的，即使是专业的监测数据在这种规模下也变得不具有代表性（即没有地球生物多样性的总体随机样本）。然而，物种分布或丰度的时间趋势的稳健估计需要代表性数据。这最终是一个统计问题，是所有希望从样本中理解现实的科学所共有的。随机样本是强有力的统计推断的核心，因此偏离这一条件应该让我们停下来思考。幸运的是，统计学家们已经投入了大量的精力来考虑如何使非随机样本更加可靠，并且可以从其他研究领域获得丰富的建议和技术方法。我们的项目将研究这套技术，以突出如何加强我们对当前生物多样性危机的认识的生态证据基础，以及如何将这种不确定性准确、清晰地传达给政策制定者和公众。

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.