# Spatial and Temporal Dependency of NDVI Satellite Imagery in Predicting Bird Diversity over France
## 3 Results  
### Comparison of Single-Date NDVI Models with DHI According to Species Richness Metrics  
Bird-habitat models showed different explanatory performances according to the species richness metrics, data types and acquisition periods considered (Table 2). Whatever the time period of NDVI, climate always had a significant effect on the four species richness metrics. For the models based on climate only, the explanatory power is high for woodland species (\(D^{2}=23\%\)) but more limited for the other groups, with the lowest explanatory power for farmland species (\(D^{2}=6\%\)). Adding NDVI or DHI variables to climate increased the performance of the models. The best fits were observed for woodland (\(D^{2}=45\%\) at 250 m) and farmland (\(D^{2}=35\%\) at 250 m) birds. The lowest values were found for generalist species (from \(D^{2}=7\%\) to \(D^{2}=14\%\) at 250 m). The DHI-based models showed similar performances in comparison with the best NDVI-based models for woodland birds (\(D^{2}=43\%\)), generalist species (\(D^{2}=13\%\)) and total species richness (\(D^{2}=20\%\)). By contrast, the explanatory power was lower for farmland species (\(D^{2}=21\%\)). The same conclusions between the DHI and NDVI-based models were obtained whatever the spatial resolution of NDVI. Regarding NDVI variables, _average_ NDVI was more important than _standard-deviation_ NDVI for explaining species richness metrics. For DHI, _average_ DHI cv (i.e., seasonality, based on the coefficient of variation of annual fPAR) and _average_ DHI cum (i.e., productivity, based on the cumulative annual fPAR) contributed most (see Appendix C). Considering the effect of NDVI seasonal variation on the model performances, whatever the landscape context, late winter (NDVI 6-Mar) was the least accurate time period for every species richness variable. The optimal NDVI time period varied according to the bird species richness metric (Table 2). For woodland species richness, end summer (NDVI 14-Sep) was the best time period, but only slight difference in accuracy was observed with the other periods, except for winter (NDVI 6-Mar). For farmland species richness, late spring (NDVI 26-Jun) was the most adapted. For generalist and total species richness, several periods were similarly accurate. The seasonal variation of NDVI was a lower impact on these response variables since the contribution of NDVI was limited (Appendix C). The response shapes to the NDVI variables were different according to the bird group considered (Figure 4). For woodland birds, species richness increased continuously with the _average_ NDVI variable. By contrast, farmland species richness was quite stable for low values of _average_ NDVI but decreased when _average_ NDVI reached 0.7. Total richness increased until the same threshold (average NDVI of 0.7) and decreased after this value. The curves were constructed after selecting the best NDVI time period for each bird group. In terms of predictive performance, the best predictions were obtained for woodland and farmland birds (maximum mean _Rho_ = 0.70 and 0.53, respectively) (Figure 5). Generalist and total species richness showed fairly good but lower predictive accuracies (maximum mean _Rho_ = 0.36 and _Rho_ = 0.39, respectively). Comparing to the single-date models, the consensus NDVI approach showed superior predictive performances only for generalist species richness. The predictive accuracy of DHI was slightly but significantly lower than the best NDVI single-date model for all the bird groups.