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Characteristics of timberline and treeline altitudinal distribution in Mt. Namjagbarwa and their geographical interpretation

Characteristics of timberline and treeline altitudinal distribution in Mt. Namjagbarwa and their... Different types of vegetation patches are alternately and randomly distributed in a timberline ecotone where the upper limit is the treeline and the lower limit is the timberline. However, most studies on timberline/treeline altitudinal distributions have simplified timberline or treeline as continuous curves and disregarded the fuzziness of timberline/treeline and the randomness of different vegetation patch distributions in a timberline ecotone. To study the altitudinal distribution characteristics of timberline and treeline from the perspective of uncertainty theory, we constructed the timberline and treeline elevation cloud models in Mt. Namjagbarwa in east Himalayas. Subsequently, we established multiple linear regression models by using nine influencing factors, namely, aspect, slope, topographic relief, dryness index, average temperature in January and July, latitude, summit syndrome (represented by the vertical distance from the peak), and snow effect (represented by the nearest distance from the snow) as independent variables, and the elevations of timberline/treeline as dependent variables. Then we compared the contributions of the nine factors in timberline, treeline, and the core and peripheral areas of timberline and treeline. The results show that 1) the timberline/treeline elevation cloud model can represent the overall characteristics (especially the uncertainty) of the altitudinal distributions of the timberline/treeline well. The uncertainty of treeline’s altitudinal distribution is higher than that of timberline (entropy and hyper entropy: 207.59 m and 70.36 m for treeline elevation cloud; entropy and hyper entropy: 191.17 m and 50.13 m for timberline elevation cloud). 2) The influence of climate and topography on timberline and treeline are similar. The average temperature in July has a significant negative correlation with the timberline/treeline elevation in Mt. Namjagbarwa, which is the most critical factor that affects timberline and treeline elevation, explaining the altitudinal distribution of 44.01% timberline and 46.74% treeline. However, the contributions of the nine factors in core and peripheral areas of timberline and treeline area are evidently different. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Journal of Mountain Science Springer Journals

Characteristics of timberline and treeline altitudinal distribution in Mt. Namjagbarwa and their geographical interpretation

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References (84)

Publisher
Springer Journals
Copyright
Copyright © Science Press, Institute of Mountain Hazards and Environment, CAS and Springer-Verlag GmbH Germany, part of Springer Nature 2022
ISSN
1672-6316
eISSN
1993-0321
DOI
10.1007/s11629-021-7214-4
Publisher site
See Article on Publisher Site

Abstract

Different types of vegetation patches are alternately and randomly distributed in a timberline ecotone where the upper limit is the treeline and the lower limit is the timberline. However, most studies on timberline/treeline altitudinal distributions have simplified timberline or treeline as continuous curves and disregarded the fuzziness of timberline/treeline and the randomness of different vegetation patch distributions in a timberline ecotone. To study the altitudinal distribution characteristics of timberline and treeline from the perspective of uncertainty theory, we constructed the timberline and treeline elevation cloud models in Mt. Namjagbarwa in east Himalayas. Subsequently, we established multiple linear regression models by using nine influencing factors, namely, aspect, slope, topographic relief, dryness index, average temperature in January and July, latitude, summit syndrome (represented by the vertical distance from the peak), and snow effect (represented by the nearest distance from the snow) as independent variables, and the elevations of timberline/treeline as dependent variables. Then we compared the contributions of the nine factors in timberline, treeline, and the core and peripheral areas of timberline and treeline. The results show that 1) the timberline/treeline elevation cloud model can represent the overall characteristics (especially the uncertainty) of the altitudinal distributions of the timberline/treeline well. The uncertainty of treeline’s altitudinal distribution is higher than that of timberline (entropy and hyper entropy: 207.59 m and 70.36 m for treeline elevation cloud; entropy and hyper entropy: 191.17 m and 50.13 m for timberline elevation cloud). 2) The influence of climate and topography on timberline and treeline are similar. The average temperature in July has a significant negative correlation with the timberline/treeline elevation in Mt. Namjagbarwa, which is the most critical factor that affects timberline and treeline elevation, explaining the altitudinal distribution of 44.01% timberline and 46.74% treeline. However, the contributions of the nine factors in core and peripheral areas of timberline and treeline area are evidently different.

Journal

Journal of Mountain Science – Springer Journals

Published: Oct 1, 2022

Keywords: Timberline; Treeline; Geographical interpretation; Uncertainty; Influencing factors; Mt. Namjagbarwa

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