Spatial patterning among savanna trees in high-resolution, spatially extensive data.

Abstract:

:In savannas, predicting how vegetation varies is a longstanding challenge. Spatial patterning in vegetation may structure that variability, mediated by spatial interactions, including competition and facilitation. Here, we use unique high-resolution, spatially extensive data of tree distributions in an African savanna, derived from airborne Light Detection and Ranging (LiDAR), to examine tree-clustering patterns. We show that tree cluster sizes were governed by power laws over two to three orders of magnitude in spatial scale and that the parameters on their distributions were invariant with respect to underlying environment. Concluding that some universal process governs spatial patterns in tree distributions may be premature. However, we can say that, although the tree layer may look unpredictable locally, at scales relevant to prediction in, e.g., global vegetation models, vegetation is instead strongly structured by regular statistical distributions.

authors

Staver AC,Asner GP,Rodriguez-Iturbe I,Levin SA,Smit IPJ

doi

10.1073/pnas.1819391116

subject

Has Abstract

pub_date

2019-05-28 00:00:00

pages

10681-10685

issue

22

eissn

0027-8424

issn

1091-6490

pii

1819391116

journal_volume

116

pub_type

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