Significance Plants have evolved diverse life history strategies to succeed in Earth’s varied environments. Some species grow quickly, produce copious seeds, and die within a few weeks. Other species grow slowly and rarely produce seeds but live thousands of years. We show that simple morphological measurements can predict where a species falls within the global range of life history strategies: species with large seeds, long-lived leaves, or dense wood have population growth rates influenced primarily by survival, whereas individual growth and fecundity have a stronger influence on the dynamics of species with small seeds, short-lived leaves, or soft wood. This finding increases the ability of scientists to represent complex population processes with a few easily measured character traits.

In many terrestrial ecosystems, variation in aboveground net primary production (ANPP) is positively correlated with variation in interannual precipitation. Global climate change will alter both the mean and the variance of annual precipitation, but the relative impact of these changes in precipitation on mean ANPP remains uncertain. At any given site, the slope of the precipitation‐ANPP relationship determines the sensitivity of mean ANPP to changes in mean precipitation, whereas the curvature of the precipitation‐ANPP relationship determines the sensitivity of ANPP to changes in precipitation variability. We used 58 existing long‐term data sets to characterize precipitation‐ANPP relationships in terrestrial ecosystems and to quantify the sensitivity of mean ANPP to the mean and variance of annual precipitation. We found that most study sites have a nonlinear, saturating relationship between precipitation and ANPP, but these nonlinearities were not strong. As a result of these weak nonlinearities, ANPP was nearly 40 times more sensitive to precipitation mean than variance. A 1% increase in mean precipitation caused a −0.2% to 1.8% change in mean ANPP, with a 0.64% increase on average. Sensitivities to precipitation mean peaked at sites with a mean annual precipitation near 500 mm. Changes in species composition and increased intra‐annual precipitation variability could lead to larger ANPP responses to altered precipitation regimes than predicted by our analysis.

Use policyThe full-text may be used and/or reproduced, and given to third parties in any format or medium, without prior permission or charge, for personal research or study, educational, or not-for-prot purposes provided that:• a full bibliographic reference is made to the original source • a link is made to the metadata record in DRO • the full-text is not changed in any way The full-text must not be sold in any format or medium without the formal permission of the copyright holders.Please consult the full DRO policy for further details. Abstract. Understanding how biotic mechanisms confer stability in variable environments is a fundamental quest in ecology, and one that is becoming increasingly urgent with global change. Several mechanisms, notably a portfolio effect associated with species richness, compensatory dynamics generated by negative species covariance and selection for stable dominant species populations can increase the stability of the overall community. While the importance of these mechanisms is debated, few studies have contrasted their importance in an environmental context. We analyzed nine long-term data sets of grassland species composition to investigate how two key environmental factors, precipitation amount and variability, may directly influence community stability and how they may indirectly influence stability via biotic mechanisms. We found that the importance of stability mechanisms varied along the environmental gradient: strong negative species covariance occurred in sites characterized by high precipitation variability, whereas portfolio effects increased in sites with high mean annual precipitation. Instead of questioning whether compensatory dynamics are important in nature, our findings suggest that debate should widen to include several stability mechanisms and how these mechanisms vary in importance across environmental gradients.

Climate gradients shape spatial variation in the richness and composition of plant communities. Given future predicted changes in climate means and variability, and likely regional variation in the magnitudes of these changes, it is important to determine how temporal variation in climate influences temporal variation in plant community structure. Here, we evaluated how species richness, turnover, and composition of grassland plant communities responded to interannual variation in precipitation by synthesizing long-term data from grasslands across the United States. We found that mean annual precipitation,(MAP) was a positive predictor of species richness across sites, but a positive temporal relationship between annual precipitation and richness was only evident within two sites with low MAP. We also found higher average rates of species turnover in dry sites that in turn had a high proportion of annual species, although interannual rates of species turnover were surprisingly high across all locations. Annual species were less abundant than perennial species at nearly all sites, and our analysis showed that the probability of a species being lost or gained from one year to the next increased with decreasing species abundance. Bray-Curtis dissimilarity from one year to the next, a measure of species composition change that is influenced mainly by abundant species, was insensitive to precipitation at all sites. These results suggest that the richness and turnover patterns we observed were driven primarily by rare species, which comprise the majority of the local species pools at these grassland sites. These findings are consistent with the idea that short-lived and less abundant species are more sensitive to interannual climate variability than longer-lived and more abundant species. We conclude that, among grassland ecosystems, xeric grasslands are likely to exhibit the greatest responsiveness of community composition (richness and turnover) to predicted future increases in interannual precipitation variability. Over the long-term, species composition may shift to reflect spatial patterns of mean precipitation; however, perennial-dominated systems will be buffered against rising interannual variation, while systems that have a large number of rare, annual species will show the greatest temporal variability in species composition in response to rising interannual variability in precipitation.

Abstract. Expected increases in interannual precipitation variability due to climate change will lead to increases in the variability of primary production, with potentially important consequences for natural resource management. Previous work has suggested that various biotic and abiotic processes might amplify or buffer variation in production in response to variation in precipitation. In particular, production to rain variability ratios (PRVR), the coefficient of variation of production divided by the coefficient of variation of precipitation, indicate that production is often relatively more variable than precipitation. We used 37 long-term data sets from grasslands across the globe to test how future increases in precipitation variability might alter the variability of aboveground net primary production (ANPP). We demonstrate that PRVR is not a useful metric: it is predicted by a site's precipitation-production relationship and a PRVR greater than 1 need not imply that increases in the variability of ANPP will be disproportionately greater than increases in precipitation variability. Instead, it is the form of the precipitation-ANPP relationship that determines how increases in precipitation variability will impact ANPP variability. We fit linear, lag effect, and nonlinear precipitation-ANPP relationships to each data set. At most sites, the precipitation-ANPP relationship is weakly nonlinear, though the lag effect model, incorporating previous year ANPP, performed best at several sites. To test whether the three models project differences in the response of ANPP to future increases in precipitation variability, we directly perturbed the observed precipitation time series and quantified the results of this perturbation on ANPP variability. Under simple linear or lag effect models, relative increases in ANPP variability were always equal to the relative increases in precipitation variability. When we modeled ANPP as a nonlinear, saturating function of precipitation, projected increases in ANPP variability were disproportionately high, with production dropping more in dry years than it increases in wet years. In six cases, increases in ANPP variability were twice as large as increases in precipitation variability. Based on Akaike model weights, a 5% increase in precipitation variability would cause a 6.3% increase in ANPP variability on average.