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

非概率样本的生物多样性指标：科学和社会的跨学科学习

• 批准号: NE/X00967X/1
• 负责人: Gavin Stewart
• 金额: $ 0.92万
• 依托单位: Newcastle University
• 依托单位国家: 英国
• 项目类别: Research Grant
• 财政年份: 2022
• 资助国家: 英国
• 起止时间: 2022 至 无数据
• 项目状态: 已结题
• 来源: https://gtr.ukri.org/projects?ref=NE%2FX00967X%2F1
• 关键词: Biodiversity; indicators; nonprobability; samples; Interdisciplinary

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.

了解全球生物多样性危机需要定期监测和报告。为此，科学家综合使用了生物多样性数据和统计方法。然而，生物多样性数据往往不是具有代表性的现实样本。其他研究领域多年来一直在处理类似的问题，例如当政治学家试图根据没有代表性的民意调查预测选举结果时。说明此类证据质量问题是在生态学中成熟使用“大数据”的关键部分，特别是在越来越多的研究成果被要求评估国际目标(例如，与《生物多样性公约》有关的目标)和国家政府政策的情况下。例如，即将出台的英国环境法正计划使用生态指标来设定和评估与环境状况有关的目标的进展情况。虽然这些指标长期以来一直被用作向政府提供信息的“官方统计数据”，但这种与立法的直接联系是新的。考虑到这种使用可能带来的所有后续决定(例如，为保护提供资金)，对我们的环境进行准确的评估，包括对非代表性样本的调整，显然是至关重要的。与此同时，数字通信和IT的发展创造了前所未有的机会来可视化和传播数据中的模式。即使在最近的过去，新冠大流行也增加了向公众提供图表和数据的速度。与此同时，公众对环境的兴趣也在稳步增长，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和向公众展示“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一部分健康的数量趋势是这一趋势的重要组成部分，并定期发布、宣传和广泛分享。这种趋势经常被用作“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，这是一个具有长期生态学研究历史的地区。传达围绕这些指标的不确定性是让公众了解科学家关于生物多样性变化的真实情况的基本部分。然而，通常不会考虑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是基于业余自然主义者的活动，虽然这些活动往往质量很高，但往往不是随机抽样的结果。在全球范围内，数据是高度不同的，即使是专业的监测数据在这种规模上也变得不具代表性(即没有地球生物多样性的总体随机样本)。然而，对物种分布或丰度的时间趋势的稳健估计需要有代表性的数据。这归根结底是一个统计学问题，对所有希望从样本中了解现实的科学来说都是共同的。随机样本是强大统计推断的核心，因此偏离这一条件应该会让我们停下来思考。幸运的是，统计学家们花了很多精力来考虑如何使非随机样本更可靠，并为此目的收集了来自其他研究领域的丰富建议和技术方法。我们的项目将研究这套技术，以突出如何改善支撑我们对当前生物多样性危机的知识的生态证据基础，以及如何将这种不确定性准确和明确地传达给政策制定者和公众。