Journal
PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF THE UNITED STATES OF AMERICA
Volume 116, Issue 22, Pages 10681-10685Publisher
NATL ACAD SCIENCES
DOI: 10.1073/pnas.1819391116
Keywords
savanna; spatial pattern; LiDAR; heterogeneity
Categories
Funding
- Andrew W. Mellon Foundation
- National Science Foundation Division of Mathematical Sciences [1615531, 1615585]
- National Science Foundation Macrosystems Biology Grant [1802453]
- Avatar Alliance Foundation
- Margaret A. Cargill Foundation
- David and Lucile Packard Foundation
- Gordon and Betty Moore Foundation
- Grantham Foundation for the Protection of the Environment
- W. M. Keck Foundation
- John D. and Catherine T. MacArthur Foundation
- Direct For Biological Sciences
- Division Of Environmental Biology [1802453] Funding Source: National Science Foundation
- Division Of Mathematical Sciences
- Direct For Mathematical & Physical Scien [1615585] Funding Source: National Science Foundation
- Division Of Mathematical Sciences
- Direct For Mathematical & Physical Scien [1615531] Funding Source: National Science Foundation
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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.
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