## 4 Discussion  
Alpine grasslands on the Tibetan Plateau are extremely sensitive to climate variation and human activities because of their vulnerability and severe physical conditions in this region [5]. The ongoing climate warming and historical overgrazing are considered to be main drivers for alpine pasture degradation there [4; 8]. Adaptive strategies and sustainable policies are increasingly called for with respect to alpine pasture conservation faced in a developing Tibet [6]. In addition, it is a big challenge to identify, disentangle, and assess the relative contribution of climate and anthropogenic variables to variability in ecosystem functionality, but it is also increasingly required [33]. [PERSON] et al. [34] proposed a method of defining land capability units coupled with the normalized difference vegetation index (NDVI) to distinguish natural variables from anthropogenic influences. Similarly, [PERSON] et al. [35] identified and assessed vegetation changes that were mainly induced by human cavities in temperate grasslands in Inner Mongolia using a temporal NDVI residual trend method. In two recent studies, [PERSON] et al. [8] and [PERSON] et al. [20] accepted the difference between potential and actual productivity of alpine grasslands that were simulated by theoretical and remote sensing-based models, respectively, to represent the intensity of human influences. For example, [PERSON] et al. [20] reported that 61.2% of the total grassland area experienced restoration from 2001 to 2013 on the Qinghai-Tibetan Plateau and that human activities, climate variation, and their combined effect accounted for 28.6%, 12.8% and 19.9% of the restored alpine grasslands with increasing productivity. However, a major shortcoming still remains in the hypotheses of both [PERSON] et al. [8] and [PERSON] et al. [20] in that grassland productivity dynamics are only affected by climate and human activities, with the potential plant regrowth within the same season after livestock grazing being ignored. A multi-site survey with fenced versus grazed paired plots across the northern Tibetan Plateau also found that the differences in aboveground productivity were not only controlled by climate variables but also influenced by grazing management and closely related to plant community properties [13; 36; 37]. Therefore, the proportion of grassland productivity consumed by livestock should be included in remote-sensing based models for simulating grassland productivity, to represent the actual grassland productivity that has really been influenced by both climate change and human activities. [PERSON] et al. [21] proposed a modified framework for assessing the climate and human impacts on grassland productivity on the Tibetan Plateau, in which the proportion of productivity appropriated by human society was estimated from current-year livestock inventories and meat production with specific transform coefficients for yak and sheep, respectively. Thus, the difference between potential and actual productivity, stimulated by theoretical (mechanism) models and by remote-sensing models, respectively, can be used not only to indicate the relative contribution of natural and anthropogenic factors, but also to reasonably direct the livestock regulation under differential climate change scenarios. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Mean** & **Trend** & **The Target Area (km2)** & **The Percentage (\%)** \\ \hline \multirow{3}{*}{=0} & \textgreater{}0 & 0 & 0 \\  & \textgreater{}0 & 0 & 0 \\  & \textgreater{}0 & 0 & 0 \\ \hline \multirow{3}{*}{\(>\)0} & \textgreater{}0 & 405,349 & 58.74 \\  & \textgreater{}0 & 0 & 0 \\  & \textless{}0 & 71,162 & 10.31 \\ \hline \multirow{3}{*}{\(<\)0} & \textgreater{}0 & 91,648 & 13.28 \\  & \textgreater{}0 & 0 & 0 \\ \cline{1-1}  & \textless{}0 & 112,974 & 16.37 \\ \hline \hline \end{tabular}
\end{table}