Cyanobacterial blooms: Statistical models describing risk factors for national-scale lake assessment and lake management

Abstract

The NERC and CEH trade marks and logos ('the Trademarks') are registered trademarks of NERC in the UK and other countries, and may not be used without the prior written consent of the Trademark owner. . Surprisingly, the models developed reveal that nutrient concentrations are not the primary explanatory variable; water colour and alkalinity were more important. However, given suitable environments (low colour, neutral-alkaline waters), cyanobacteria do increase with both increasing retention time and increasing TP concentrations, supporting the observations that cyanobacteria are one of the most visible symptoms of eutrophication, particularly in warm, dry summers. The models can contribute to the assessment of risks to public health, at a regional-to national level, helping target lake monitoring and management more cost-effectively at those lakes at highest risk of breaching World Health Organisation guideline levels for cyanobacteria in recreational waters. The models also inform restoration options available for reducing cyanobacterial blooms, indicating that, in the highest risk lakes (alkaline, low colour lakes), risks can generally be lessened through management aimed at reducing nutrient loads and increasing flushing during summer.

How to mitigate cyanobacterial blooms and cyanotoxin production in eutrophic water reservoirs?

“…prefer turbulent conditions (Scheffer et al, 1997;Lindenschmidt & Chorus, 1998) and can form toxigenic perennial blooms in shallow, flow-through lakes and dam reservoirs (Grabowska et al, 2014;. As reported by Carvalho et al (2011) and Romo et al (2013), longer water residence time increased total cyanobacteria biomass, like in the case of Microcystis populations and MC concentrations in the lakes studied, with the low frequency of water-level changes and small flushing. Large, buoyant colonies of Microcystis spp.…”

“…As reported by Reynolds & Lund (1988), the hydraulic retention time of lakes can be decisive in phytoplankton development. According to the modelling study of Elliott (2010) and statistically elaborated data from 134 lakes (Carvalho et al, 2011), the water retention time is one of the most important factors influencing cyanobacteria mass development. In shallow eutrophic lakes and reservoirs, both waterlevel management and natural flushing have an impact on the trophic status of water bodies; therefore, appropriate water-level management can be a decisive element in lake responses, especially (Coops & Hosper, 2002;Coops et al, 2003;Verspagen et al, 2006;Haldna et al, 2008), where cyanobacterial blooms are a common negative phenomenon (Ballot et al, 2010;Kobos et al, 2013;Pawlik-Skowronska et al, 2013).…”

Strength and uncertainty of phytoplankton metrics for assessing eutrophication impacts in lakes

“…It may be that, unlike the other metrics, at a broad lake scale, lakes either have cyanobacteria or do not (e.g. low alkalinity lakes, Carvalho et al, 2011). Lakes that do not have cyanobacteria clearly have little seasonal or inter-annual variability in cyanobacteria.…”

Causal analysis of phytoplankton development in a small humic lake using structural equation modelling

“…Yet with LMM it is not necessary to split the data in any way, but all gathered information can be efficiently used (so called 'borrowing strength theory'). Despite this clear advantage over simple regression, for the review only seven LMM papers for phytoplankton was found, of which two were actually using a non-linear, so called general additive method (Carvalho et al 2011, Salmaso et al 2012). The hierarchical model for chlorophyll a introduced by Malve and Qian (2006) was used as a basis for the LLR modelling tool, which has been developed to ease the use of models in WFD-related management of lakes.…”