# Spatial and Temporal Dependency of NDVI Satellite Imagery in Predicting Bird Diversity over France
## Material and Method
### Climate and Satellite Data  
Because avitauna patterns are driven by large climate gradients at the French national scale, a map of macro-climate regions of France was used to derive a climate factor. The delineation of the climate regions in this map is based on 14 temperature and precipitation variables calculated from a 30-year period of climate data (1971-2000) provided by the french national meteorological service [31]. The map is composed of eight regions (Figure 2): the climate of mountain areas (type 1); the semi-continental climate and the climate of peripheral mountain areas (type 2); the degraded oceanic climate of the north and center plains (type 3); the altered oceanic climate (type 4); the marked oceanic climate (type 5); the altered Mediterranean climate (type 6); the southwest basin climate (type 7); and the marked Mediterranean climate (type 8). Based on this map, the dominant climate region was estimated for each plot of bird data and defined as one qualitative predictor. An alternative would have been to use WorldClim2 data which are locally defined at 1 km\({}^{2}\) scale [32]. However, these data are based on an interpolation using fewer weather stations than the map defined by [31] at French scale. This is why we retained the first source of data (i.e., map of macro-climate regions). Normalized Difference Vegetation Index (NDVI) was used to provide surrogate of local vegetation cover and landscape heterogeneity patterns over France. The Terra-MODIS NDVI 16-day-composit product at 250-m spatial resolution (MOD13Q1) was retained. To accommodate for seasonal variation  
Figure 1: Distribution of the selected bird plot squares over France for the year 2010. Each plot of 2 km side contains 10 point counts. Plots included in the analyses (\(n=759\)) were filtered according to the quality of the satellite imagery. and evaluate the temporal effect of NDVI on models performance, five time periods in 2010 were selected (Figure 2): late winter (6 March 2010, NDVI\({}_{065}\)), before leaves growing of deciduous tree; late spring (26 June 2010, NDVI\({}_{177}\)), before winter crops harvesting; late summer (29 August 2010, NDVI\({}_{241}\)), after winter crops harvesting before summer crops harvesting; end summer (14 September 2010, NDVI\({}_{257}\)), after partial summer crops harvesting; begin autumn (30 September 2010, NDVI\({}_{273}\)), after full crops harvesting. At these time periods, landscape composition and heterogeneity are revealed differently in NDVI data. In winter, NDVI values are low in the deciduous forests and in the non-cultivated agricultural parcels because of absence of photosynthetic activity. In spring and summer, NDVI reaches its highest values, mainly in winter and summer crops respectively, but also in grasslands and forests. In autumn, after all crops harvesting, NDVI discriminates well forests and open habitats because of higher difference of NDVI values, agricultural parcels being bare (see Appendix E). The NDVI data of each time period were selected after checking the MODIS quality layers of all the 16-day time series products of 2010. Based on the average value of the MOD13Q1 _pixel reliability_ over France (0 = good, 1 = marginal data, 2 = snow/ice, 3 = cloudy), the images showing the highest quality (i.e., having the lowest average quality index) were retained for each time period. Then, a filter was applied on the bird survey plots available (\(n=1091\)). Plots including NDVI pixels of bad quality were removed. Only plots composed of 100% of good pixels (i.e., with a _pixel reliability_ = 0) at the five time periods were used (\(n=759\)). Finally, NDVI values in these plots were summarized through several statistical predictors: _minimum_, _maximum_, _range_, _average_, _median_, and _standard deviation_. In order to prevent multicollinearity between covariates, only the _average_ (Avg) and _standard deviation_ (SD) predictors were retained (Spearman's \(\mid\)_Rho_\(\mid\)\(<0.7\)) [33].