The aim of this study is to assess the impact of nutrients in combination with climatic stressors on lakes in Northern Europe based on phytoplankton indicators, under current and future conditions. Eutrophication and climate change are considered an important stressor combination for lakes in most parts of the world. High concentrations of total phosphorus (TP) in combination with increased temperature tend to benefit growth of phytoplankton, and in particular cyanobacteria, which can result in harmful algal blooms. In this study, we used monitoring data from ca. 800 lakes in United Kingdom, Norway, Sweden and Finland (in total ca. 2000 lake-years). The data were compiled during the past EU project WISER and are being modelled in the on-going EU project MARS (2014-2018). The first step was to quantify the empirical relationships between phytoplankton indicators (cyanobacteria, biomass and the Phytoplankton Trophic Index), nutrients (primarily TP) and climatic variables (temperature, precipitation and wind) and other lake characteristics (including typology factors such as mean depth, surface area, altitude, alkalinity and humic level). A hierarchical regression model was used account for lake types and for repeated measurements from lakes (lake-years). Following the conceptual model DPSIR (driver-pressure-state-impact-response), we also attempted to link the nutrient concentrations to drivers (population density and land use) and pressures (nutrient loads). The second step was to construct a parametric model constructed based on the quantified relationships. The purpose of this model was to predict the ecological status of the lakes based phytoplankton indicators under different future scenarios of climate and potentially land use. Preliminary assessment by this model has indicated the risk of cyanobacteria concentrations exceeding an acceptable limit increases with higher temperature but decreases slightly with more precipitation. In the current assessment, more realistic future scenarios will be applied for each individual lake. Here we use future projections for climate and land use for the years 2030 and 2060, which are generated on a 0.5 degrees grid for all of Europe. The projections are based on the 3 storylines defined by the MARS project ("Techno world", "Consensus world", "Fragmented world"), which combine different climatic and socio-economic scenarios.

1. Towards recovery
of Europe’s waters
Multiple stress of eutrophication and
climate change in lakes:
projected effects of future climate scenarios for
phytoplankton in Northern European lakes
Jannicke Moe1, Anne Lyche Solheim1, Niina Kotamäki2, Hege Gundersen1,
Laurence Carvalho3, Birger Skjelbred1, Marko Järvinen2, Geoff Phillips4
1) Norwegian Institute for Water Research (NIVA); 2) Finnish Environment Institute (SYKE);
3) Centre for Ecology and Hydrology (CEH), UK; 4) University of Stirling, UK
SIL conference
1-5 August 2016
Torino, Italy
The MARS project (“Managing Aquatic ecosystems and water resources under multiple stress”)
is funded by the European Union under the 7th Framework Programme, contract no. 603378.

2. Limnology in the 21th century: science gaps
Kendra S. Cheruvelil: Limnology in the 21th century:
incorporating data-intensive research, open science, and
team science to address broad-scale problems
(plenary lecture Wednesday morning)
• Extrapolation to unstudied lakes
• Scaling up to larger spatial scales
• Forecasting to future periods

3. Aim of our study
• To assess the impact of nutrients in combination with
climatic stressors on lakes in Northern Europe
based on phytoplankton indicators,
under current and future conditions.
• How will future temperature increase affect
the ecological status of phytoplankton,
under different scenarios of TP concentrations?

4. What is new in this study?
• Large-scale study
– Northern GIG (UK, NO, SE, FI)
– Data from 1100 lakes
• EU project WISER (Moe et al. 2013)
– New European "broad lake types"
• Future climate scenarios
– Based on IPCC scenarios
• Response: phytoplankton trophic index (PTI)
(Ptacnik et al. 2009)
– Species composition and TP preferences
– One of the 3 components of ecological status
of lake phytoplankton cf. WFD

5. Approach: statistical and predictive modelling
Nutrient
concentrations
Phytoplankton
indices
WISER data
Statistical
model
Temperature
Precipitation
Climate data
HISTORICAL DATA
(1988-2009)
Nutrient
concentrations
Phytoplankton
indices
Temperature
Precipitation
MARS climate scenarios
FUTURE SCENARIOS
(2010, 2030, 2090)
"What if" scenarios
WISER data
Predictive
model

