1. Sampling efficiency is crucial to overcome the data crisis in biodiversity and to understand what drives the distribution of rare species.
2. Adaptive niche‐based sampling (ANBS) is an iterative sampling strategy that relies on the predictions of species distribution models (SDMs). By predicting highly suitable areas to guide prospection, ANBS could improve the efficiency of sampling effort in terms of finding new locations for rare species. Its iterative quality could potentially mitigate the effect of small and initially biased samples on SDMs.
3. In this study, we compared ANBS with random sampling by assessing the gain in terms of new locations found per unit of effort. The comparison was based on both simulations and two field surveys of mountain birds.
4. We found an increasing probability of contacting the species through iterations if the focal species showed specialization in the environmental gradients used for modelling. We also identified a gain when using pseudo‐absences during first iterations, and a general tendency of ANBS to increase the omission rate in the spatial prediction of the species' niche or habitat.
5. Overall, ANBS is an effective and flexible strategy that can contribute to a better understanding of distribution drivers in rare species.

中文翻译：

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基于生态位的自适应采样可提高发现稀有和难捉物种的能力：模拟和现场测试

1. 采样效率对于克服生物多样性方面的数据危机以及了解什么驱动稀有物种的分布至关重要。
2. 自适应基于生态位的采样（ANBS）是一种迭代采样策略，它依赖于物种分布模型（SDM）的预测。通过预测高度合适的区域来指导勘探，ANBS可以在寻找稀有物种的新地点方面提高采样效率。它的迭代质量可能会减轻小样本和最初有偏差的样本对SDM的影响。
3. 在这项研究中，我们通过评估单位工作量中新位置所获得的收益，将ANBS与随机抽样进行了比较。比较是基于模拟和两次山地鸟类实地调查。
4. 如果焦点物种显示出用于建模的环境梯度方面的专业化，我们发现通过迭代接触物种的可能性增加。我们还确定了在第一次迭代中使用伪缺席时的增益，以及在物种的生态位或生境的空间预测中ANBS普遍会增加遗漏率的趋势。
5. 总体而言，ANBS是一种有效且灵活的策略，有助于更好地了解稀有物种的分布驱动因素。