Dissecting NDVI–species richness relationships in Hawaiian dry forests
Article first published online: 23 MAY 2012
© 2012 Blackwell Publishing Ltd
Journal of Biogeography
Volume 39, Issue 9, pages 1678–1686, September 2012
How to Cite
Pau, S., Gillespie, T. W. and Wolkovich, E. M. (2012), Dissecting NDVI–species richness relationships in Hawaiian dry forests. Journal of Biogeography, 39: 1678–1686. doi: 10.1111/j.1365-2699.2012.02731.x
- Issue published online: 14 AUG 2012
- Article first published online: 23 MAY 2012
- ecosystem function;
- forest structure;
- habitat complexity;
- tropical dry forest;
- woody plants
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.