6. Approach: statistical and predictive modelling
Nutrient
concentrations
Phytoplankton
indices
WISER data
Statistical
model
Temperature
Precipitation
Climate data
HISTORICAL DATA
(1988-2009)
Nutrient
concentrations
Phytoplankton
indices
Temperature
Precipitation
MARS climate scenarios
FUTURE SCENARIOS
(2010, 2030, 2090)
"What if" scenarios
WISER data
Predictive
model

7. The Phytoplankton Trophic Index (PTI)
• Indicator score based on each genus'
TP optima (sj), weighted by its biomass (aj)
• PTI differs between lake types
• PTI responds to climatic variables
(Phillips et al. 2013)




 n
j
j
n
j
jj
a
sa
PTI
1
1

8. Historical climate data
• Downloaded from Joint Research Centre
– http://agri4cast.jrc.ec.europa.eu
• Spatial resolution: 25 km x 25 km grid
• Temporal resolution: daily
• Variables used in analysis:
– Mean summer air temperature
(Jun-Aug)
– Total summer precipitation
From Richardson et al. (in prep)

9. New European "broad lake types"
ETC/ICM, 2015. European Freshwater Ecosystem Assessment: Cross-walk between the
Water Framework Directive and Habitats Directive types, status and pressures

10. Statistical method: hierarchical regression model
• In our current model, different lake types have ...
– Different PTI level (intercept)
– Different effect of TP
– Same effect of temperature
– Same interaction effect TP * temperature
Largerdataset,
higherprecision
Higherrelevance,
higheraccuracy
9 lake types
In our study:
1100 lakes
4600 samples
(lake-months)

11. Approach: statistical and predictive modelling
Nutrient
concentrations
Phytoplankton
indices
WISER data
Statistical
model
Temperature
Precipitation
JRC climate data
HISTORICAL DATA
(1988-2009)
Nutrient
concentrations
Phytoplankton
indices
Temperature
Precipitation
MARS climate scenarios
FUTURE SCENARIOS
(2010, 2030, 2090)
"What if" scenarios
WISER data
Predictive
model

12. Climate scenarios:
"Consensus world" vs. "Fragmented world"
Climate Scenario 2010 2030 2090
"Consensus World"
(RCP 4.5)
15.9 17.1 18.1
"Fragmented World"
(RCP 8.5)
15.9 17.4 21.3
"2010" (2006-2015) "2030" (2026-2035) "2090" (2086-2095)
Mean summer air
temperature (°C)
Predicted mean summer air temperature (°C)
• Climate model: IPSL-CMSA-LR
• Spatial resolution: 0.5 x 0.5° grid
• Temporal resolution: daily
• More info: MARS fact sheet #03

13. Predicted change in PTI for different climate scenarios
Climate scenario 4.5: Consensus World
Climate scenario 8.5: Fragmented World

14. Predicted change in PTI for different climate scenarios
• Consensus World:
increased PTI for
only 3 lake types
• Fragmented World:
increased PTI for
most lake types
Climate scenario 4.5: Consensus World
Climate scenario 8.5: Fragmented World

15. Focus on lake type 2: Lowland siliceous
Type 2:
Lowland siliceous
(34% of the lakes in this
study)
Type 7:
Mid-altitude siliceous
(16%)
Type 1:
Very large lakes
(9%)

16. Focus on lake type 2: Lowland siliceous
Type 2:
Lowland siliceous
(34% of the lakes in this
study)

17. "What if" scenarios for TP
Phillips & Pitt 2015. A comparison of European freshwater
nutrient boundaries used for the WFD: A report to ECOSTAT.
3 scenarios (applied to all years):
1) 50% lower TP
– Based on the average
difference between
the current TP and
the target TP
(Good/Moderate boundary)
for the lake type
2) 100% TP
– The current TP concentration
of each lake
3) 50% higher TP
Lake type 2 (Lowland siliceous)

18. How will warmer climate affect PTI under different
scenarios of lake TP concentrations?
Lake type 2 (Lowland siliceous)
Climate scenario 8.5: Fragmented World
Climate scenario 4.5: Consensus World

19. How will warmer climate affect PTI under different
scenarios of lake TP concentrations?
Lake type 2 (Lowland siliceous)
Climate scenario 8.5: Fragmented World
Climate scenario 4.5: Consensus World
• Best case CC scenario (Consensus world):
– TP 100%: warmer climate will increase PTI in the long run
– TP +50%: warmer climate will increase PTI already in 2030
– TP -50%: warmer climate will not increase PTI

