1. Studies of spatial point patterns (SPPs) are often used to examine the role that density-dependence (DD) and environmental filtering (EF) play in community assembly and species coexistence in forest communities. However, SPP analyses often struggle to distinguish the opposing effects that DD and EF may have on the distribution of tree species.
2. We tested percolation threshold analysis on simulated tree communities as a method to distinguish the importance of thinning from DD EF on SPPs. We then compared the performance of percolation threshold analysis results and a Gibbs point process model in detecting environmental associations as well as clustering patterns or overdispersion. Finally, we applied percolation threshold analysis and the Gibbs point process model to observed SPPs of 12 dominant tree species in a Puerto Rican forest to detect evidence of DD and EF.
3. Percolation threshold analysis using simulated SPPs detected a decrease in clustering due to DD and an increase in clustering from EF. In contrast, the Gibbs point process model clearly detected the effects of EF but only identified DD thinning in two of the four types of simulated SPPs. Percolation threshold analysis on the 12 observed tree species' SPPs found that the SPPs for two species were consistent with thinning from DD processes only, four species had SPPs consistent with EF only and SPP for five reflected a combination of both processes. Gibbs models of observed SPPs of living trees detected significant environmental associations for 11 species and clustering consistent with DD processes for seven species.
4. Percolation threshold analysis is a robust method for detecting community assembly processes in simulated SPPs. By applying percolation threshold analysis to natural communities, we found that tree SPPs were consistent with thinning from both DD and EF. Percolation threshold analysis was better suited to detect DD thinning than Gibbs models for clustered simulated communities. Percolation threshold analysis improves our understanding of forest community assembly processes by quantifying the relative importance of DD and EF in forest communities.

中文翻译：

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渗透阈值分析可以检测模拟和天然树木群落中的群落组装过程

1. 空间点模式 (SPP) 的研究通常用于检查密度依赖性 (DD) 和环境过滤 (EF) 在森林群落中的群落组装和物种共存中的作用。然而，SPP 分析往往难以区分 DD 和 EF 可能对树种分布产生的相反影响。
2. 我们测试了对模拟树木群落的渗透阈值分析，作为区分 SPP 上稀疏与 DD EF 重要性的方法。然后，我们比较了渗透阈值分析结果和吉布斯点过程模型在检测环境关联以及聚类模式或过度分散方面的性能。最后，我们应用渗透阈值分析和吉布斯点过程模型观察波多黎各森林中 12 种优势树种的 SPP，以检测 DD 和 EF 的证据。
3. 使用模拟 SPP 的渗透阈值分析检测到由于 DD 导致的聚类减少和来自 EF 的聚类增加。相比之下，吉布斯点过程模型清楚地检测到 EF 的影响，但仅在四种模拟 SPP 中的两种中识别出 DD 变薄。对 12 种观察树种的 SPP 进行渗透阈值分析发现，两种树种的 SPP 仅与 DD 过程的间伐一致，4 种树种的 SPP 仅与 EF 一致，而 5 种的 SPP 反映了两种过程的组合。观察到的活树 SPP 的 Gibbs 模型检测到 11 个物种的显着环境关联，并且聚类与 7 个物种的 DD 过程一致。
4. 渗透阈值分析是一种用于检测模拟 SPP 中社区组装过程的稳健方法。通过对自然群落应用渗透阈值分析，我们发现树木 SPP 与 DD 和 EF 的细化一致。对于集群模拟社区，渗透阈值分析比 Gibbs 模型更适合检测 DD 稀疏。渗透阈值分析通过量化 DD 和 EF 在森林群落中的相对重要性，提高了我们对森林群落组装过程的理解。