Loading…
Cyanobacterial dominance in lakes
Cyanobacterial dominance in lakes has received much attention in the past because of frequent bloom formation in lakes of higher trophic levels. In this paper, underlying mechanisms of cyanobacterial dominance are analyzed and discussed using both original and literature data from various shallow mi...
Saved in:
Published in: | Hydrobiologia 2000-11, Vol.438 (1-3), p.1-12 |
---|---|
Main Authors: | , |
Format: | Article |
Language: | English |
Subjects: | |
Online Access: | Get full text |
Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Summary: | Cyanobacterial dominance in lakes has received much attention in the past because of frequent bloom formation in lakes of higher trophic levels. In this paper, underlying mechanisms of cyanobacterial dominance are analyzed and discussed using both original and literature data from various shallow mixed and deep stratifying lakes from temperate and (sub)tropical regions. Examples include all four ecotypes of cyanobacteria sensu Mur et al. (1993), because their behavior in the water column is entirely different. Colony forming species (Microcystis) are exemplified from the large shallow Tai Hu, China. Data from a shallow urban lake, Alte Donau in Austria are used to characterize well mixed species (Cylindrospermopsis), while stratifying species (Planktothrix) are analyzed from the deep alpine lake Mondsee. Nitrogen fixing species (Aphanizomenon) are typified from a shallow river-run lake in Germany. Factors causing the dominance of one or the other group are often difficult to reveal because several interacting factors are usually involved which are not necessarily the same in different environments. Strategies for restoration, therefore, depend on both the cyanobacterial species involved and the specific causing situation. Some uncertainty about the success of correctives, however, will remain due to the stochastic nature of the events and pathways leading to cyanobacterial blooms. Truly integrated research programs are required to generate predictive models capable of quantifying key variables at appropriate spatial and temporal scales.[PUBLICATION ABSTRACT] |
---|---|
ISSN: | 0018-8158 1573-5117 |
DOI: | 10.1023/A:1004155810302 |