Data from: The utility of normalized difference vegetation index for predicting African buffalo forage quality

Many studies of mammalian herbivores have employed remotely sensed
vegetation greenness, in the form of Normalized Difference Vegetation
Index (NDVI) as a proxy for forage quality. The assumption that reflected
greenness represents forage quality often goes untested, and limited data
exist on the relationships between remotely sensed and traditional forage
nutrient indicators. We provide the first study connecting NDVI and forage
nutrient indicators within a free-ranging African herbivore ecosystem. We
examined the relationships between fecal nutrient levels (nitrogen and
phosphorus), forage nutrient levels, body condition, and NDVI for African
buffalo (Syncerus caffer) in a South African savanna ecosystem over a
2-year period (2001 and 2002). We used an information-theoretic approach
to rank models of fecal nitrogen (Nf) and phosphorus (Pf) as functions of
geology, season, and NDVI in each year separately. For each year, the
highest ranked models for Nf accounted for 61% and 65% of the observed
variance, and these models included geology, season, and NDVI. The
top-ranked model for Pf in 2001, although capturing 54% of the
variability, did not include NDVI. In 2002, we could not identify a top
ranking model for phosphorus (i.e., all models were within 2 AICc of each
other). Body condition was most highly correlated (equation image;
P ≤ 0.001) with NDVI at a 1 month time lag and with Nf at a 3 months time
lag (equation image; P ≤ 0.001), but was not significantly correlated with
Pf. Our findings suggest that NDVI can be used to index nitrogen content
of forage and is correlated with improved body condition in African
buffalo. Thus, NDVI provides a useful means to assess forage quality where
crude protein is a limiting resource. We found that NDVI accounted for
more than a seasonal effect, and in a system where standing biomass may be
high but of low quality, understanding available nutrients is useful for
management.