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Impact of Climate Change on the Ecology of Algal Blooms [Project #4382] ORDER NUMBER: 4382 DATE AVAILABLE: February 2015 PRINCIPAL INVESTIGATORS: Anna Rigosi, Chaturangi Wickramaratne, Justin D. Brookes, Leon van der Linden, Bas Ibelings, and Cayelan Carey OBJECTIVES The goal of this research was to predict how the risk of cyanobacterial blooms in lakes and reservoirs will vary with potential climate change. Lakes with different morphology and trophic states may have different sensitivities to cyanobacteria and climate change. However, there may be general principles that are globally applicable in determining cyanobacterial growth and control. The objectives were fourfold: 1. Determine how global warming could impact cyanobacterial abundance 2. Determine the relative importance of temperature and nutrients in determining changes in phytoplankton and cyanobacterial abundance 3. Determine how different cyanobacterial taxa respond to temperature and nutrients 4. Propose mitigation strategies to deal with cyanobacteria given the potential warmer temperatures in the future BACKGROUND Cyanobacteria continue to be a concern for the water industry. Toxic cyanobacterial blooms present a considerable risk to drinking water and have major public health, ecological, and economic effects (Codd et al. 2005; Falconer 2005). The taste and odour compounds produced by cyanobacteria compromise water quality and incur additional treatment costs. Cyanobacterial blooms have increased in intensity and frequency as a result of increased nutrient concentrations and the damming of waterways, and utilities increasingly have to deal with the risk of cyanobacterial metabolites. Recent geographic expansion of cyanobacteria has been observed (Jöhnk et al. 2008; Huber et al. 2012) and several studies suggest that increases in temperature due to climate change will increase the development of cyanobacteria relative to other phytoplankton in lakes and reservoirs (Paerl and Huisman 2008; Paerl and Huisman 2009). However, others suggest that nutrient availability could be at least as important as temperature in the increased occurrence of cyanobacterial blooms (Brookes and Carey 2011; Kosten et al. 2012). ©2015 Water Research Foundation. ALL RIGHTS RESERVED. Thus, increased nutrients and temperature are believed to be two of the most important factors driving phytoplankton community composition and the proliferation of cyanobacteria. However, it remains unclear how these two factors interact, if their interaction is dependent on the trophic state of the system, and if other factors such as indirect temperature effects or species interaction are important factors in controlling cyanobacterial dominance. APPROACH Four approaches were used to investigate how climate change, specifically global warming, is likely to affect cyanobacteria: 1. 2. 3. 4. Literature review of factors influencing cyanobacterial growth Statistical analysis of large databases containing data for multiple U.S. lakes Application of Bayesian models to multiple lakes with high temporal resolution data Prediction of cyanobacterial growth in elevated temperatures and nutrients, using deterministic coupled hydrodynamic ecological models The literature review sought evidence detailing the response of cyanobacteria to changes in water temperature, stratification, and nutrient loads, driven by global warming. Field observations, microcosm experiments, long-term data set analysis, and modeling experiments were considered to evaluate the available knowledge on how nutrient and temperature factors interact to drive responses in the phytoplankton community and cyanobacteria. Secondly, a database of more than 1,000 lakes from across the United States was analyzed and the contribution of temperature and nutrients as factors controlling cyanobacterial abundance was examined. In particular, an evaluation was made of 1) which factor, nutrient or temperature, was the most important variable controlling phytoplankton biomass and cyanobacterial biomass; 2) whether there was a synergistic interaction between these two factors; and 3) if this interaction was dependent on trophic state or cyanobacterial taxa composition. A Bayesian network was developed to predict probability of cyanobacterial blooms under combined environmental changes. The network was constructed with ecological data from 20 lakes located at different latitude and characterized by different sizes and trophic states. The Bayesian network was used to predict how different environmental factors change the probability of hazardous blooms forming. From these results, predictions of future climate warming impacts on cyanobacterial biomass could be determined. The fourth approach was to use a coupled hydrodynamic-ecological model to explicitly predict the cyanobacterial response to warming and nutrient load. The model was calibrated and applied to two lakes with different trophic status (Mt Bold reservoir, Australia and Lake Tarawera, New Zealand). Several scenarios were simulated: first increasing temperature alone by 1, 2, 3, or 4 °C; second by increasing or decreasing nutrient loads (20% and 50%); lastly, undertaking a matrix of scenarios including both temperature change and altered nutrient loading. RESULTS/CONCLUSIONS The literature review confirmed that cyanobacterial blooms are favored not by an individual environmental factor, but by a combination of multiple interacting physical, chemical, and biotic drivers. It is evident that although cyanobacterial blooms can be enhanced by increasing ©2015 Water Research Foundation. ALL RIGHTS RESERVED. temperature, there must be sufficient phosphorus and nitrogen to sustain high populations. This supports the proposition that controlling nutrient inputs can limit the growth of nuisance species and negate any advantage cyanobacteria may gain with increased temperature. The review of literature identified a poor knowledge of whether there is an interaction between nutrients and temperature in promoting cyanobacterial blooms. To fill this research gap, three approaches were used. A statistical model is used to identify the main factors driving cyanobacteria development worldwide and how these factors are statistically related, and to assess the probability of bloom occurrence given specific environmental conditions. A deterministic modelling approach is used to develop a generalized model testing lake sensitivity to climate change, simulating various combinations of nutrient loading and warming scenarios. The analysis of a 1,000-lake database revealed that the interaction between temperature and nutrients was not synergistic and that cyanobacterial biovolume was predominantly controlled