Predicting interactions between wetland vegetation and the soil-water and surface-water environment using diversity, abundance and attribute values

Issue Title: Theme: Macrophytes in Aquatic Ecosystems: From Biology to Management Proceedings of the 11th International Symposium on Aquatic Weeds, European Weed Research Society This study investigated the response of freshwater wetland vegetation to hydrological driving factors by assessing collec...

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Bibliographic Details
Published in:Hydrobiologia 2006-10, Vol.570 (1), p.189-196
Main Authors: KENNEDY, M. P, MURPHY, K. J, GILVEAR, D. J
Format: Article
Language:English
Subjects:
Animal and plant ecology
Animal, plant and microbial ecology
Aquatic ecosystems
Aquatic plants
Biological and medical sciences
Earth sciences
Earth, ocean, space
Exact sciences and technology
Fundamental and applied biological sciences. Psychology
General aspects
Hydrology
Hydrology. Hydrogeology
Synecology
Vegetation
Wetlands
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Summary:Issue Title: Theme: Macrophytes in Aquatic Ecosystems: From Biology to Management Proceedings of the 11th International Symposium on Aquatic Weeds, European Weed Research Society This study investigated the response of freshwater wetland vegetation to hydrological driving factors by assessing collective vegetation variables, traits of dominant plant populations and hydrological and hydrochemical variables, repeat-sampled within wetland sites across Scotland and northern England. Sampling was conducted at 55 permanent sample stations located along 11 independent transects. Eco-hydrological interactions were investigated using a regression-based modelling approach. Facets of the water-table dynamic (e.g., level of drawdown, level of fluctuation), along with vegetation abundance (e.g., biomass, stem density) and diversity (e.g., species richness) values, were used to build predictive models. Of the models predicting vegetation characteristics, the greatest predictive power was R ^sup 2^ = 0.67 (p < 0.001) for a model predicting stem density (m^sup -2^). Conversely, vegetation variables proved useful for predicting characteristics of the water-table environment. In this instance, the greatest predictive power was R ^sup 2^ = 0.79 (p < 0.001) for a model predicting minimum water table level (i.e. maximum level of drawdown). The models were tested using data collected during 2000 from repeat sites and independent sites. This approach might be successfully applied for the purposes of integrated eco-hydrological management and monitoring of freshwater wetland vegetation.[PUBLICATION ABSTRACT]
ISSN:0018-8158
1573-5117
DOI:10.1007/s10750-006-0191-3