# Spatial and Temporal Dependency of NDVI Satellite Imagery in Predicting Bird Diversity over France
###### Abstract  
Continuous-based predictors of habitat characteristics derived from satellite imagery are increasingly used in species distribution models (SDM). This is especially the case of Normalized Difference Vegetation Index (NDVI) which provides estimates of vegetation productivity and heterogeneity. However, when NDVI predictors are incorporated into SDM, synchrony between biological observations and image acquisition must be questionned. Due to seasonal variations of NDVI during the year, landscape patterns of habitats are revealed differently from one date to another leading to variations in models' performance. In this paper, we investigated the influence of acquisition time period of NDVI to explain and predict bird community patterns over France. We examined if the NDVI acquisition period that best fit the bird data depends on the dominant land cover context. We also compared models based on single time period of NDVI with one model built from the Dynamic Habitat Index (DHI) components which summarize variations in vegetation phenology throughout the year from the fraction of radiation absorbed by the canopy (fPAR). Bird species richness was calculated as response variable for 759 plots of 4 km\({}^{2}\) from the French Breeding Bird Survey. Bird specialists and generalists to habitat were considered. NDVI and DHI predictors were both derived from MODIS products. For NDVI, five time periods in 2010 were compared, from late winter to begin of autumn. A climate predictor was also used and Generalized Additive Models were fitted to explain and predict bird species richness. Results showed that NDVI-based proxies of dominant habitat identity and spatial heterogeneity explain more bird community patterns than DHI-based proxies of annual productivity and seasonality. We also found that models' performance was both time and context-dependent, varying according to the bird groups. In general, best time period of NDVI did not match with the acquisition period of bird data because in case of synchrony, differences in habitats are less pronounced. These findings suggest that the most powerful approach to estimate bird community patterns is the simplest one. It only requires NDVI predictors from a single appropriate time period, in addition to climate, which makes the approach very operational. MODIS; seasonal NDVI; bird species richness; dynamic habitat index; breeding bird; species distribution model; habitat productivity; habitat heterogeneity; macroecology 201810.1136; doi:10.3390/rs1007113610.3390/rs1007113610. At finer spatial scales, the components of landscape mosaics play a key role in explaining species habitat selection [4; 5; 6]. Remote sensing-based predictors of habitat characteristics may contribute to improve the performances of species distribution models (SDM) [7]. However, the question of the most appropriate data and representation (discrete vs. continuous-based metrics) in SDM still remains [8; 9; 10; 11; 12]. Using unclassified satellite images with continuous-based metrics is a fast way to estimate landscape productivity and heterogeneity. These indirect surrogates can explain and predict community patterns with equal or higher performances than those obtained with land cover maps [11; 13; 14; 15]. The normalized difference vegetation index (NDVI) which is calculated from near-infrared and red bands is widely used to reflect environmental factors [16]. At large scale, NDVI is a good estimate of ecosystem productivity, enabling to model energy-abundance relationships or to predict species richness [17; 18; 19]. Locally, its spatial heterogeneity can be related to the diversity of ecological habitats, also explaining patterns of species groups with different ecological preferences [20; 21]. Because NDVI varies during the year according to the vegetation phenological stages, this indicator conveys differently the landscape mosaic between the seasons, leading to contrasting performances in explaining biodiversity patterns [21; 22; 23].