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Beyond Precipitation: Physiographic Gradients Dictate the Relative Importance of Environmental Drivers on Savanna Vegetation

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  • Miguel A Campo-Bescós
  • Rafael Muñoz-Carpena
  • David A Kaplan
  • Jane Southworth
  • Likai Zhu
  • Peter R Waylen

Abstract

Background: Understanding the drivers of large-scale vegetation change is critical to managing landscapes and key to predicting how projected climate and land use changes will affect regional vegetation patterns. This study aimed to improve our understanding of the role, magnitude and spatial distribution of the key environmental factors driving vegetation change in southern African savanna, and how they vary across physiographic gradients. Methodology/Principal Findings: We applied Dynamic Factor Analysis (DFA), a multivariate times series dimension reduction technique to ten years of monthly remote sensing data (MODIS-derived normalized difference vegetation index, NDVI) and a suite of environmental covariates: precipitation, mean and maximum temperature, soil moisture, relative humidity, fire and potential evapotranspiration. Monthly NDVI was described by cyclic seasonal variation with distinct spatiotemporal patterns in different physiographic regions. Results support existing work emphasizing the importance of precipitation, soil moisture and fire on NDVI, but also reveal overlooked effects of temperature and evapotranspiration, particularly in regions with higher mean annual precipitation. Critically, spatial distributions of the weights of environmental covariates point to a transition in the importance of precipitation and soil moisture (strongest in grass-dominated regions with precipitation 950 mm). Conclusions/Significance: We quantified the combined spatiotemporal effects of an available suite of environmental drivers on NDVI across a large and diverse savanna region. The analysis supports known drivers of savanna vegetation but also uncovers important roles of temperature and evapotranspiration. Results highlight the utility of applying the DFA approach to remote sensing products for regional analyses of landscape change in the context of global environmental change. With the dramatic increase in global change research, this methodology augurs well for further development and application of spatially explicit time series modeling to studies at the intersection of ecology and remote sensing.

Suggested Citation

  • Miguel A Campo-Bescós & Rafael Muñoz-Carpena & David A Kaplan & Jane Southworth & Likai Zhu & Peter R Waylen, 2013. "Beyond Precipitation: Physiographic Gradients Dictate the Relative Importance of Environmental Drivers on Savanna Vegetation," PLOS ONE, Public Library of Science, vol. 8(8), pages 1-14, August.
    • Handle: RePEc:plo:pone00:0072348
    DOI: 10.1371/journal.pone.0072348
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    References listed on IDEAS

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    1. Huiyan Sang & Jianhua Z. Huang, 2012. "A full scale approximation of covariance functions for large spatial data sets," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 74(1), pages 111-132, January.
    2. Catherine Linard & Marius Gilbert & Robert W Snow & Abdisalan M Noor & Andrew J Tatem, 2012. "Population Distribution, Settlement Patterns and Accessibility across Africa in 2010," PLOS ONE, Public Library of Science, vol. 7(2), pages 1-8, February.
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    1. Jane Southworth & Lesley Rigg & Cerian Gibbes & Peter Waylen & Likai Zhu & Shannon McCarragher & Lin Cassidy, 2013. "Integrating Dendrochronology, Climate and Satellite Remote Sensing to Better Understand Savanna Landscape Dynamics in the Okavango Delta, Botswana," Land, MDPI, vol. 2(4), pages 1-19, November.

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