PDF(Pubmed)
Abstract：
Ecologists have historically quantified fundamental biodiversity patterns, including species-area relationships (SARs) and beta diversity, using observed species counts. However, imperfect detection may often bias derived community metrics and subsequent community models. Although several statistical methods claim to correct for imperfect detection, their performance in species-area and β-diversity research remains unproven. We examine inaccuracies in the estimation of SARs and β-diversity parameters that emerge from imperfect detection, and whether such errors can be mitigated using a non-parametric diversity estimator (iNEXT.3D) and Multi-Species Occupancy Models (MSOMs). We simulated 28,350 sampling regimes of 2835 fragmented communities, varying the mean and standard deviation of species detection probabilities, and the number of sampling repetitions. We then quantified the bias, accuracy, and precision of derived estimates of model coefficients for SARs and the effects of patch area on β-diversity (pairwise Sørensen similarity). Imperfect detection biased estimates of all evaluated parameters, particularly when mean detection probabilities were low, and there were few sampling repetitions. Observed counts consistently underestimated species richness and SAR z-values, and overestimated SAR c-values; iNEXT.3D and MSOMs only partially resolved these biases. iNEXT.3D provided the best estimates of SAR z-values, although MSOM estimates were generally comparable. All three methods accurately estimated pairwise Sørensen similarity in most circumstances, but only MSOMs provided unbiased estimates of the coefficients of models examining covariate effects on β-diversity. Even when using iNEXT.3D or MSOMs, imperfect detection consistently caused biases in SAR coefficient estimates, calling into question the robustness of previous SAR studies. Furthermore, the inability of observed counts and iNEXT.3D to estimate β-diversity model coefficients resulted from a systematic, area-related bias in Sørensen similarity estimates. Importantly, MSOMs corrected for these biases in β-diversity assessments, even in suboptimal scenarios. Nonetheless, as estimator performance consistently improved with increasing sampling repetitions, the importance of appropriate sampling effort cannot be understated.
摘要：
生态学家在历史上量化了基本的生物多样性模式，包括物种-区域关系（SAR）和β多样性，使用观察到的物种计数。然而,不完美的检测通常可能会偏差导出的社区指标和随后的社区模型。尽管几种统计方法声称可以纠正不完美的检测，它们在物种面积和β多样性研究中的表现仍未得到证实。我们检查了不完美检测中出现的SAR和β多样性参数估计的不准确性，以及是否可以使用非参数多样性估计器（iNEXT.3D）和多物种占用模型（MSOM）来减轻此类错误。我们模拟了2835个支离破碎的社区的28350个抽样制度，改变物种检测概率的平均值和标准偏差，以及采样重复次数。然后我们量化了偏差，准确度,SARs模型系数的推导估计值的精度以及斑块面积对β多样性的影响（成对Sørensen相似性）。所有评估参数的不完美检测有偏估计，特别是当平均检测概率较低时，几乎没有采样重复。观察到的计数始终低估了物种丰富度和SARz值，和高估的SARc值；iNEXT.3D和MSOM仅部分解决了这些偏差。iNEXT.3D提供了SARz值的最佳估计，尽管MSOM估计总体上具有可比性。在大多数情况下，这三种方法都准确地估计了成对Sørensen相似性，但是只有MSOM提供了检查协变量对β多样性影响的模型系数的无偏估计。即使使用iNEXT.3D或MSOM，不完美的检测始终导致SAR系数估计的偏差，质疑以往SAR研究的稳健性。此外，观测计数和iNEXT.3D无法估计β多样性模型系数是由于系统的，Sørensen相似性估计中的区域相关偏差。重要的是,在β多样性评估中纠正了这些偏差的MSOM，即使在次优的情况下。尽管如此，随着采样重复次数的增加，估计器性能不断提高，适当抽样工作的重要性不可低估。
