Inferring the processes underlying the emergence of observed patterns is a key challenge in theoretical ecology. Much effort has been made in the past decades to collect extensive and detailed information about the spatial distribution of tropical rainforests, as demonstrated, e.g. in the 50 ha tropical forest plot on Barro Colorado Island, Panama. These kinds of plots have been crucial to shed light on diverse qualitative features, emerging both at the single-species or the community level, like the spatial aggregation or clustering at short scales. Here, we build on the progress made in the study of the density correlation functions applied to biological systems, focusing on the importance of accurately defining the borders of the set of trees, and removing the induced biases. We also pinpoint the importance of combining the study of correlations with the scale dependence of fluctuations in density, which are linked to the well-known empirical Taylor’s power law. Density correlations and fluctuations, in conjunction, provide a unique opportunity to interpret the behaviours and, possibly, to allow comparisons between data and models. We also study such quantities in models of spatial patterns and, in particular, we find that a spatially explicit neutral model generates patterns with many qualitative features in common with the empirical ones.

推断观察到的模式出现的潜在过程是理论生态学中的关键挑战。在过去的几十年中，已经做出了很多努力来收集有关热带雨林空间分布的广泛而详细的信息，例如在巴拿马的Barro Colorado Island的50公顷的热带森林地块中就证明了这一点。这些类型的地块对于揭示各种质量特征至关重要，无论是在单一物种还是在社区一级，例如在小规模上的空间聚集或聚类。在这里，我们基于对应用于生物系统的密度相关函数的研究进展，重点是准确定义树木边界的重要性，并消除引起的偏差。我们还指出了将相关性研究与密度波动的比例依赖性相结合的重要性，密度波动与众所周知的经验泰勒幂定律相关。密度相关性和波动性共同为解释行为提供了独特的机会，并可能允许对数据和模型进行比较。我们还在空间模式的模型中研究了这种数量，特别是，我们发现一个空间明确的中性模型生成的模式具有许多与经验性定性特征相同的特征。以便在数据和模型之间进行比较。我们还在空间模式的模型中研究了这种数量，特别是，我们发现一个空间明确的中性模型生成的模式具有许多与经验性定性特征相同的特征。以便在数据和模型之间进行比较。我们还在空间模式的模型中研究了这种数量，特别是，我们发现一个空间明确的中性模型生成的模式具有许多与经验性定性特征相同的特征。