URN zum Zitieren der Version auf EPub Bayreuth: urn:nbn:de:bvb:703-epub-7260-1
Titelangaben
Kienle, David ; Irl, Severin D. H. ; Beierkuhnlein, Carl:
Mass elevation effect and continentality have a stronger impact on global treelines than spatial isolation.
In: Global Ecology and Biogeography.
Bd. 32
(2023)
Heft 7
.
- S. 1087-1097.
ISSN 1466-8238
DOI der Verlagsversion: https://doi.org/10.1111/geb.13689
Volltext
|
|||||||||
Download (2MB)
|
Abstract
Abstract Aim The global relationship between treeline elevation and temperature (or latitude as a proxy) is well established. However, additional large-scale and regional abiotic influences such as mass elevation effect (MEE), continentality and isolation are superimposed onto the latitude-treeline relationship. To quantify these effects, we apply globally applicable measures and test the effects of MEE, an aspect of continental climate and isolation on treeline elevation. Location Global treeline elevations (n = 629). Methods We sampled treeline sites using earth observation. We calculated MEE as the distance to the nearest mountain chain limits. Continentality was assessed by the distance to the nearest coastline. Isolation was calculated by the nearest distance of a mountain chain to another mountain chain within a comparable elevational band. Results The global latitudinal pattern showed a distinct bimodal latitude-treeline elevation relationship. Treeline elevations increased substantially with increased MEE and distance to coastlines while isolation even decreased treeline elevations. Main Conclusions Our study shows a globally consistent effect of MEE and distance to the coastline on treeline elevation, contributing to our basic understanding of large-scale biogeographic processes governing treeline formation. MEE and continentality reduce cloudiness and increase solar radiation, resulting in higher treeline elevations. Isolation effects are not consistent and may be influenced by immigration and speciation. Understanding global treeline formation using comprehensive measures contributes to a better understanding of how environmental conditions determine vegetation boundaries at large spatial scales.