A Multivariate Regression Model to Explain the Altitudinal Distribution of Timberlines on the Eurasian Continent

Abstract

The altitude of alpine timberline elevation has been considered to correlate with certain climatic factors. Many related isotherms (e.g., warmest month 10 °C isotherm) have been proposed to explain the altitudinal distribution of alpine timberline at the global scale. However, any climatic index actually has a wide range at the alpine timberline position worldwide. The altitudinal position of the alpine timberline is related to far more than just one climatic factor. Therefore, we developed a multivariable model for timberline elevation variability by collecting data from 473 timberline sites on the Eurasian continent. We analyzed 12 climatic variables that potentially account for timberline variation. Principal component and regression analyses were used to mine four climatic variables. The mean temperature of the warmest month (MTWM), mean temperature of the coldest month (MTCM), climatic continentality (K), and annual precipitation (AP) explained 95% of the variability of timberline elevation. MTWM, MTCM, K, and AP contributed 18%, 41.28%, 34.9%, and 5.82%, respectively, to the altitudinal distribution of alpine timberline on the whole continent; 20%, 44%, 28.86%, and 7.14% in the eastern continent; and 17.71%, 39.79%, 40.21%, and 2.29% in the western continent. We showed that MTWM, MTCM, K, and AP are deterministic factors for the altitudinal distribution of alpine timberline in the Eurasian continent. MTCM and K contributed to explaining the altitudinal distribution of timberline both in the entire, eastern, and western parts of the Eurasian continent. Our research highlights the significance of MTCM for the altitudinal distribution of timberline.