Centre for Biodiversity and Environment Research, University College London, London, UK.
Institute of Zoology, Zoological Society of London, London, UK.
Glob Chang Biol. 2023 Sep;29(17):4966-4982. doi: 10.1111/gcb.16841. Epub 2023 Jun 27.
Global biodiversity is facing a crisis, which must be solved through effective policies and on-the-ground conservation. But governments, NGOs, and scientists need reliable indicators to guide research, conservation actions, and policy decisions. Developing reliable indicators is challenging because the data underlying those tools is incomplete and biased. For example, the Living Planet Index tracks the changing status of global vertebrate biodiversity, but taxonomic, geographic and temporal gaps and biases are present in the aggregated data used to calculate trends. However, without a basis for real-world comparison, there is no way to directly assess an indicator's accuracy or reliability. Instead, a modelling approach can be used. We developed a model of trend reliability, using simulated datasets as stand-ins for the "real world", degraded samples as stand-ins for indicator datasets (e.g., the Living Planet Database), and a distance measure to quantify reliability by comparing partially sampled to fully sampled trends. The model revealed that the proportion of species represented in the database is not always indicative of trend reliability. Important factors are the number and length of time series, as well as their mean growth rates and variance in their growth rates, both within and between time series. We found that many trends in the Living Planet Index need more data to be considered reliable, particularly trends across the global south. In general, bird trends are the most reliable, while reptile and amphibian trends are most in need of additional data. We simulated three different solutions for reducing data deficiency, and found that collating existing data (where available) is the most efficient way to improve trend reliability, whereas revisiting previously studied populations is a quick and efficient way to improve trend reliability until new long-term studies can be completed and made available.
全球生物多样性正面临危机，必须通过有效的政策和实地保护来解决。但是，政府、非政府组织和科学家需要可靠的指标来指导研究、保护行动和政策决策。开发可靠的指标具有挑战性，因为这些工具所依据的数据不完整且存在偏差。例如，“生命星球指数”（Living Planet Index）跟踪全球脊椎动物生物多样性的变化状况，但在用于计算趋势的汇总数据中存在分类学、地理和时间上的差距和偏差。然而，如果没有现实世界的比较基础，就无法直接评估指标的准确性或可靠性。相反，可以使用建模方法。我们开发了一种趋势可靠性模型，使用模拟数据集作为“现实世界”的替代品，退化样本作为指标数据集（例如“生命星球数据库”）的替代品，以及一种距离度量来通过比较部分采样和完全采样的趋势来量化可靠性。该模型表明，数据库中物种的代表性比例并不总是反映趋势可靠性。重要因素是时间序列的数量和长度，以及它们的平均增长率和增长率变化，无论是在时间序列内部还是之间。我们发现，“生命星球指数”中的许多趋势需要更多的数据才能被认为是可靠的，特别是在全球南方的趋势。一般来说，鸟类趋势是最可靠的，而爬行动物和两栖动物趋势最需要额外的数据。我们模拟了三种不同的解决方案来减少数据不足，发现整理现有数据（在可用的情况下）是提高趋势可靠性最有效的方法，而重新研究以前研究过的种群是在新的长期研究完成并可用之前提高趋势可靠性的快速而有效的方法。
