Estimates of biodiversity change are essential for the management and conservation of ecosystems. Accurate estimates rely on selecting representative sites, but monitoring often focuses on sites of special interest. How such site-selection biases influence estimates of biodiversity change is largely unknown. Site-selection bias potentially occurs across four major sources of biodiversity data, decreasing in likelihood from citizen science, museums, national park monitoring, and academic research. We defined site-selection bias as a preference for sites that are either densely populated (i.e., abundance bias) or species rich (i.e. richness bias). We simulated biodiversity change in a virtual landscape and tracked the observed biodiversity at a sampled site. The site was selected either randomly or with a site-selection bias. We used a simple spatially resolved, individual-based model to predict the movement or dispersal of individuals in and out of the chosen sampling site. Site-selection bias exaggerated estimates of biodiversity loss in sites selected with a bias by on average by 300-400% compared with randomly selected sites. Based on our simulations, site-selection bias resulted in positive trends being estimated as negative trends: richness increase was estimated as 0.1 in randomly selected sites, whereas sites selected with a bias showed a richness change of -0.1 to -0.2 on average. Thus, site-selection bias may falsely indicate decreases in biodiversity. We varied sampling design and characteristics of the species and found that site-selection biases were strongest in short time series, for small grains, organisms with low dispersal ability, large regional species pools, and strong spatial aggregation. Based on these findings, to minimize site-selection bias, we recommend use of systematic site-selection schemes; maximize sampling area; calculating biodiversity measures cumulatively across plots; and use of biodiversity measures that are less sensitive to rare species, such as the effective number of species. Awareness of the potential impact of site-selection bias is needed for biodiversity monitoring, the design of new studies on biodiversity change, and the interpretation of existing data. Article impact statement: Nonsubjective site selection is important for accurate estimation of the direction of biodiversity change. This article is protected by copyright. All rights reserved.

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选址偏差对生物多样性变化估计的影响

生物多样性变化的估计对于生态系统的管理和保护至关重要。准确估计依赖于选择有代表性的地点，但监测通常侧重于特别感兴趣的地点。这种选址偏差如何影响生物多样性变化的估计在很大程度上是未知的。选址偏差可能发生在四个主要的生物多样性数据来源中，公民科学、博物馆、国家公园监测和学术研究的可能性降低。我们将站点选择偏差定义为对人口稠密（即丰度偏差）或物种丰富（即丰富度偏差）站点的偏好。我们模拟了虚拟景观中的生物多样性变化，并在采样地点跟踪了观察到的生物多样性。该站点是随机选择的或具有站点选择偏差的。我们使用了一个简单的空间解析、基于个体的模型来预测个体进出所选采样点的移动或分散。与随机选择的地点相比，选址偏差平均夸大了有偏差选址的生物多样性损失估计值 300-400%。根据我们的模拟，站点选择偏差导致正趋势被估计为负趋势：在随机选择的站点中，丰富度增加估计为 0.1，而有偏差选择的站点显示平均丰富度变化为 -0.1 到 -0.2。因此，选址偏差可能错误地表明生物多样性的减少。我们改变了物种的抽样设计和特征，发现选址偏差在短时间序列中最强，对于小颗粒、扩散能力低的生物、区域物种库大，空间聚集性强。基于这些发现，为了尽量减少选址偏差，我们建议使用系统的选址方案；最大化采样面积；跨地块累积计算生物多样性措施；使用对稀有物种不太敏感的生物多样性措施，例如有效物种数量。生物多样性监测、生物多样性变化新研究的设计以及现有数据的解释都需要意识到选址偏差的潜在影响。文章影响说明：非主观选址对于准确估计生物多样性变化的方向很重要。本文受版权保护。版权所有。我们建议使用系统的选址方案；最大化采样面积；跨地块累积计算生物多样性措施；使用对稀有物种不太敏感的生物多样性措施，例如有效物种数量。生物多样性监测、生物多样性变化新研究的设计以及现有数据的解释都需要意识到选址偏差的潜在影响。文章影响说明：非主观选址对于准确估计生物多样性变化的方向很重要。本文受版权保护。版权所有。我们建议使用系统的选址方案；最大化采样面积；跨地块累积计算生物多样性措施；使用对稀有物种不太敏感的生物多样性措施，例如有效物种数量。生物多样性监测、生物多样性变化新研究的设计以及现有数据的解释都需要意识到选址偏差的潜在影响。文章影响说明：非主观选址对于准确估计生物多样性变化的方向很重要。本文受版权保护。版权所有。比如有效物种数。生物多样性监测、生物多样性变化新研究的设计以及现有数据的解释都需要意识到选址偏差的潜在影响。文章影响说明：非主观选址对于准确估计生物多样性变化的方向很重要。本文受版权保护。版权所有。比如有效物种数。生物多样性监测、生物多样性变化新研究的设计以及现有数据的解释都需要意识到选址偏差的潜在影响。文章影响说明：非主观选址对于准确估计生物多样性变化的方向很重要。本文受版权保护。版权所有。