# Spatial and Temporal Dependency of NDVI Satellite Imagery in Predicting Bird Diversity over France
## 4 Discussion  
Single-Date NDVI Is as Accurate as DHI or Multi-Date NDVI to Explain and Predict Bird Species Richness  
Our study is the first to evaluate the potential added value of DHI components, compared to NDVI, to model the relationships between bird community and landscape structure. The results suggest that the integrative DHI components which summarizes vegetation productivity and heterogeneity over the year are not the best predictors to explain and predict diversity patterns of breeding birds over France. We found very close performances between NDVI and DHI-based models for woodland birds, generalists and total species richness. However, we observed that single-date NDVI models fitted and predicted better than DHI model for farmland birds (with a difference around 14% for the explained deviance). This is true whatever the spatial resolution of the NDVI product. NDVI at 500-m was highly correlated with NDVI at 250-m (r \(>\) 0.92 between _average_ NDVI variables for all dates). Contrary to our assumption [21], difference in spatial resolution had no effect on models' performance. Figure 7: Comparison of performances (i.e., [PERSON]’s rank correlations _Rho_ between observed and predicted values) in predicting species richness of woodland and farmland birds according to different dominant land cover in the bird plots. The bird-habitat models were based on climate only, climate with 250-m NDVI variables of each time period, and climate with ensemble 250-m NDVI (consensus approach). The predictive performances were calculated by 3-fold cross-validation with 100 repetitions. The average values of _Rho_ is provided above each boxplot. Different letters indicate that _Rho_ values are significantly different between datasets. At the scale of analysis (bird plot of 4 km\({}^{2}\)), _average_ and _standard deviation_ of NDVI 250-m and NDVI 500-m are similar. A possible explanation of the non-superiority of the DHI could be the difference in proxies of vegetation productivity. The DHI is based on the fPAR (derived from a physically based model) and not on NDVI which may saturate at high biomass. But a recent study showed that DHI derived from vegetation index (such as NDVI or EVI) was weaker to explain bird species richness than DHI computed from MODIS fPAR, LAI or Gross Primary Productivity (GPP) [29]. Previous studies testing the predictive power of DHI on bird species diversity were based on very large sample units such as USA ecoregions (around 90,000 km\({}^{2}\) in average) that are homogeneous in terms of environmental conditions [29; 48]. Compared to our analysis, similar degrees of correlation were observed at this spatial extent with variations depending on avian functional groups. In [29], the multiple regression model predicted species richness of woodland birds using the fPAR-derived DHI with an adjusted coefficient of determination (\(\%R_{adj}^{2}\)) equals to 0.58. In [48] the explained variance was weaker (\(\%R_{adj}^{2}=0.37\)). In both cases, cumulative DHI was most predictive which can be referred to the species-energy theory [49]. In our study, the sample unit of investigation is substantially smaller (4 km\({}^{2}\)) and match with the bird survey. At this fine scale, birds mainly respond to local landscape factors such as resource availability, habitat amount and habitat diversity [50]. The results show that DHI discriminates the habitat composition and heterogeneity less accurately at this scale than an appropriate single-date NDVI. This is especially true in agricultural landscapes where differences in DHI between crops, grasslands and woodlands may be less pronounced than NDVI at best date because of the integrated greeness over the year. Therefore, the _average_ and _standard deviation_ NDVI variables (proxies of dominant habitat identity and spatial heterogeneity) explain more bird community variance than cumulative and variation DHI (proxies of annual productivity and seasonnality).