|Title||Intra-annual rainfallvariability and grassland productivity: can the past predictthe future|
|Publication Type||Journal Article|
|Year of Publication||2006|
|Authors||Nippert, JB, Knapp, AK, Briggs, JM|
|Keywords||ANPP, Climate change, grassland, Precipitation variability, soil moisture, tallgrass prairie|
Precipitation quantity has been shown to influence grassland aboveground net primary productivity (ANPP) positively whereas experimental increases in of temporal variability in water availability commonly exhibit a negative relationship with ANPP. We evaluated long term ANPP datasets from the Konza Prairie Long Term Ecological Research (LTER) program (1984–1999) to determine if similar relationships could be identified based on patterns of natural variability (magnitude and timing) in precipitation. ANPP data were analyzed from annually burned sites in native mesic grassland and productivity was partitioned into graminoid (principally C4 grasses) and forb (C3 herbaceous) components. Although growing season precipitation amount was the best single predictor of total and grass ANPP (r 2=0.62), several measures of precipitation variability were also significantly and positively correlated with productivity, independent of precipitation amount. These included soil moisture variability, expressed as CV, for June (r 2=0.45) and the mean change in soil moisture between weekly sampling periods in June and August (%wv) (r 2=0.27 and 0.32). In contrast, no significant relationships were found between forb productivity and any of the precipitation variables (p>0.05). A multiple regression model combining precipitation amount and both measures of soil moisture variability substantially increased the fit with productivity (r 2=0.82). These results were not entirely consistent with those of short-term manipulative experiments in the same grassland, however, because soil moisture variability was often positively, not negatively related to ANPP. Differences in results between long and short term experiments may be due to low variability in the historic precipitation record compared to that imposed experimentally as experimental levels of variability exceeded the natural variability of this dataset by a factor of two. Thus, forecasts of ecosystem responses to climate change (i.e. increased climatic variability), based on data constrained by natural and recent historical rainfall patterns may be inadequate for assessing climate change scenarios if precipitation variability in the future is expected to exceed current levels.