Consequences of alternative stable states for short-term model-based control of cyanobacterial blooms

• Published in: Ecological modelling Vol. 491; p. 110671
• Main Authors: Jacobs, Bas, van Voorn, George, van Heijster, Peter, Hengeveld, Geerten M.
• Format: Journal Article
• Language: English
• Published: Elsevier B.V 01.05.2024
• Subjects: algal blooms; Alternative stable states; Bifurcation; Cyanobacterial bloom; Differential equations; lakes; macrophytes; Mitigation; Tipping point; Mitigation; Bifurcation; Cyanobacterial bloom; Alternative stable states; Tipping point; Differential equations
• ISSN: 0304-3800; 1872-7026
• DOI: 10.1016/j.ecolmodel.2024.110671

We explore potential management strategies for short-term mitigation efforts of cyanobacterial blooms informed by process-based dynamic models. We focus on the case where blooms are linked to the existence of alternative stable states, such that, under the same conditions but depending on the past, a lake may be dominated either by cyanobacteria (“blue algae”), causing a harmful algal bloom, or by green algae and macrophytes in a clear water state. Changing conditions may cause the favourable clear water state to disappear through a tipping point, causing the lake to switch rapidly to the turbid cyanobacteria state. At the same time, it may take considerable effort to undo this tipping and return to the favourable state. We identify four different strategies for bloom mitigation in this scenario: Doing nothing, reacting to a bloom, resetting the lake at a later point, and preventing the bloom. We found that these strategies have different cost profiles. The optimal strategy depends on many factors, including the relative costs of blooms and interventions, the time during which the environment favours a bloom and the bifurcation structure that determines where in parameter space blooms appear and disappear. In general, low bloom costs and short bloom times favour not intervening, while high bloom costs favour prevention. In between, waiting for more favourable conditions before resetting to a clear state may be preferable, especially for long bloom times, where constant intervention becomes expensive. Transient dynamics are also relevant, with a trade-off between minimising intervention effort and maximising bloom reversal speed. •Models can assist mitigation of cyanobacterial blooms linked to tipping points.•Different bloom management strategies are possible, each with their own cost profile.•The optimal strategy depends on the relative costs of the bloom and the intervention.•Low bloom costs favour not intervening, while high bloom costs favour prevention.•Moderate bloom costs favour temporarily allowing a bloom before intervention.