基于含不完美检测的特征分配模型的贝塔多样性贝叶斯推断
Bayesian inference on beta diversity via feature allocation models with imperfect detection
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中文总结 AI 辅助
针对含不完美检测的物种出现数据，提出结合潜在特征分配模型与基于 occupancy 的检测机制的贝叶斯特征分配模型，实现贝塔多样性的有效推断，模拟与真菌数据应用验证其性能提升。
中文摘要 AI 辅助
贝塔多样性量化不同生态群落间物种组成的变异，是理解空间及环境梯度下生物多样性模式的基础。贝塔多样性的统计推断颇具挑战：物种出现数据维度高，大量物种即便经广泛采样仍未被观测到，且调查存在不完美检测问题。现有方法通常基于经验相异指数（不确定性量化有限），或依赖不切实际的可交换性与完美检测假设的模型。我们针对含不完美检测的部分可交换物种出现数据，提出一类新的贝叶斯特征分配模型。该框架将潜在特征分配模型与基于 occupancy 的检测机制相结合，允许不同位点间存在异质物种组成，明确考虑假阴性问题，并兼容先前未观测物种的发现。我们为物种共享与贝塔多样性开发一致的概率推断方法，推导群落间异质性的显式后验与预测分布，包括位点间共享物种数量及未来采样下的预期数量。这些分析结果可解释组成异质性的后验摘要，并助力高维生物多样性研究中的可扩展推断。模拟研究及全球真菌生物多样性数据的应用表明，该方法在物种共享与群落间多样性的推断上表现更优。
英文摘要
Beta diversity quantifies variation in species composition across ecological communities and is fundamental for understanding biodiversity patterns across space and environmental gradients. Statistical inference on beta diversity is challenging: species occurrence data are high dimensional, many species remain unobserved despite extensive sampling, and surveys are subject to imperfect detection. Existing approaches are typically based on empirical dissimilarity indices with limited uncertainty quantification or on models that rely on unrealistic exchangeability and perfect detection assumptions. We introduce a new class of Bayesian feature allocation models for partially exchangeable species occurrence data with imperfect detection. The framework combines latent feature allocation models with occupancy-based detection mechanisms, allowing heterogeneous species compositions across sites, explicitly accounting for false negatives, and accommodating the discovery of previously unobserved species. We develop coherent probabilistic inference for species sharing and beta diversity, deriving explicit posterior and predictive distributions for between-community heterogeneity, including the number of shared species across sites and the number expected under future sampling. These analytical results yield interpretable posterior summaries of compositional heterogeneity and facilitate scalable inference in high-dimensional biodiversity studies. Simulation studies and an application to global fungal biodiversity data demonstrate improved inference on species sharing and between-community diversity.
