## 1 Introduction
In both cases, due to the resulting decreases in plant productivity, ranchers are faced with a dilemma: they may reduce the stocking rates and/or provide supplementary food to livestock, reducing profits and risking \"overspill\" overgrazing effects around areas of supplementary feeding [28; 29]; or they may maintain the stocking rates, assuming increasing levels of ecosystem and land degradation [1; 21]. Avoiding the aforementioned risk of land and ecosystem degradation is often complex, because significant reductions in livestock, enough to prevent overgrazing in dry years, would result in a reduction in the economic incomes of the local farmers, with the associated risk of abandonment, which may be as detrimental as excessive intensification [30]. Hence, increasing effort is being devoted to the development of flexible management strategies that can be adapted to expected intra- and inter-annual fluctuations in plant primary production in response to rainfall [31; 32]. Ranches in the Mediterranean and semiarid regions, particularly those in protected areas, often host a variety of vegetation typesthat respond differently to seasonal and inter-annual changes in rainfall. The distribution of the different vegetation types is often associated with spatial heterogeneity in soil fertility and water content, and may reduce the risk of drought-induced overgrazing if adequately tailored to spatio-temporal grazing patterns [29], e.g., in the presence of wetland vegetation, which may maintain high levels of productivity in dry years and \"escape\" grazing in rainy years due to prolonged flooding [33]. The development and optimization of these management tools will become even more valuable when we consider the expected scenarios of climate change, which predict an amplification of the hydrological cycle, characterized by more extreme precipitation events and more extensive periods between events (e.g., [34; 35]). The broad spatial extension and dynamism of the processes that must be taken into account require, however, innovative approaches, which can be currently undertaken due to the increasing availability of remote sensing data and tools. New advances in remote sensing and the increasing amount of freely accessible images and information have improved our ability to study environmental patterns and processes at a broad array of spatial and temporal scales [36], allowing us to use long-term, spatially-continuous data series essential for understanding ecosystem dynamics (e.g., [37]) and integrating biodiversity monitoring data [38; 39]. The combination of remote sensing data, field observations, and statistical modelling is already enabling scientists to address research questions that were unapproachable in the recent past, such as the detection of disruptions in ecosystem processes, the characterization of changes in plant phenology, or the impact of climate change on vegetation [40]. In this study we used a combination of remote sensing and in situ information to characterize the main factors driving the spatio-temporal variation in plant primary production, using the four main vegetation types of an iconic, Mediterranean protected area (Donana National Park) hosting traditional ranching as a suitable case example. For this purpose, we analyzed its response to the two most important drivers: climatology (accumulated rainfall and vegetation phenology) and the grazing pressure exerted by wild (red and fallow deer) and domestic (cattle and horses) ungulates. In doing so, we sought to: (1) Quantify the separate and combined impact that the population densities of wild ungulates and the stocking rates of domestic herbivores have on the production of the different vegetation types. (2) Evaluate the resilience of the vegetation to the combination of environmental stress (strong inter-annual fluctuations in rainfall and phenology) and grazing pressure, and the expected impact of climate change thereupon. (3) Derive adaptive strategies enabling a sustainable combination of ranching and wildlife conservation in this type of area.