Download Executive Summary: Impact of Climate Change on the Ecology of

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.