## 2 Materials and Methods
### Estimation of Primary Production  
Satellite information obtained from the Institute of Surveying, Remote Sensing and Land Information (IVFL) of the University of Natural Resources and Applied Life Sciences (BOKU), Vienna, was used to estimate the production of different vegetation types during the study period. This institution offers remote sensing products--smooth and continuous Normalized Difference Vegetation Index (NDVI) and Enhanced Vegetation Index (EVI)--from MODIS satellite images with different temporal resolution (from 16 to 7 days) from year 2000 to present. These products are the result of processing standard products from Terra and Aqua satellites, namely MODIS Level-3 16 day composite Vegetation Indices (VI) available at a spatial resolution of 250 m. The combination of 16 day composites from these two satellites--Terra (MOD13 series) and Aqua (MYD13 series)--allows acquisition of imagery at a temporal resolution of 7 days. Detailed information on the processes for creating these time series can be consulted in [43] and at BOKU's website ([[https://boku.ac.at/rali/geomatics](https://boku.ac.at/rali/geomatics)]([https://boku.ac.at/rali/geomatics](https://boku.ac.at/rali/geomatics)) accessed on 10 January 2019). Figure 1: Distribution of the four vegetation types used in this study within the Dóñana National Park. Black lines represent different management units (largely coinciding with property boundaries). Scrubland is shown in orange, grassland in yellow, saltmarsh in light green, and bulrush marshland in dark green. The acronyms refer to the names of the management units included in the study. The black dot indicates the location of the meteorological station. White areas indicate areas of bare soil, forest stands, or mixed vegetation (i.e., pixels where the dominant vegetation occupied less than 70% of the area) that were not used for the calculations of NDVI per vegetation type and management unit (as explained above). In this study, we used the Normalized Difference Vegetation Index (NDVI), a commonly used vegetation index that serves as a proxy of vegetation density and plant health [44]. NDVI was demonstrated to reflect appropriately the vegetation response to rainfall variability [45] and extreme events (e.g., droughts) across different biomass around the world [46]. We selected NDVI as a proxy of primary production because (1) we felt that it achieved the best balance between spatial resolution (pixel size) and robustness; and (2) we have already tested (i.e., calibrated and validated) a statistical model relating biomass production to NDVI for one of the main vegetation types [47]. Alternative products, such as MODIS NPP and GPP, had a lower spatial resolution (1 to 0.5 km) than MODIS NDVI (0.25 km); and Landsat images, which provide a better spatial resolution (15-30 m) than MODIS, cannot be processed by the TIMESAT software (which requires regular time intervals) to fit the phenological curves, and showed large gaps due to different reasons, such as missing data (lines and columns), line shifts, radiometric incoherence, and the presence of clouds over the study area. We took a number of precautions to address the limitations of using NDVI, as compared to NPP or GPP (which may work better in areas dominated by bare ground or with high tree cover) or Landsat (which provide better spatial resolution). First, the four vegetation types selected are structurally homogeneous and moderately productive, and we explicitly excluded forest stands with dense canopies and areas of bare soil. Second, we excluded from our dataset all pixels with mixed vegetation cover (i.e., those where the dominant vegetation occupied less than 70% of the pixel's area). We used the long-term series of NDVI values with the highest temporal resolution available (every 7 days) for the study period (January 2000-August 2014, corresponding to the period for which ungulate population data were available).