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Colony formation in the cyanobacterium Microcystis
Open accessAbstract
Morphological evolution from a unicellular to multicellular state provides greater opportunities for organisms to attain larger and more complex living forms. As the most common freshwater cyanobacterial genus, Microcystis is a unicellular microorganism, with high phenotypic plasticity, which forms colonies and blooms in lakes and reservoirs worldwide. We conducted a systematic review of field studies from the 1990s to 2017 where Microcystis was dominant. Microcystis was detected as the dominant genus in waterbodies from temperate to subtropical and tropical zones. Unicellular Microcystis spp. can be induced to form colonies by adjusting biotic and abiotic factors in laboratory. Colony formation by cell division has been induced by zooplankton filtrate, high Pb 2+ concentration, the presence of another cyanobacterium (Cylindrospermopsis raciborskii), heterotrophic bacteria, and by low temperature and light intensity. Colony formation by cell adhesion can be induced by zooplankton grazing, high Ca 2+ concentration, and microcystins. We hypothesise that single cells of all Microcystis morphospecies initially form colonies with a similar morphology to those found in the early spring. These colonies gradually change their morphology to that of M. ichthyoblabe, M. wesenbergii and M. aeruginosa with changing environmental conditions. Colony formation provides Microcystis with many ecological advantages, including adaption to varying light, sustained growth under poor nutrient supply, protection from chemical stressors and protection from grazing. These benefits represent passive tactics responding to environmental stress. Microcystis colonies form at the cost of decreased specific growth rates compared with a unicellular habit. Large colony size allows Microcystis to attain rapid floating velocities (maximum recorded for a single colony, ∼ 10.08 m h−1) that enable them to develop and maintain a large biomass near the surface of eutrophic lakes, where they may shade and inhibit the growth of less‐buoyant species in deeper layers. Over time, accompanying species may fail to maintain viable populations, allowing Microcystis to dominate. Microcystis blooms can be controlled by artificial mixing. Microcystis colonies and non‐buoyant phytoplankton will be exposed to identical light conditions if they are evenly distributed over the water column. In that case, green algae and diatoms, which generally have a higher growth rate than Microcystis, will be more successful. Under such mixing conditions, other phytoplankton taxa could recover and the dominance of Microcystis would be reduced. This review advances our understanding of the factors and mechanisms affecting Microcystis colony formation and size in the field and laboratory through synthesis of current knowledge. The main transition pathways of morphological changes in Microcystis provide an example of the phenotypic plasticity of organisms during morphological evolution from a unicellular to multicellular state. We emphasise that the mechanisms and factors influencing competition among various close morphospecies are sometimes paradoxical because these morphospecies are potentially a single species. Further work is required to clarify the colony‐forming process in different Microcystis morphospecies and the seasonal variation in this process. This will allow researchers to grow laboratory cultures that more closely reflect field morphologies and to optimise artificial mixing to manage blooms more effectively.
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Metadata
- Authors
- Xiao, M., Li, M., & Reynolds, C. S.
- Venue
- Biological Reviews
- Publisher
- Wiley
- Published
- 2018-02-22
- Volume
- 93
- Issue
- 3
- Pages
- 1399-1420
- ISSN
- 0006-3231 (ISSN-L)
- 1464-7931
- 1469-185X
- OpenAlex
- W2792815161
- Cited by
- 382
- References
- 167
- FWCI
- 4.04
- Type
- journal article
- Language
- en
- Open access
- hybrid
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Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
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Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.