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标题：Effect of latitude and mountain height on the timberline (Betula pubescens ssp. czerpanovii) elevation along the central Scandinavian mountain range
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作者：Arvid Odland
期刊名称：Fennia - International Journal of Geography
印刷版ISSN：1798-5617
出版年度：2015
卷号：193
期号：2
页码：260-270
语种：English
出版社：Geographical Society of Finland
摘要：Previously published isoline maps of Fennoscandian timberlines show that their highest elevations lie in the high mountain areas in central south Norway and from there the limits decrease in all directions. These maps are assumed to show differences in “climatic forest limits”, but the isoline patterns indicate that factors other than climate may be decisive in most of the area. Possibly the effects of ‘massenerhebung’ and the “summit syndrome” may locally have major effects on the timberline elevation. The main aim of the present study is to quantify the effect of latitude and mountain height on the regional variation of mountain birch timberline elevation. The study is a statistical analysis of previous published data on the timberline elevation and nearby mountain height. Selection of the study sites has been stratified to the Scandinavian mountain range (the Scandes) from 58 to 71 o N where the timberlines reach their highest elevations. The data indicates that only the high mountain massifs in S Norway and N Sweden are sufficiently high to allow birch forests to reach their potential elevations. Stepwise regression shows that latitude explains 70.9% while both latitude and mountain explain together 89.0% of the timberline variation. Where the mountains are low (approximately 1000 m higher than the measured local timberlines) effects of the summit syndrome will lower the timberline elevation substantially and climatically determined timberlines will probably not have been reached. This indicates that models of future timberlines and thereby the alpine area extent in a warmer world may result in unrealistic conclusions without taking account of local mountain heights.
关键词：Scandinavia;Forest limit;Multiple regression;Ecology;Global warming;Massenerhebung