by nutrients. However, the relative importance of temperature and nutrients varied depending on lake trophic status and cyanobacterial taxon. Nutrients were the most important factor in oligotrophic lakes, temperature was most important in mesotrophic lakes, while the interaction between nutrients and temperature was highly significant in eutrophic lakes. The species of cyanobacteria have varying sensitivities to nutrient and temperature; Anabaena appear more sensitive to nutrients and Microcystis were more sensitive to temperature. Using Bayesian network models, the hypothesis that nutrient remediation could offset the stimulatory effect of increasing temperature on cyanobacteria was explored. The network predicted that a 0.8 °C increase in water temperature (above an initial water temperature of 24 °C) or an increase in total phosphorus from 0.01 mg L-1 to 0.02 mg L-1 would increase the probability of hazardous bloom development by 5% (cyanobacterial abundance > 1x105 cells mL-1). This corresponds to an increase in probability of bloom development to 20%, considering a predicted increase of about 4 °C in water temperature in the next century. A 1-D coupled hydrodynamic-ecological model was applied to dynamically analyze the effect of temperature increase and changes in nutrient inputs on lake physical-chemical variables and consequently on phytoplankton community. Modeling experiments indicated that seasonal variability and the interaction between algal groups (e.g., cyanobacteria and chlorophytes) were significant factors controlling phytoplankton growth and composition. Different results were obtained for eutrophic and oligotrophic systems. In the eutrophic system, where chlorophytes, diatoms and cyanobacteria were developing, the magnitude of peaks of phytoplankton groups was affected, but not their timing. In the oligotrophic system, both peak timing and abundance were affected. Chlorophytes and diatoms were the most abundant groups and cyanobacteria did not develop even in the worst-case scenario (increasing temperature 4 °C and increasing nutrient inputs 50% of the observed value). Moreover, in the eutrophic lake, Mt Bold, an initial decrease in nutrients, if associated with increase in temperature, would favor cyanobacteria with respect to chlorophytes. These results suggest that community composition and competition between phytoplankton groups is a key factor determining lake response to changes in temperature and nutrients. Moreover, only further nutrient reduction (e.g., soluble reactive phosphorous < 0.02 mg L-1) would start to become beneficial for eutrophic systems under warm scenarios. Another major task completed by the research team was the development of an open source deterministic dynamic model simulating multiple phytoplankton groups. The model was calibrated and validated for two lakes of different sizes and trophic states. It showed a good performance predicting total chlorophyll-a concentration and phytoplankton succession, and it is now available to be applied in other lakes or reservoirs worldwide. ©2015 Water Research Foundation. ALL RIGHTS RESERVED. APPLICATIONS/RECOMMENDATIONS In light of increased temperatures, alteration of storm and drought periods, and increased weather variability, the consensus amongst the scientific community and water professionals is that the climate of the planet is changing, which will have serious ramifications for water resources. Water utilities around the world are concerned about the hazards associated with climate change, and particularly about warming temperatures. Cyanobacteria have been identified as a problem that is likely to get worse with climate change. The findings of this work demonstrate that this outcome is not inevitable. While cyanobacteria are favoured by warmer temperatures, a reduction in nutrients can offset temperature increases. Reducing nutrient loads to lake ecosystems will 1) offset other climate change effects and decrease the maximum phytoplankton biomass and 2) decrease the incidence of problematic cyanobacterial blooms and the subsequent heat capture by phytoplankton within the surface layer of lakes. Nutrient loading is far easier to remediate at the decadal and regional scale than warming temperatures. Under one of the most optimistic climate change scenarios, in which greenhouse gases stabilize at year 2000 concentrations, global surface temperatures are still projected to increase by at least 0.3 – 0.6 °C by 2100. More realistic scenarios predict increases up to 3.5 °C by 2100 due to higher concentrations of greenhouse gases, which must be regulated on a global scale. Alternatively, nutrient remediation can be implemented at the watershed scale, for which there are many successful engineering and policy options available. Decreasing nutrient concentrations will have a much stronger impact on phytoplankton productivity in aquatic systems than temperature change. So, acting locally can offset the impacts of a global problem. Understanding water quality risks and implementing a strategy to reduce it is everyday business for a water utility. This project has developed several tools to help utilities predict what challenges they can expect from cyanobacteria in response to nutrient loading or from increases in temperature. These include a Bayesian network and a deterministic model that predicts reservoir hydrodynamics, nutrient dynamics, and phytoplankton growth. This deterministic open-source model is a dynamic ecological model (General Lake Model, GLM, coupled to the Framework for Aquatic Biogeochemical Model, FABM) developed and tested in collaboration with the University of Western Australia. This is a powerful instrument to analyze interactions between physical, chemical, and biological variables in lakes and reservoirs. This model can be tailored to model a lake or reservoir and predict how it will respond to different management strategies, future climate, or nutrient loading. This research shows that an increase in cyanobacterial blooms is not an inevitable result of climate change. Reducing nutrients can offset temperature increases. This is a global problem and requires a global response; water utilities and communities can do little to alter the increasing temperature. On the other hand, nutrient control can occur at a local scale, and communities have the power to control cyanobacteria locally and maintain water quality and healthy human populations. RESEARCH PARTNERS The University of Adelaide, South Australia SA Water, South Australia ©2015 Water Research Foundation. ALL RIGHTS RESERVED. PARTICIPANTS The research team is grateful for the support of the Global Lake Ecological Observatory Network (GLEON). ©2015 Water Research Foundation. ALL RIGHTS RESERVED.