Seasonal Response of Grasslands to Climate Change Figure 4. Trends in monthly mean temperature (left) and precipitation (right) on the Tibetan Plateau between 1982 and 2006. Gray areas indicate regions, for which no significant trends were detected by the Mann-Kendall test at p,0.1. doi:10.1371/journal.pone.0049230.g004 to the data, and the slope of the resulting regression equation was used to illustrate the rate of change over the study period. For the Mann-Kendall test, we rejected the hypothesis of no trend in the time series at an error probability level of 0.1. Identification of Climatic Drivers Partial Least Squares (also known as Projection to Latent Structures; PLS) regression was applied for screening for statistical relationships between independent climate variables and depen- dent vegetation variables. This method first determines latent factors, a variant of principal components, to reduce the dimensionality of the independent (and optionally also the dependent) variables [37]. It then uses these factors as independent factors in a linear model to explain variation in the dependent variables. This method is frequently used in remote sensing [38– 40], and has recently been shown to have potential for analysis of high-resolution climate data [41–43]. PLOS ONE | www.plosone.org 5 November 2012 | Volume 7 | Issue 11 | e49230