1. Stacked species distribution models (S‐SDM) provide a tool to make spatial predictions about communities by first modelling individual species and then stacking the modelled predictions to form assemblages. The evaluation of the predictive performance is usually based on a comparison of the observed and predicted community properties (e.g. species richness, composition). However, the most available and widely used evaluation metrics require the thresholding of single species' predicted probabilities of occurrence to obtain binary outcomes (i.e. presence/absence). This binarization can introduce unnecessary bias and error.
2. Herein, we present and demonstrate the use of several groups of new or rarely used evaluation approaches and metrics for both species richness and community composition that do not require thresholding but instead directly compare the predicted probabilities of occurrences of species to the presence/absence observations in the assemblages.
3. Community AUC, which is based on traditional AUC, measures the ability of a model to differentiate between species presences or absences at a given site according to their predicted probabilities of occurrence. Summing the probabilities gives the expected species richness and allows the estimation of the probability that the observed species richness is not different from the expected species richness based on the species' probabilities of occurrence. The traditional Sørensen and Jaccard similarity indices (which are based on presences/absences) were adapted to maxSørensen and maxJaccard and to probSørensen and probJaccard (which use probabilities directly). A further approach (improvement over null models) compares the predictions based on S‐SDMs with the expectations from the null models to estimate the improvement in both species richness and composition predictions. Additionally, all metrics can be described against the environmental conditions of sites (e.g. elevation) to highlight the abilities of models to detect the variation in the strength of the community assembly processes in different environments.
4. These metrics offer an unbiased view of the performance of community predictions compared to metrics that requiring thresholding. As such, they allow more straightforward comparisons of model performance among studies (i.e. they are not influenced by any subjective thresholding decisions).

中文翻译：

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如何在没有阈值的情况下评估社区的预测？

1. 堆叠物种分布模型（S-SDM）提供了一种工具，可通过对单个物种进行建模然后对建模的预测进行堆叠以形成组合来对社区进行空间预测。预测性能的评估通常基于对观察到的和预测的群落特性（例如物种丰富度，组成）的比较。但是，最可用和使用最广泛的评估指标要求对单个物种的预测发生概率进行阈值处理以获得二元结果（即存在/不存在）。这种二值化会引入不必要的偏差和误差。
2. 在这里，我们介绍并证明了使用几组新的或很少使用的评估方法和指标来进行物种丰富度和群落组成，不需要阈值，而是直接将预测的物种发生概率与物种中存在/不存在的观察结果进行比较。的集合。
3. 基于传统AUC的Community AUC（社区AUC）测量模型根据其预测的发生概率在给定位置区分物种存在与否的能力。对概率求和可以得出预期的物种丰富度，并可以根据物种的发生概率估算观察到的物种丰富度与预期物种丰富度没有差异的概率。传统的Sørensen和Jaccard相似性指数（基于存在/不存在）适用于maxSørensen和maxJaccard以及probSørensen和probJaccard（直接使用概率）。进一步的方法（相对于零模型的改进）将基于S‐SDM的预测与零模型的期望进行比较，以估算物种丰富度和成分预测的改进。另外，可以针对场所的环境条件（例如海拔）描述所有度量标准，以突出显示模型检测不同环境中社区组装过程强度变化的能力。
4. 与需要阈值的指标相比，这些指标提供了社区预测性能的公正视图。因此，它们允许在研究之间对模型性能进行更直接的比较（即，它们不受任何主观阈值决策的影响）。