Little is known about the relationships between treeline elevation and climate at regional and local scales. It is compelling to fill this research gap with data from the Tibetan Plateau where some of the highest alpine treelines in the world are found. This research question partially results from the lack of in situ temperature data at treeline sites. Herein, treeline variables (e.g., elevation, topography, tree species) and temperature data were collected from published investigations performed during this decade on the Tibetan Plateau. Temperature conditions near treeline sites were estimated using global databases and these estimates were corrected by using in situ air temperature measurements. Correlation analyses and generalized linear models were used to evaluate the effects of different variables on treeline elevation including thermal (growing-season air temperatures) and non-thermal (latitude, longitude, elevation, tree species, precipitation, radiation) factors. The commonality analysis model was applied to explore how several variables (July mean temperature, elevation of mountain peak, latitude) were related to treeline elevation. July mean temperature was the most significant predictor of treeline elevation, explaining 55% of the variance in treeline elevation across the Tibetan Plateau, whereas latitude, tree species, and mountain elevation (mass-elevation effect) explained 30% of the variance in treeline elevation. After considering the multicollinearity among predictors, July mean temperature (largely due to the influence of minimum temperature) still showed the strongest association with treeline elevation. We conclude that the coupling of treeline elevation and July temperature at a regional scale is modulated by non-thermal factors probably acting at local scales. Our results contribute towards explaining the decoupling between climate warming and treeline dynamics.
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