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

Author:

Villegas P.1ORCID,Cavagna A.12,Cencini M.1ORCID,Fort H.3ORCID,Grigera T. S.1456

Affiliation:

1. Istituto dei Sistemi Complessi, Consiglio Nazionale delle Ricerche, via dei Taurini 19 00185 Rome, Italy

2. Dipartimento di Fisica, Università Sapienza, 00185 Rome, Italy

3. Institute of Physics, Faculty of Science, Universidad de la República, Iguá 4225, Montevideo 11400, Uruguay

4. Instituto de Física de Líquidos y Sistemas Biológicos—CONICET and Universidad Nacional de La Plata, La Plata, Argentina

5. CCT CONICET La Plata, Consejo Nacional de Investigaciones Científicas y Técnicas, Argentina

6. Departamento de Física, Facultad de Ciencias Exactas, Universidad Nacional de La Plata, La Plata, Argentina

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 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.

Funder

H2020 European Research Council

Horizon 2020 Framework Programme

Publisher

The Royal Society

Subject

Multidisciplinary

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