IMHE OpenIR  > Journal of Mountain Science  > Journal of Mountain Science-2017  > Vol14 No.4
Vegetation-based bioindication of humus forms in coniferous mountain forests
Alternative TitleVegetation-based bioindication of humus forms in coniferous mountain forests
Kerstin ANSCHLAG; Dylan TATTI; Niels HELLWIG; Giacomo SARTORI; Jean-Michel GOBAT; Gabriele BROLL
Corresponding AuthorKerstin ANSCHLAG
2017-04
Source PublicationJournal of Mountain Science
ISSN1672-6316
Volume14Issue:4Pages:662-673
Subtype期刊论文
AbstractHumus forms, especially the occurrence and the thickness of the horizon of humified residues (OH), provide valuable information on site conditions. In mountain forest soils, humus forms show a high spatial variability and data on their spatial patterns is often scarce. Our aim was to test the applicability of various vegetation features as proxy for OH thickness. Subalpine coniferous forests dominated by Picea abies (L.) H. Karst. and Larix decidua Mill. were studied in the Province of Trento, Italian Alps, between ca. 900 and 2200 m a.s.l. Braun-Blanquet vegetation relevés and OH thickness were recorded at 152 plots. The vegetation parameters, tested for their suitability as indicators of OH thickness, encompassed mean Landolt indicator values of the herb layer (both unweighted and cover-weighted means) as well as parameters of vegetation structure (cover values of plant species groups) calculated from the relevés. To our knowledge, the predictive power of Landolt indicator values (LIVs)for humus forms had not been tested before.Correlations between OH thickness and mean LIVs were strongest for the soil reaction value, but indicator values for humus, nutrients, temperature and light were also significantly correlated with OH thickness. Generally, weighting with species cover reduced the indicator quality of mean LIVs for OH thickness. The strongest relationships between OH thickness and vegetation structure existed in the following indicators: the cover of forbs (excluding graminoids and ferns) and the cover of Ericaceae in the herb layer. Regression models predicting OH thickness based on vegetation structure had almost as much predictive power as models based on LIVs. We conclude that LIVs analysis can produce fairly reliable information regarding the thickness of the OH horizon and, thus, the humus form. If no relevé data are readily available, a field estimation of the cover values of certain easily distinguishable herb layer species groups is much faster than a vegetation survey with consecutive indicator value analysis, and might be a feasible way of quickly indicating the humus form.
Other AbstractHumus forms, especially the occurrence and the thickness of the horizon of humified residues (OH), provide valuable information on site conditions. In mountain forest soils, humus forms show a high spatial variability and data on their spatial patterns is often scarce. Our aim was to test the applicability of various vegetation features as proxy for OH thickness. Subalpine coniferous forests dominated by Picea abies (L.) H. Karst. and Larix decidua Mill. were studied in the Province of Trento, Italian Alps, between ca. 900 and 2200 m a.s.l. Braun-Blanquet vegetation relevés and OH thickness were recorded at 152 plots. The vegetation parameters, tested for their suitability as indicators of OH thickness, encompassed mean Landolt indicator values of the herb layer (both unweighted and cover-weighted means) as well as parameters of vegetation structure (cover values of plant species groups) calculated from the relevés. To our knowledge, the predictive power of Landolt indicator values (LIVs)for humus forms had not been tested before.Correlations between OH thickness and mean LIVs were strongest for the soil reaction value, but indicator values for humus, nutrients, temperature and light were also significantly correlated with OH thickness. Generally, weighting with species cover reduced the indicator quality of mean LIVs for OH thickness. The strongest relationships between OH thickness and vegetation structure existed in the following indicators: the cover of forbs (excluding graminoids and ferns) and the cover of Ericaceae in the herb layer. Regression models predicting OH thickness based on vegetation structure had almost as much predictive power as models based on LIVs. We conclude that LIVs analysis can produce fairly reliable information regarding the thickness of the OH horizon and, thus, the humus form. If no relevé data are readily available, a field estimation of the cover values of certain easily distinguishable herb layer species groups is much faster than a vegetation survey with consecutive indicator value analysis, and might be a feasible way of quickly indicating the humus form.
KeywordLandolt Indicator Values Oh Horizon Forest Ecosystem Montane Forest Italian Alps
DOI10.1007/s11629-016-4290-y
Indexed BySCI
Language英语
CSCD IDCSCD:5957511
Citation statistics
Cited Times:5[WOS]   [WOS Record]     [Related Records in WOS]
Document Type期刊论文
Identifierhttp://ir.imde.ac.cn/handle/131551/18549
CollectionJournal of Mountain Science_Journal of Mountain Science-2017_Vol14 No.4
Recommended Citation
GB/T 7714
Kerstin ANSCHLAG,Dylan TATTI,Niels HELLWIG,et al. Vegetation-based bioindication of humus forms in coniferous mountain forests[J]. Journal of Mountain Science,2017,14(4):662-673.
APA Kerstin ANSCHLAG,Dylan TATTI,Niels HELLWIG,Giacomo SARTORI,Jean-Michel GOBAT,&Gabriele BROLL.(2017).Vegetation-based bioindication of humus forms in coniferous mountain forests.Journal of Mountain Science,14(4),662-673.
MLA Kerstin ANSCHLAG,et al."Vegetation-based bioindication of humus forms in coniferous mountain forests".Journal of Mountain Science 14.4(2017):662-673.
Files in This Item:
File Name/Size DocType Version Access License
5.pdf(899KB)期刊论文作者接受稿开放获取CC BY-NC-SAView Application Full Text
Related Services
Recommend this item
Bookmark
Usage statistics
Export to Endnote
Google Scholar
Similar articles in Google Scholar
[Kerstin ANSCHLAG]'s Articles
[Dylan TATTI]'s Articles
[Niels HELLWIG]'s Articles
Baidu academic
Similar articles in Baidu academic
[Kerstin ANSCHLAG]'s Articles
[Dylan TATTI]'s Articles
[Niels HELLWIG]'s Articles
Bing Scholar
Similar articles in Bing Scholar
[Kerstin ANSCHLAG]'s Articles
[Dylan TATTI]'s Articles
[Niels HELLWIG]'s Articles
Terms of Use
No data!
Social Bookmark/Share
File name: 5.pdf
Format: Adobe PDF
All comments (0)
No comment.
 

Items in the repository are protected by copyright, with all rights reserved, unless otherwise indicated.