Joint assessment of density correlations and fluctuations for analysing spatial tree patterns

TL;DR

This paper combines the analysis of density correlations and fluctuations to better understand spatial tree patterns in tropical forests, highlighting the importance of accurate border definitions and scale-dependent fluctuations.

Contribution

It introduces a joint approach to analyze density correlations and fluctuations, improving interpretation of spatial patterns and comparing empirical data with neutral models.

Findings

Density correlations reveal clustering at short scales.

Fluctuations follow Taylor's power law across scales.

Neutral models replicate key features of empirical patterns.

Abstract

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 kind 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 an unique opportunity to interpret the behaviors 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.