Biodiversity metrics have become a ubiquitous component of conservation assessments across scales. However, whilst indices have become increasingly widely used, their ability to perform in the face of different biases has remained largely untested under realistic conditions. Citizen science data are increasingly available, but present new challenges and biases, thus understanding how to use them effectively is essential. Here, we built a virtual world incorporating BirdLife data and accounting for their biases, then explored how well commonly-used diversity metrics could estimate known values across a suite of representative scenarios. We used predictive modelling to model bird diversity globally and overcome biases using the approaches found most accurate in prior assessments. Performance was highly variable across the different types of biases, but in many instances Simpson's index performed best, followed by Hill numbers, whereas Pielou's index was almost universally worst. From standardised tests, we then applied these metrics to eBird data using 611 520 112 samples of 10 359 species of bird (around 88% of known species), to reconstruct global diversity patterns at five and ten km resolutions. However, when we mapped out diversity using Maxent based on these indices, Simpson's index generally over-predicted diversity, whereas Hill numbers were more conservative. Based on an average of the better projected indices, one can map out diversity across resolutions and overcome biases accurately predicting diversity patterns even for data-poor areas, but if a single metric is used, Hill numbers are most robust to bias. Going forward, this workflow will enable standardized best practices for diversity mapping based on a clear understanding of the performance of different metrics.$$

中文翻译：

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衡量指标：哪些多样性指标最适合不同形式的数据偏差？


生物多样性指标已成为各种规模的保护评估的普遍组成部分。然而，虽然指数的使用越来越广泛，但它们在面对不同偏差时的表现能力在很大程度上尚未在现实​​条件下得到检验。公民科学数据越来越多，但也带来了新的挑战和偏见，因此了解如何有效地使用它们至关重要。在这里，我们构建了一个包含 BirdLife 数据并考虑其偏差的虚拟世界，然后探讨了常用的多样性指标如何在一系列代表性场景中估计已知值。我们使用预测模型对全球鸟类多样性进行建模，并使用先前评估中最准确的方法克服偏差。不同类型的偏差的表现差异很大，但在许多情况下，辛普森指数表现最好，其次是希尔数字，而 Pielou 指数几乎普遍最差。然后，我们通过标准化测试，将这些指标应用到 eBird 数据中，使用 10 359 种鸟类（约占已知物种的 88%）的 611 520 112 个样本，以 5 公里和 10 公里的分辨率重建全球多样性模式。然而，当我们根据这些指数使用 Maxent 绘制多样性时，辛普森指数通常会高估多样性，而 Hill 数字则更为保守。基于更好的预测指数的平均值，我们可以绘制出不同分辨率的多样性，并克服偏差，即使对于数据匮乏的区域也能准确预测多样性模式，但如果使用单一指标，希尔数对偏差最稳健。 展望未来，该工作流程将基于对不同指标性能的清晰了解，实现多样性映射的标准化最佳实践。 $$