Dissecting NDVI–species richness relationships in Hawaiian dry forests

Authors

  • Stephanie Pau,

    Corresponding author
    1. Department of Geography, University of California, Los Angeles, Los Angeles, CA 90095-1524, USA
      Stephanie Pau, National Center for Ecological Analysis and Synthesis (NCEAS), 735 State Street, Suite 300, Santa Barbara, CA 93101, USA.
      E-mail: pau@nceas.ucsb.edu
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  • Thomas W. Gillespie,

    1. Department of Geography, University of California, Los Angeles, Los Angeles, CA 90095-1524, USA
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  • Elizabeth M. Wolkovich

    1. Biodiversity Research Centre, University of British Columbia – Vancouver, Vancouver, BC V6T 1Z4, Canada
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Stephanie Pau, National Center for Ecological Analysis and Synthesis (NCEAS), 735 State Street, Suite 300, Santa Barbara, CA 93101, USA.
E-mail: pau@nceas.ucsb.edu

Abstract

Aim  A growing body of research has used the normalized difference vegetation index (NDVI) as a proxy for productivity to predict species richness. Yet the mechanisms that produce the relationship between NDVI and species richness remain unclear because of correlated biotic and abiotic factors that influence NDVI. In this study we investigated different biotic and abiotic effects that potentially drive plant species richness–productivity relationships.

Location  Hawaiian Islands, USA.

Methods  We quantified woody plant species richness, structure (density, basal area and canopy height), and species composition along a precipitation gradient of 14 Hawaiian dry forest plots. We then used structural equation models combined with 10 years of satellite data to disentangle the effects of precipitation, structure and NDVI-estimated productivity on species richness.

Results  Underlying the simple correlation between NDVI and species richness was the indirect effect of precipitation and direct effect of forest structure. The best-fit model showed there was no direct effect of NDVI on species richness.

Main conclusions  Our results demonstrate that complex relationships drive simple correlations between species richness and productivity. Considering the mechanisms and underlying factors driving NDVI–species richness relationships could improve predictions of species diversity as satellite measures of productivity have an increasingly important role in habitat mapping, species distribution modelling and predictions for global change.

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