Department of Bioinformatics, College of Life Sciences, Zhejiang University, Hangzhou, 310058, China.
Sci Rep. 2022 Sep 23;12(1):15917. doi: 10.1038/s41598-022-16485-9.
Characterizing tree spatial patterns and interactions are helpful to reveal underlying processes assembling forest communities. Spatial networks, despite their complexity, are powerful to examine spatial interactions at an individual level using well-defined patterns. However, complex forestation networks introduce uncertainties. Validation methods are needed to assess whether network-based metrics can identify different processes. Here, we constructed three types of networks, which reflect various aspects of tree competition. Based on five spatial null models and 199 Monte-Carlo simulations, we were able to select network-based metrics that exhibited well performance in distinguishing different processes. This technique was then applied to a tropical forest dataset in Costa Rica. We found that the average node degree and the clustering coefficient are good metrics like the paired correlation function. In addition, the network approach can identify fine-scale spatial variations of tree competition and its underlying causes. Our analyzes also indicate that a bit of caution is needed when defining the network structure as well as designing network-based metrics. We suggested that validation techniques using corresponding spatial null models are critically important to reduce the negative effects caused by uncertainties of the network.
描述树木的空间格局和相互作用有助于揭示森林群落形成的潜在过程。尽管空间网络非常复杂，但它们可以使用定义明确的模式在个体水平上检查空间相互作用，这非常强大。然而，复杂的造林网络会带来不确定性。需要验证方法来评估基于网络的指标是否可以识别不同的过程。在这里，我们构建了三种类型的网络，反映了树木竞争的各个方面。基于五个空间零模型和 199 次蒙特卡罗模拟，我们能够选择表现出区分不同过程的良好性能的基于网络的指标。然后，我们将该技术应用于哥斯达黎加的热带森林数据集。我们发现，平均节点度和聚类系数是很好的指标，就像配对相关函数一样。此外，网络方法可以识别树木竞争及其潜在原因的细微空间变化。我们的分析还表明，在定义网络结构和设计基于网络的指标时需要谨慎。我们建议使用相应的空间零模型进行验证技术对于减少网络不确定性造成的负面影响至关重要。
