the end of the growing season). Only high temperatures in September and October were related to later occurrence of end- of-season phenological stages. High NDVI during maturity and senescence was correlated with low temperatures in May and June, as well as high temperatures during July and September. This may indicate that rapid plant development during early vegetation phases, driven by warm conditions, limits biomass accumulation during later stages. Once plants have reached their mature stage, warm conditions enhance biomass production. Overall, the summer and fall stages of plants have advanced over the past 25 years, accompanied by higher photosynthetic capacity. General Discussion On the Tibetan Plateau, temperature increases over the past 25 years have evidently extended the period, during which temper- atures are conducive to plant growth, yet local vegetation seems unable to exploit these additional thermal resources. Instead, Tibetan grasslands have shown little change in the beginning of the growing season, which in recent years appears to have retreated rather than advanced [28]. In combination with advances in summer and fall stages, these trends have led to a significant shortening of the growing season on the Tibetan Plateau [48]. At the same time, photosynthetic capacity, as indicated by the NDVI, has increased significantly during the same time span. Taken together, these trends in vegetation timing and intensity show that vegetation in this region grows faster and more vigorously as temperatures increase. It also reaches the end of its reproductive cycle earlier than in the past, which may be due to the determinate nature of grassland phenology on the Tibetan Plateau. Plants in this winter-cold climate must complete their annual cycles quickly to escape early frosts. Recent temperature increases have brought about earlier fulfillment of their thermal time requirements, after which plants start senescing. The inability of plants to make full use of available thermal resources hints at a mismatch between current vegetation and climate that is likely due to recent climate change. Current vegetation in most places on the Tibetan Plateau may no longer be the climax vegetation of its environment, and grasses from elsewhere might be better suited to conditions of such places. However, these better adapted plants are not currently available there. Tibetan grasses are largely perennial grasses which predominately propagate vegetatively, so that the speed at which they can extend their habitat through migration is very slow. Under such conditions, it may take many years until a new equilibrium between climate and vegetation is reached. This time lag may increase further if the warming trend of the Tibetan Plateau persists. For vegetation assemblages that are not well adjusted to the climate that they occur in, projecting future change trajectories is difficult. In particular, using general ecological models for such projections is not necessarily a valid approach. Past studies have shown that it is possible to relate ecological indicators, such as net primary productivity or carbon sequestration, to climatic factors [49,50], but such relationships may only be valid for ecosystems in their climax state. Yet most ecosystems will take a considerable amount of time to adapt to a new climate regime, and projections about future shifts in climate indicate that many ecosystems may continue lagging behind their climatic setting for many centuries to come. This prospect raises doubts about climate change impact projection methods based on equilibrium states, such as empirical species distribution modeling [51]or approaches based on generalized ecological indicators, such as Dynamic Vegetation Models [52]. Accurately modeling succession processes in ecosys- tems caused by climate change, in particular the speed, with which Seasonal Response of Grasslands to Climate Change various species can adjust to changing environments, is one of the major challenges in climate change impact projection. It will require detailed understanding of species migration, dispersal and climatic responses during all seasons. Conclusions In principle, climatic conditions on the Tibetan Plateau have become more conducive to plant growth over the past 25 years, and the period of thermally favorable conditions has extended. However, plants have not been able to exploit these resources, showing a shorter and more intense growing season rather than a longer period of active growth. Slow species migration rates in the perennial grasslands of the Tibetan Plateau are likely responsible for a mismatch between climate and vegetation assemblage. The current lack of a vegetation-climate equilibrium state makes it difficult to predict climate change impacts on Tibetan grasslands. Our analysis has shown that changes in the timing of phenological events or vegetation activity cannot easily be attributed to individual climate factors, but arise from a combination of different weather conditions during different parts of the growing season. Even weather conditions during the winter months, when plants are dormant, show correlations with the timing of phenological events, and with vegetation activity, which may be related to vernalization requirements. Partial Least Squares regression has proven to be a valuable tool for detecting complex relationships between vegetation and climate and shows promise for analysis of similar situations elsewhere. Supporting Information Figure S1 Correlations of monthly temperatures (left) and precipitation (right) with the timing of grassland maturity on the Tibetan Plateau, according to Partial Least Squares (PLS) regression. For each variable, pixels for which the variable-importance-in-the-projection score was,0.8 are shown in gray. Pixels with insufficient data for PLS analysis are shown in white. (TIF) Figure S2 Correlations of monthly temperatures (left) and precipitation (right) with vegetation activity during the period of fast growth (as measured by the NDVI) on the Tibetan Plateau, according to Partial Least Squares (PLS) regression. For each variable, pixels for which the variable-importance-in-the-projection score was,0.8 are shown in gray. Pixels with insufficient data for PLS analysis are shown in white. (TIF) Figure S3 Correlations of monthly temperatures (left) and precipitation (right) with vegetation activity during plant maturity (as measured by the NDVI) on the Tibetan Plateau, according to Partial Least Squares (PLS) regression. For each variable, pixels for which the variable-importance-in-the-projection score was,0.8 are shown in gray. Pixels with insufficient data for PLS analysis are shown in white. (TIF) Acknowledgments We are grateful for very extensive and constructive comments by two anonymous reviewers, who have greatly contributed to the value of this paper. We also thank Dr. R. Edward Grumbine from the Key Laboratory of Economic Plants and Biotechnology, Kunming Institute of Botany, for suggestions on paper structure and English writing. PLOS ONE | www.plosone.org 14 November 2012 | Volume 7 | Issue 11 | e49230