Spatial and temporal variability in the relationship ...

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Bibra and. North Lakes dry up naturally in summer, while the water level of in Lake Monger is artificially main- tained during summer. The amount of daylight.
Environ Monit Assess DOI 10.1007/s10661-012-3031-0

Spatial and temporal variability in the relationship between cyanobacterial biomass and microcystins Som Cit Sinang & Elke S. Reichwaldt & Anas Ghadouani

Received: 27 May 2012 / Accepted: 27 November 2012 # Springer Science+Business Media Dordrecht 2012

Abstract The increasing incidence of toxic cyanobacterial blooms, together with the difficulties to reliably predict cyanobacterial toxin (e.g. microcystins) concentration, has created the need to assess the predictive ability and variability of the cyanobacterial biomass– microcystin relationship, which is currently used to assess the risk to human and ecosystems health. To achieve this aim, we assessed the relationship between cyanobacterial biomass and microcystin concentration on a spatiotemporal scale by quantifying the concentration of cyanobacterial biomass and microcystin in eight lakes over 9 months. On both a temporal and spatial scale, the variability of microcystin concentration exceeded that of cyanobacterial biomass by up to four times. The relationship between cyanobacterial biomass and microcystin was weak and site specific. The variability of cyanobacterial biomass only explained 25 % of the variability in total microcystin concentration and

7 % of the variability of cellular microcystin concentration. Although a significant correlation does not always imply real cause, the results of multiple linear regression analysis suggest that the variability of cyanobacterial biomass and cellular microcystin concentration is influenced by salinity and total phosphorus, respectively. The weak cyanobacterial biomass–microcystin relationship, coupled with the fact that microcystin was present in concentrations exceeding the WHO drinking water guidelines (1 μgL−1) in most of the collected samples, emphasizes the high risk of error connected to the traditional indirect microcystin risk assessment method. Keywords Cyanobacterial variability . Microcystin variability . Microcystin content . Cyanobacterial biomass–microcystin relationship . Total phosphorus

Introduction S. C. Sinang : E. S. Reichwaldt : A. Ghadouani (*) Aquatic Ecology and Ecosystem Studies, School of Environmental Systems Engineering, The University of Western Australia, 35 Stirling Highway, M015, Crawley, WA 6009, Australia e-mail: [email protected] Present Address: S. C. Sinang Faculty of Science and Mathematics, Sultan Idris Education University, 35900 Tanjong Malim, Perak, Malaysia

Toxic cyanobacterial blooms are considered to be a major threat to the safety of water resources throughout the world (Ghadouani and Coggins 2011) as they have serious negative impacts on public health, affect the stability of aquatic ecosystems (Kann and Smith 1999; Ghadouani et al. 2003; Ghadouani et al. 2006; Havens 2007; Chen et al. 2012) and can affect the availability of treated wastewater for water recycling programs (Barrington and Ghadouani 2008; Barrington et al. 2011). Many cyanobacterial genera, including Microcystis, Planktothrix and Anabaena, produce hepatotoxic

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microcystins, which, if ingested, cause both acute and chronical poisoning in animals and humans, especially liver injury (Chorus and Bartram 1999; Carmichael et al. 2001; Ghadouani et al. 2004; Chen et al. 2009). The occurrences of cyanobacteria and microcystin in natural environment are highly variable on both a temporal and regional scale. A comparison of several studies from different regions, including Australia (Kemp 2009), China (Liu et al. 2011), the Czech Republic (Palikova et al. 2011), India (Maske et al. 2010), Ireland (Mooney et al. 2011), Kenya (Kotut et al. 2010), Philippines (Baldia et al. 2003), Saudi Arabia (AlShehri 2010) and Spain (Carrasco et al. 2006) showed that the maximum microcystin concentrations (MC) (as microcystin per unit dry mass) detected during cyanobacterial blooms ranged over 5 orders of magnitude, while maximum cyanobacteria ranged over 3 orders of magnitude. This suggests that the cyanobacterial biomass (CB)–microcystin relationship is highly variable. The amount of microcystin detected during cyanobacterial blooms has been suggested to be a result of cyanobacterial biomass and microcystin production, which are both influenced by environmental factors (Kardinaal and Visser 2005; Zurawell et al. 2005). For instance, microcystin production and cyanobacterial growth were both found to be positively correlated to temperature, salinity and nutrients in some studies (Hobson and Fallowfield 2003; Orr et al. 2004; Tonk et al. 2007; Izydorczyk et al. 2008; T. W. Davis et al. 2009; Rinta-Kanto et al. 2009), while other studies reported a negative or no correlation (Kotak et al. 2000; S. K. Wu et al. 2006; Tonk et al. 2007; Srivastava et al. 2012). As a result of these inconsistencies, the environmental drivers of microcystin and cyanobacterial variability are still highly discussed. It can be assumed that the variability of microcystin concentration in the water is regulated by a combination of environmental factors that act on the metabolic pathway of microcystin production, the rate of cyanobacterial cell division and the dynamics of spatial distribution of cyanobacterial cells. The observed high variability in the occurrence of microcystin during bloom events makes it difficult to develop a reliable risk indicator. Provisional guideline values of 1 and 20 μg microcystin L−1 (expressed as microcystin–LR toxicity equivalents) are set for drinking and recreational water, respectively (WHO 1998, 2003). Even so, several countries have developed their own guideline values for microcystin in drinking water. Notably, this level was set at 1.3 μgL−1 in Australia (NHMRC

