Department of Forest Vegetation, Forestry and Forest Products Research Institute, 1 Matsunosato, Tsukuba, 305-8687, Japan.
Fenner School of Environment and Society, Australian National University, Canberra, Australian Capital Territory, 2601, Australia.
Ecology. 2019 Aug;100(8):e02759. doi: 10.1002/ecy.2759. Epub 2019 Jun 20.
Recently developing hierarchical community models (HCMs) accounting for incomplete sampling are promising approaches to understand community organization. However, pros and cons of incorporating incomplete sampling in the analysis and related design issues remain unknown. In this study, we compared HCM and canonical redundancy analysis (RDA) carried out with 10 different dissimilarity coefficients to evaluate how each approach restores true community abundance data sampled with imperfect detection. We conducted simulation experiments with varying numbers of sampling sites, visits, mean detectability and mean abundance. Performance of HCM was measured by estimates of "expected" (mean) abundance ( ) and realized abundance ( : direct estimate of site- and species-specific abundance). We also compared HCM and different types of RDA (normal, partial, and weighted), all performed with the same ten different dissimilarity coefficients, with unequal number of visits to sampling sites. In addition, we applied the models to a virtual survey carried out on the Barro Colorado Island tree plot data for which we know true community abundance. Simulation experiments showed that yielded by HCM best restored the underlying abundance of constituent species among 12 abundance estimates by HCM and RDA regardless if the sampling was equal or unequal. Mean abundance predominantly affected the performance of HCM and RDA while yielded by HCM had comparable performance to percentage difference and Gower dissimilarity coefficients of RDA. Relative performance of RDA types depended on the combination of dissimilarity coefficients and the distribution of sampling effort. Best performance of followed by , percentage difference and Gower dissimilarity were also observed for the analysis of tree plot data, and graphical plots (triplots) based on rather than clearly separated the effects of two environmental covariates on the abundance of constituent species. Under our conditions of model evaluation and the method, we concluded that, in terms of assessing the environmental dependence of abundance, HCMs and RDA can have comparable performance if we can choose appropriate dissimilarity coefficients for RDA. However, since HCMs provide straightforward biological interpretations of parameter estimates and flexibility of the analysis, HCMs would be useful in many situations as well as conventional canonical ordinations.
最近发展的层次社区模型（HCM）考虑了不完全采样，是理解社区组织的有前途的方法。然而，在分析中纳入不完全采样的优缺点以及相关的设计问题仍然未知。在这项研究中，我们比较了 HCM 和典型冗余分析（RDA），使用了 10 种不同的不相似系数，以评估每种方法如何恢复使用不完全检测采样的真实社区丰度数据。我们进行了模拟实验，实验中采样点、访问次数、平均可检测性和平均丰度的数量各不相同。HCM 的性能通过“预期”（平均值）丰度（ ）和实现丰度（ ：直接估计站点和物种特异性丰度）的估计来衡量。我们还比较了 HCM 和不同类型的 RDA（正常、部分和加权），所有这些都使用了相同的 10 种不相似系数，对采样点的访问次数不相等。此外，我们将这些模型应用于在巴罗克科罗拉多岛树木样地数据上进行的虚拟调查，我们知道真实的社区丰度。模拟实验表明，在 HCM 和 RDA 的 12 种丰度估计中，HCM 产生的 最好地恢复了组成物种的基础丰度，无论采样是否相等。平均丰度主要影响 HCM 和 RDA 的性能，而 HCM 产生的 与 RDA 的百分比差异和戈氏不相似系数具有可比的性能。RDA 类型的相对性能取决于不相似系数的组合和采样努力的分布。在分析树木样地数据时，也观察到 、 、百分比差异和戈氏不相似系数的最佳性能，并且基于 而不是 的图形图（三角图）清楚地分离了两个环境协变量对组成物种丰度的影响。在我们的模型评估和方法条件下，我们得出结论，在评估丰度的环境依赖性方面，如果我们可以为 RDA 选择适当的不相似系数，那么 HCM 和 RDA 可以具有可比的性能。然而，由于 HCM 提供了参数估计的直接生物学解释以及分析的灵活性，因此在许多情况下，HCM 与传统的典型排序一样有用。
