Global-change drivers of ecosystem functioning modulated by natural variability and saturating responses

Abstract

Humans are altering global environment at an unprecedented rate through changes in biodiversity, climate, nitrogen cycle, and land use. To address their effects on ecosystem functioning, experiments most frequently explore one driver at a time and control as many confounding factors as possible. Yet, which driver exerts the largest influence on ecosystem functioning and whether their relative importance changes among systems remain unclear. We analyzed experiments in the Patagonian steppe that evaluated the aboveground net primary production (ANPP) response to manipulated gradients of species richness, precipitation, temperature, nitrogen fertilization (N), and grazing intensity. We compared the effect on ANPP relative to ambient conditions considering intensity and direction of manipulations for each driver. The ranking of responses to drivers with comparable manipulation intensity was as follows: biodiversity>grazing>precipitation>N. For a similar intensity of manipulation, the effect of biodiversity loss was 4.0, 3.6, and 1.5, times larger than N deposition, decreased precipitation, and increased grazing intensity. We interpreted our results considering two hypotheses. First, the response of ANPP to changes in precipitation and biodiversity is saturating, so we expected larger effects when the driver was reduced, relative to ambient conditions, than when it was increased. Experimental manipulations that reduced ambient levels had larger effects than those that increased them. Second, the sensitivity of ANPP to each driver is inversely related to the natural variability of the driver. In Patagonia, the ranking of natural variability of drivers is as follows: precipitation>grazing>temperature>biodiversity>N. So, in general, the ecosystem was most sensitive to drivers that varied the least. Comparable results from Cedar Creek (MN) support both hypotheses and suggest that sensitivity to drivers varies among ecosystem types. Given the importance of understanding ecosystem sensitivity to predict global-change impacts, it is necessary to design new experiments located in regions with contrasting natural variability and that include the full range of drivers.