2011) and 1.5 μgL−1 in Canada (HC 2010). However, in many countries, including Australia, the current alert level framework for potentially toxic cyanobacterial blooms in recreational and drinking water is based on cyanobacterial biomass or cell counts (ANZECC 2000; NHMRC 2011), which are used as an indicator to indirectly estimate the risk of microcystin contamination. An analysis of 20 peer-reviewed publications that reported on the cyanobacterial biomass–microcystin concentration relationship shows inconsistencies between studies: 55 % of the cases reported a positive relationship (Frank 2002; Graham et al. 2004; J. F. Briand et al. 2005; Znachor et al. 2006; Naselli-Flores et al. 2007; Nasri et al. 2007; E. Briand et al. 2008; Hotto et al. 2008; S. Wu et al. 2008; Okello et al. 2010; Vasconcelos et al. 2011), while 45 % reported no clear relationship (Jean et al. 2000; Baldia et al. 2003; Ballot et al. 2003; Carrasco et al. 2006; Jayatissa et al. 2006; Pavlova et al. 2006; Watzin et al. 2006; Yang et al. 2006; Chen et al. 2009). Furthermore, no trend was observed between the cyanobacterial biomass–microcystin relationships and sampling frequency, range of microcystin concentration, dominant cyanobacterial genus, or measurement units used to express cyanobacterial biomass and microcystin concentration. Therefore, a greater understanding of the variability of the relationship between cyanobacterial biomass and microcystin is needed to assess whether the existing microcystin risk assessment strategy is reliable for protecting public health from the occurrence of highly variable microcystin concentration. In this study, we anticipated that the high spatial and temporal variability in cyanobacterial biomass and microcystin occurrence would lead to a weak cyanobacterial biomass–microcystin relationship. The overall aims of this study were to (a) examine the cyanobacterial biomass–microcystin relationship by comparing the variability of the relationship on spatial and temporal scales in eight urban, shallow, productive systems and (b) identify the potential environmental drivers of cyanobacteria and microcystin variability.

Material and methods Study area Eight urban lakes on the Swan Coastal Plain, which lies between the Darling Scarp and the Indian Ocean

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in Western Australia, were selected for this study: Emu Lake, Jackadder Lake, Lake Monger, Blue Gum Lake, North Lake, Bibra Lake, Little Rush Lake and Yangebup Lake (Fig. 1). All lakes are shallow with a mean depth< 2 m in the majority of lakes, and a stable thermal stratification is usually prevented due to continuous or intermittent wind mixing (J. A. Davis et al. 1993). The hydrologies of these lakes are mainly affected by the rainfall pattern and experience a strong seasonal hydrological cycle. Furthermore, Emu and Yangebup Lakes are also groundwater through-flow systems. Usually, maximum water levels occur in September and October after winter, and minimum water levels occur in March and April after the end of summer months (J. A. Davis et al. 1993). Bibra and North Lakes dry up naturally in summer, while the water level of in Lake Monger is artificially maintained during summer. The amount of daylight Fig. 1 The locations of eight studied lakes on Swan Coastal Plain

received by these lakes varies by season from 10 h in winter to 14 h in summer. Sampling and analyses Weekly or bimonthly samplings were carried out between November 2008 and July 2009. The sampling frequency for each lake depended on the presence of cyanobacterial biomass, as they are a prerequisite for the presence of cyanobacterial toxins. Three lakes were studied for the whole period, while sampling in Bibra Lake, Blue Gum Lake, Little Rush Lake, Lake Monger and North Lake ended earlier as they dried up during summer. On-site measurements and samples were taken at the same point for every sampling occasion, from shore sites with a water depth of 0.6 to 1 m. Water temperature, pH and salinity were measured on-site with a WP-81 probe (TPS Pty Ltd) at a depth of 0.6 m. Grab

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water samples for cyanobacteria, microcystin and total phosphorus (TP) concentration quantification were taken from 0.15 m below the surface to avoid surface scum. Variations in physicochemical variables at different depths are minimal as another study in this lake found no differences between surface and bottom samples (Reichwaldt 2012, unpublished data). Samples were stored immediately in glass bottles in the dark on ice after the collection. Nutrient analyses and quantification of cyanobacteria TP concentrations were analysed with the ascorbic acid method (Clesceri et al. 1998). Cyanobacterial and total phytoplankton chlorophyll-a (chl-a) were measured with a top-bench version of a FluoroProbe (bbe Moldaenke, Germany). The FluoroProbe measures chl-a fluorescence and differentiates divisions of phytoplankton by their specific fluorescence emission spectrum (Beutler et al. 2002). The fluorescence is then transformed through an algorithm and total biomass of each phytoplankton division including cyanobacteria, expressed as chl-a concentration equivalents (in micrograms of chl-a per litre) (Beutler et al. 2002; Ghadouani and Smith 2005). FluoroProbe chl-a measurements were validated against chl-a data of samples extracted according to standard methods (Clesceri et al. 1998) (linear regression analysis: R2 =0.94, N=32, P