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

A Carla Staver1, Gregory P Asner2, Ignacio Rodriguez-Iturbe3

  • 1Department of Ecology and Evolutionary Biology, Yale University, New Haven, CT 06511; carla.staver@yale.edu irodriguez@ocen.tamu.edu.

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|May 16, 2019
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Summary
This summary is machine-generated.

Predicting savanna vegetation is challenging. Airborne Light Detection and Ranging (LiDAR) data reveal tree clusters follow statistical distributions, offering predictable patterns at larger scales.

Keywords:
LiDARheterogeneitysavannaspatial pattern

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Area of Science:

  • Ecology
  • Spatial Statistics
  • Remote Sensing

Background:

  • Savanna vegetation patterns are complex and difficult to predict.
  • Spatial interactions like competition and facilitation influence vegetation distribution.
  • Understanding these patterns is crucial for ecological modeling.

Purpose of the Study:

  • To analyze tree-clustering patterns in an African savanna using high-resolution data.
  • To determine if tree distribution follows predictable statistical laws.
  • To assess the influence of the underlying environment on these patterns.

Main Methods:

  • Utilized airborne Light Detection and Ranging (LiDAR) data for high-resolution tree distribution mapping.
  • Analyzed spatial extent and clustering of trees across the savanna landscape.
  • Applied power-law analysis to quantify tree cluster size distributions.

Main Results:

  • Tree cluster sizes in the savanna were governed by power laws across multiple spatial scales.
  • The parameters of these power-law distributions were invariant across different environmental conditions.
  • Despite local unpredictability, large-scale vegetation structure exhibits regular statistical patterns.

Conclusions:

  • Savanna tree distributions, while locally variable, are statistically structured at scales relevant for global vegetation models.
  • The observed power-law distributions suggest underlying universal processes may influence spatial patterning.
  • Airborne LiDAR data provide valuable insights into savanna vegetation dynamics and spatial organization.