Download Climate-induced changes in phytoplankton - Pyhäjärvi

1
Rapu – Crayfish – Järvet pulassa – Lakes in Trouble – evaluating economic and ecological effects
of climate change, Pori 22.-23.10.2014
Lake Pyhäjärvi 3.11.1999 Jouko Sarvala
Lake Taihu 28.2.2012 Jouko Sarvala
Climate-induced changes in phytoplankton (Extended abstract)
Jouko Sarvala
Section of Ecology, Department of Biology, University of Turku & Pyhäjärvi Institute
We compared long-term monitoring data from two lakes in different climate zones, the boreal
Pyhäjärvi in Finland and the subtropical Taihu in China. In spite of the difference in location, both
lakes have been subject to similar climate change, and they experience direct anthropogenic
pressures of varying magnitude. In both areas, the mean, minimum and maximum air temperatures
in summer showed slightly declining trends in 1959-1980, but increased steeply since ca. 1980
(~0.6ºC decade-1). Likewise, the daily temperature range declined, and precipitation increased in
both areas. In Pyhäjärvi, wind speed first declined, then increased, while the opposite was true for
Taihu.
The scale of the direct anthropogenic pressures varies between the study lakes. Pyhäjärvi area
harbours intensive agriculture but is sparsely inhabited, while Taihu area is among the most
industrialised regions in China, and home to several millions of inhabitants. Accordingly, in
Pyhäjärvi, changes in nutrient concentrations have been moderate, although TP doubled from the
early 1970s to the late 1990s. Conductivity, COD, and particularly pH have increased, indicating
eutrophication and increasing primary production. In Taihu, TN and TP levels, as well as
conductivity, increased up to 2006-2007, with some decline thereafter. Surprisingly, COD changed
little, and pH declined from extremely high to moderately high levels.
In Pyhäjärvi, diatoms were the dominant group in phytoplankton, their biomass doubling or tripling
from the 1960s to 2000s; cyanobacteria increased even more, developing bloom conditions in
several years. All other groups showed initial increase with peak levels in the 1990s, followed by
decreases in the 2000s. In Taihu, cyanobacteria were the dominant group throughout the period,
although with slightly decreased biomass from 1999-2010. Diatoms and chlorophytes were the next
abundant groups, both slightly decreasing over time. In both lakes, presence/absence data indicated
that species composition of phytoplankton did not change significantly over time. In contrast,
biomass data showed that the relative abundances of species changed in both lakes.
Ordination with non-metric multidimensional scaling indicated that climate change played a vital
role in directing phytoplankton community dynamics in the more northern Lake Pyhäjärvi, while
human activities were most responsible for the phytoplankton community changes in Lake Taihu.
2
Our results implied that climate warming potentially enhanced cyanobacteria dominance in both
lakes.
Significant effects of warming temperatures on the phytoplankton of Pyhäjärvi were also suggested
by two recent modelling exercises. Malve et al. (2007: Environmental Modelling & Software) fitted
a purpose-built non-linear dynamic phytoplankton model LakeState to eight years of in situ data
from Pyhäjärvi, using adaptive Markov chain Monte Carlo estimation of 72 parameters according to
a Bayesian approach. Parameters for Cyanobacteria were better identified than those for other algal
groups, but model results agreed satisfactorily with observations in all groups. The model was used
to find out the combinations of total phosphorus and biomass of herbivorous zooplankton needed to
keep the cyanobacterial biomass below a desired level. Model runs with different temperatures
showed that with increasing temperature, more zooplankton was needed at each phosphorus level to
keep cyanobacteria in check, suggesting that the probability of blooms increases at high
temperatures. Pätynen et al. (2014: Boreal Environment Research) used the MyLake and
PROTECH models to test the impacts of warming on the phytoplankton in Pyhäjärvi. MyLake
model (Saloranta & Andersen 2007) produced daily thermal profiles and the ice-on and ice-off
dates from meteorological data. PROTECH simulated the growth of eight phytoplankton taxa from
nutrients, light and temperature; nutrients were also modelled utilising hydrological data and uptake
by algae. The model was tuned to the observed data in three years, and subsequently applied to two
future climate scenarios with increased temperatures. The climate scenarios both indicated that
under warmer conditions, cyanobacteria would grow significantly better, contributing a higher
proportion to phytoplankton, even if the total phytoplankton biomass would not increase unless
nutrient levels would rise, too. Early and late season predictions were problematic, and a general
weakness in all similar models is the poor representation of zooplankton grazing effect.
Based on observational data and model predictions we can firmly conclude that warming climate
has already caused, and will continue to cause substantial changes in lake phytoplankton in the
future, particularly enhancing the potentially harmful blooms of cyanobacteria. This will make our
combat against eutrophication even more difficult.
Acknowledgements
This presentation was partly based on a draft manuscript by Jianming Deng1,6, Boqiang Qin1, Jouko
Sarvala2,4, Nico Salmaso3, Guangwei Zhu1, Anne-Mari Ventelä4, Yunlin Zhang1, Guang Gao1,
Leena Nurminen5, Teija Kirkkala4, Marjo Tarvainen4, and Kristiina Vuorio7 (1 State Key Laboratory of
Lake Science and Environment, Nanjing Institute of Geography and Limnology, Chinese Academy of Sciences, 73 East
Beijing Road, Nanjing, 210008, P. R. China; 2 Department of Biology, University of Turku, FI-20014 Turku, Finland; 3
IASMA Research and Innovation Centre, Fondazione E. Mach-Istituto Agrario di S. Michele all’Adige, S. Michele
all’Adige (Trento), Italy; 4 Pyhäjärvi Institute, Sepäntie 7, Ruukinpuisto, FI-27500 Kauttua, Finland; 5 Department of
Biological and Environmental Sciences, University of Helsinki, FI-00014 Helsinki, Finland; 6 University of Chinese
Academy of Sciences, Beijing, 100049, P.R. China; 7 Department of Biological and Environmental Science, University
of Jyväskylä, FI-40014 Jyväskylä, Finland) and is part of the Sino-Finland Collaboration Project ”Lakes in
trouble”, jointly funded by the Academy of Finland and the Chinese Academy of Sciences. Data for
Lake Taihu were provided by the Nanjing Institute of Geography and Limnology, Chinese
Academy of Sciences; data for Lake Pyhäjärvi derive from several sources, including first of all the
Pyhäjärvi Institute, environmental authorities, Southwest Finland Water Protection Association, and
the University of Turku researchers. Thanks for the numerous collaborators during the tens of years
of monitoring!
3
Late autumn tranquility in Säkylä harbour, Lake Pyhäjärvi. 31.10.2007 Jouko Sarvala