Summary 1The influence of botanical composition on annual phytomass production of a semiarid grassland in response to precipitation was tested with a 19-year experiment. Three compositional states reflecting good, medium and poor rangeland condition, whose basal cover increased from poor through to good, were created in 1978.2 Multiple regression models were developed for assessing whether phytomass was influenced by precipitation, composition, phytomass of the previous year, basal cover, abundance of individual species, or diversity. Composition and precipitation accounted for 66% of the variation in phytomass, but a separate model of basal cover by precipitation was equally successful. The linear effect of rainfall on phytomass was enhanced as composition improved from poor (0.076 g m -2 mm ) to good (0.277 g m -2 mm -1 ). Phytomass increased for low precipitation if it had been high the preceding year. Phytomass was more variable over time with deteriorating condition. 3 Species' complementarity ensured greater and more stable production. Setaria sphacelata , Eragrostis chloromelas and Digitaria eriantha increased phytomass on the good or medium condition grasslands during drier years, whereas Themeda triandra had this effect during wetter years. 4 Precipitation-use efficiency (PUE) was influenced mostly by composition and a linear and quadratic effect of precipitation (63% of variance). Optimum PUE of 0.308, 0.203 and 0.096 g m -2 mm -1 for the good, medium and poor condition grasslands, respectively, occurred at intermediate amounts ( ± 680 mm) of precipitation. PUE was increased if phytomass had been high the previous year. 5 Species' complementarity of PUE in response to precipitation was evident for all compositional states. Ten, mostly uncommon, species and their interaction with precipitation explained an extra 21-42% of the variance. Stability of production was related to PUE for medium and poor condition grassland. Uncommon species therefore ensured growth efficiency and stabilized production as condition deteriorated. 6 Diversity had no influence on phytomass or PUE except for a small to moderate effect, respectively, for the medium condition grassland. 7 Vegetation structure, through limiting runoff and promoting infiltration, is an important control on the amount and efficiency of plant production under variable precipitation, whilst composition further influences the amount and stability of production.

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Summary 1Relationships between above-ground net primary productivity (ANPP) of grasslands and annual precipitation are often weak at the site level, with much of the inter-annual variation in ANPP left unexplained. A potential reason for this is that the distribution of precipitation within a growing season affects productivity in addition to the total amount. 2 We analysed long-term ANPP data for three southern African temperate grasslands (mean annual precipitation ranging from 538 mm to 798 mm) to determine the effects of precipitation event size, number and spacing relative to seasonal totals. 3 Ungrazed, non-manipulated treatments at each site showed contrasting results despite sharing a common, dominant species. At the driest site, a model combining average event size and number of events per growing season provided a substantially better fit to the ANPP data than precipitation amount (seasonal total). At the wettest site, the interval between events was the most important precipitation variable. Precipitation distribution was not important at the intermediate site where amount was the best predictor of ANPP. A limit to the size of precipitation events efficiently utilized for ANPP was evident for the driest site only. 4 At each site, experimental treatments that altered species composition and soil fertility had little effect on precipitation-ANPP relationships. The lack of consistency in the relative importance of the precipitation variables among sites suggests that local, edaphic factors modify precipitation-ANPP relationships. 5 This analysis demonstrates that the distribution and size of precipitation events can affect ANPP independent of precipitation amount. As altered precipitation regimes are forecast by global climate models, the sensitivity of ecosystems to precipitation distribution should be considered when predicting responses to climate change. 6 While mean values of precipitation, and other ecosystem drivers, are typically used to predict function at the level of whole ecosystems, our results show that more complex measures of environmental variability may be required to understand ecosystem function, and to increase the accuracy of predictions of ecosystem responses to global change.

Projected global change will increase the level of land-use and environmental stressors such as drought and grazing, particularly in drylands. Still, combined effects of drought and grazing on plant production are poorly understood, thus hampering adequate projections and development of mitigation strategies. We used a large, cross-continental database consisting of 174 long-term datasets from >30 dryland regions to quantify ecosystem responses to drought and grazing with the ultimate goal to increase functional understanding in these responses. Two key aspects of ecosystem stability, resistance to and recovery after a drought, were evaluated based on standardized and normalized aboveground net primary production (ANPP) data. Drought intensity was quantified using the standardized precipitation index. We tested effects of drought intensity, grazing regime (grazed, ungrazed), biome (grassland, shrubland, savanna) or dominant life history (annual, perennial) of the herbaceous layer to assess the relative importance of these factors for ecosystem stability, and to identify predictable relationships between drought intensity and ecosystem resistance and recovery. We found that both components of ecosystem stability were better explained by dominant herbaceous life history than by biome. Increasing drought intensity (quasi-) linearly reduced ecosystem resistance. Even though annual and perennial systems showed the same response rate to increasing drought intensity, they differed in their general magnitude of resistance, with annual systems being ca. 27% less resistant. In contrast, systems with an herbaceous layer dominated by annuals had substantially higher postdrought recovery, particularly when grazed. Combined effects of drought and grazing were not merely additive but modulated by dominant life history of the herbaceous layer. To the best of our knowledge, our study established the first predictive, cross-continental model between drought intensity and drought-related relative losses in ANPP, and suggests that systems with an herbaceous layer dominated by annuals are more prone to ecosystem degradation under future global change regimes.

We analyzed data sets on phytomass production, basal cover, and monthly precipitation of a semiarid grassland in South Africa for good, medium, and poor rangeland condition (a) to investigate whether phytomass production per unit of basal cover differed among rangeland conditions, (b) to quantify the time scales of a carryover effect from production in previous months, and (c) to construct predictive models for monthly phytomass. Finally, we applied the best models to a 73-year data set of monthly precipitation data to study the long-term variability of grassland production. Our results showed that mean phytomass production per unit of basal cover did not vary significantly among the rangeland conditions-that is, vegetated patches in degraded grassland have approximately the same production as vegetated patches in grassland in good condition. Consequently, the stark decline in production with increasing degradation is a first-order effect of reduced basal area. Current-year precipitation accounted for 64%, 62%, and 36% of the interannual variation in phytomass production for good, medium, and poor condition, respectively. We found that 61%, 68%, and 33%, respectively, of the unexplained variation is related to a memory index that combines mean monthly temperature and a memory of past precipitations. We found a carryover effect in production from the previous 4 years for grassland in good condition and from the previous 1 or 3S month for grassland in medium and poor condition. The memory effect amplified the response of production to changes in precipitation due to alternation of prolonged periods of dry or wet years/months at the time scale of the memory. The interannual variability in phytomass production per unit basal cover (coefficient of variation [CV] ϭ 0.42-0.50 for our 73-year prediction, CV ϭ 0.57-0.71 for the 19-year data) was greater than the corresponding temporal variability in seasonal rainfall (CV ϭ 0.29).

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