Seasonal Response of Grasslands to Climate Change Figure 7. Results from Partial Least Squares regression analysis for one pixel of the analysis grid, showing results for the beginning of the growing season (BGS). Green bars indicate variables, for which a positive correlation with late BGS dates was determined (possibly indicating a delaying effect of high temperatures or precipitation during this month). Red bars indicate a negative correlation (indicating an advancing effect of high temperatures or rainfall). Gray bars are drawn for variables, for which the variable-importance-in-the-projection score was,0.8, indicating that the variable is not important. This color scheme is used for all subsequent maps. doi:10.1371/journal.pone.0049230.g007 Trends in Vegetation and Climate Temperature showed increasing trends throughout much of the Tibetan Plateau, for most months of the year (Fig. 4).In particular in the northern and western regions, these trends were significant according to the Mann-Kendall test (at p,0.1). In the areas of densest vegetation, trends were mostly not significant, and in October and May, small regions in the southeast of the plateau even showed a significantly decreasing trend. Where significant increasing trends were present, these mostly ranged around+1K per decade (Fig. 4). For precipitation, which exhibits stronger interannual variation than temperature, few significant trends were detected (Fig. 4). Somewhat consistent patterns across larger areas were slight precipitation increases in January, May and August.In some regions of the southern plateau, precipitation decreased in August and September. Trends in mean annual temperature and total annual precipitation were significant throughout most of the Tibetan Plateau (Fig. 5). Most of the study region, with the exception of the south-eastern corner, showed significant temperature increases by between 0 and+1.5K per decade. Slightly increasing annual precipitation totals of up to+60 mm per decade were found throughout approximately half of the study region, while a small region in the south-west of the plateau exhibited significant precipitation losses over time (Fig. 5). For the timing of early-season phenological stages, significant trends were detected for few pixels of the analysis grids. Green-up and the beginning of the growing season showed slight delays in some parts of the central plateau over the 25-year period that was analyzed, but these were rarely significant (Fig. 6). Grassland maturity, senescence and the end of the growing season predominantly showed advancing trends, which in many places were significant. For 16% of the plateau area, advances in senescence and end of the season occurred at a rate of more than one week per decade, and for 4% of the study region these stages advanced by more than two weeks per decade (Fig. 6). For NDVI values for all growth stages, most pixels of the analysis grid showed significant increases over time, mostly ranging around+0.05 NDVI points per decade for the green-up, fast growth, maturity and senescence phases (Fig. 6). Climatic Drivers of Phenology Partial Least Squares regressions were carried out for all 1021 pixels of the analysis grid. Figure 7shows the typical output of such an analysis. As noted in the methods description, the PLS procedure produces two relevant metrics for each independent variable: the VIP score, which indicates the importance of a variable in the PLS model, and the model coefficient, which quantifies the effect. In the diagram, bars corresponding to independent variables whose VIP score is below the threshold of 0.8 are shown in grey. The remaining bars are colored in red for negative model coefficients and in green for positive ones (Fig. 7). Such outputs were produced for each pixel and mapped according to the color code used in the example figure (Fig. 7). In Figures 8, 9, 10, 11 and Figures S1, S2, S3 in the appendix, shades of green and red indicate the strength of the effects. As in the PLOS ONE | www.plosone.org 8 November 2012 | Volume 7 | Issue 11 | e49230