## 4 Discussion  
The use of remote sensing imagery combined with field surveys of ungulates and meteorological data provides a powerful tool to model the factors determining vegetation production at Dofana National Park. Model results indicate that the four different vegetation types differed in their phenology (time of the NDVI peak), their productivity (NDVI peak value), and their response to inter-annual changes in rainfall levels. These differences reflect the spatio-temporal segregation among vegetation types and may increase ecosystem resilience to both climatological variability and grazing. Regarding the latter, model results also show important differences in the impact of wild and domestic species on vegetation production: the former have neutral, weak unimodal, or even positive effects, whereas the later may have strong negative impacts on the production of most vegetation types (three of four: saltmarsh, bulrush marsh, and grassland). As a consequence, model prediction plots indicate that increasing livestock densities decreases vegetation resilience to decreased rainfall levels or delayed phenological peaks, thus exacerbating the impact of medium-term changes (2041-2070) in the quantity and temporal distribution of rainfall predicted by the climate change scenarios. Our approach represents an attempt to simplify the modeling of plant production. We did not attempt to incorporate the whole suite of different meteorological factors (average, max. or min. temperature; length of the coldest period; temporal distribution of rainfall; etc. ), and then discriminate at which lag levels they operate, because this would increase model complexity beyond its limit. Rather, we chose an indirect approach, in which two variables, \"phenology\" (the time of the NDVI peak) and \"rainfall\" (the amount of rainfall accumulated until such a moment) were introduced as surrogates for the integrated effect of climatology (the temporal sequence of rainfall and temperature) and water availability (from the start to the peak of the growth season) on plant production. The result was encouraging, because the fitted models, despite their relative simplicity (low number of parameters) were highly significant, explained a considerable proportion of the observed variation, and provided useful insight into the operation of the constituent variables, including the effects of ungulate grazing. Detailed modeling of the ultimate, climatological causes of these effects, using a more extensive dataset, will however be necessary to derive more precise predictions and forecasts. Three of the four vegetation types reached their maximum production at or close to the average rainfall levels of the historical series (ca. 500 mm), albeit marshland vegetation performed better at higher rainfall levels (ca. 600 mm) and one type (grassland) did so at lower rainfall levels (ca. 400 mm). This convergence around average rainfall probably reflects the adaptation of the vegetation to local climate conditions, on which differences following the gradient of hygrophily (from the more xerophilous shrubland to the seasonally mesophylous bulrush marsh) is superimposed. In addition, model results reflected the considerable phenological differences among the different vegetation types. These differences result in a complex spatio-temporal pattern of plant primary production, in which the spatial mosaic of vegetation types is associated with seasonal differences in plant production that, in turn, vary among years. This allows for considerable resilience to variations in rainfall level and temporal distribution, given that these fluctuations do not undergo extreme departures from average levels.