1. Understanding patterns of diversity is central to ecology and conservation, yet estimates of diversity are often biased by imperfect detection. In recent years, multi‐species occupancy models (MSOM) have been developed as a statistical tool to account for species‐specific heterogeneity in detection while estimating true measures of diversity. Although the power of these models has been tested in various ways, their ability to estimate gamma diversity—or true community size, N is a largely unrecognized feature that needs rigorous evaluation.
2. We use both simulations and an empirical dataset to evaluate the bias, precision, accuracy and coverage of estimates of N from MSOM compared to the widely applied iChao2 non‐parametric estimator. We simulated 5,600 datasets across seven scenarios of varying average occupancy and detectability covariates, as well as varying numbers of sites, replicates and true community size. Additionally, we use a real dataset of surveys over 9 years (where species accumulation reached an asymptote, indicating true N), to estimate N from each annual survey.
3. Simulations showed that both MSOM and iChao2 estimators are generally accurate (i.e. unbiased and precise) except under unideal scenarios where mean species occupancy is low. In such scenarios, MSOM frequently overestimated N. Across all scenarios, MSOM estimates were less certain than iChao2, but this led to over‐confident iChao2 estimates that showed poor coverage. Results from the real dataset largely confirmed the simulation findings, with MSOM estimates showing greater accuracy and coverage than iChao2.
4. Community ecologists have a wide choice of analytical methods, and both iChao2 and MSOM estimates of N are substantially preferable to raw species counts. The simplicity of non‐parametric estimators has obvious advantages, but our results show that in many cases, MSOM may provide superior estimates that also account more accurately for uncertainty. Both methods can show strong bias when average occupancy is very low, and practitioners should show caution when using estimates derived from either method under such conditions.

中文翻译：

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多物种占用模型作为社区丰富度的可靠估计器

1. 理解多样性的模式对于生态和保护至关重要，但是对多样性的估计常常因不完善的检测而产生偏差。近年来，已开发出多物种占用模型（MSOM）作为统计工具，以在检测中评估物种特定异质性的同时估算多样性的真实度量。尽管已通过各种方式测试了这些模型的功能，但它们具有估计伽玛多样性（或真实社区规模）的能力，但N在很大程度上是无法识别的功能，需要进行严格评估。
2. 与广泛使用的iChao2非参数估计器相比，我们使用模拟和经验数据集来评估MSOM中N估计值的偏差，精度，准确性和覆盖范围。我们模拟了7种情况下的5,600个数据集，这些情况具有不同的平均占用率和可检测性协变量，以及不同数量的站点，重复项和真实的社区规模。此外，我们使用9年内的真实调查数据集（物种积累达到渐近线，表示真实N）来估计每个年度调查的N。
3. 仿真表明，MSOM和iChao2估计量通常都是准确的（即无偏且精确），除非在平均物种占有率较低的不理想情况下。在这样的场景，MSOM频繁高估Ñ。在所有情况下，MSOM估计都没有iChao2那么确定，但这导致iChao2估计过分自信，显示覆盖范围很差。实际数据集的结果很大程度上证实了模拟结果，MSOM估计值显示出比iChao2更高的准确性和覆盖范围。
4. 社区生态学家可以选择多种分析方法，iChao2和MSOM对N的估算都大大优于原始物种数。非参数估计器的简单性具有明显的优势，但是我们的结果表明，在许多情况下，MSOM可能会提供更好的估计，这些估计也可以更准确地说明不确定性。当平均占用率非常低时，这两种方法都可能显示出强烈的偏差，在这种情况下，使用从这两种方法得出的估计值时，从业人员都应谨慎行事。