20. How will warmer climate affect PTI under different
scenarios of lake TP concentrations?
Lake type 2 (Lowland siliceous)
Climate scenario 8.5: Fragmented World
Climate scenario 4.5: Consensus World
• Worst case CC scenario (Fragmented world):
– TP 100%: warmer climate will increase PTI in the long run
– TP +50%: warmer climate will increase PTI already in 2030
– TP -50%: warmer climate will still increase PTI in the long run

21. How much will PTI increase for individual lakes?
Lake type 2 (Lowland siliceous)

22. How much will PTI increase for individual lakes?
Lake type 2 (Lowland siliceous)
• Best case CC scenario (Consensus world):
– Increases in PTI due to climate change are generally below 0.25
– Probably no change in WFD status class

23. How much will PTI increase for individual lakes?
Lake type 2 (Lowland siliceous)
• Worst case CC scenario (Fragmented world):
– Increases in PTI due to climate change are often >0.25
– These lakes have higher risk of obtaining lower ecological status
– Applies to 5-25% of the lakes, depending on the TP scenario

24. Conclusions - Lake type 2
• Even if current TP concentrations would remain,
warmer climate will increase PTI and may thereby reduce
the ecological status of lakes in the long run (2090).
• If TP concentrations were 50% higher,
temperature-induced increase in PTI could be expected
also in the short run (2030).
• If TP concentrations were 50% lower (cf. WFD targets),
temperature-induced increase in PTI could still be
expected in the worst-case CC scenario.
• Temperature-induced change in PTI probably not
sufficient to reduce ecological status class of lakes

25. Conclusions - general
• Large-scale data sets with high taxonomic resolution
are valueable for assessing effects of multiple stressors
on lake ecosystems
• Different properties of lake types are important
• Different future scenarios should be compared
• Large-scale analyses should be supplemented with
in-depth analyses of individual lakes
– cf. Carvalho et al. (this session)

26. Is this approach useful?
Nutrient
concentrations
Phytoplankton
indices
WISER data
Statistical
model
Temperature
Precipitation
Climate data
HISTORICAL DATA
(1988-2009)
Nutrient
concentrations
Phytoplankton
indices
Temperature
Precipitation
MARS climate scenarios
FUTURE SCENARIOS
(2010, 2030, 2090)
"What if" scenarios
WISER data
Predictive
model

27. Is this approach useful?
John Downing: Robert H. Peters: the way to theory,
scientific revolution, knowledge typology, the art of the
soluble, and predictive ecology
(Session 37 Predictive limnology revisited, Wednesday morning)
Fix design
How to course-correct the science that we do:
• Harness the immense power of shared data,
shared platforms, collaborative research,
international networks, and heretofore
unimaginable computing tools to create
imperfect anwers

28. Thank you for the attention!
Acknowledgements:
• The data owners providing data to the former EU
project WISER
• The MARS Geodatabase team (Lidija Globevnik,
Maja Koprivšek and others)
• NIVA colleagues for help with the climate
scenario data (Raoul-Marie Couture, Anna
Birgitta Ledang and James Sample)
• EU 7th FP, contract no. 603378

29. References
• ETC/ICM, 2015. European Freshwater Ecosystem Assessment: Cross-walk
between the Water Framework Directive and Habitats Directive types, status
and pressures, ETC/ICM Technical Report 2/2015, Magdeburg: European
Topic Centre on inland, coastal and marine waters, 95 pp. plus Annexes.
• Faneca Sanchez, M. et al. (2015) Report on the MARS scenarios of future
changes in drivers and pressures with respect to Europe’s water resources.
Part 4 of MARS Deliverable 2.1: Four manuscripts on the multiple stressor
framework.
• Moe, S. J., A. Schmidt-Kloiber, B. J. Dudley & D. Hering, 2013b. The WISER
way of organising ecological data from European rivers, lakes, transitional
and coastal waters. Hydrobiologia 704(1):11-28.
• Phillips, G., A. Lyche-Solheim, B. Skjelbred, U. Mischke, S. Drakare, G. Free,
M. Järvinen, C. Hoyos, G. Morabito, S. Poikane & L. Carvalho, 2013. A
phytoplankton trophic index to assess the status of lakes for the Water
Framework Directive. Hydrobiologia 704(1):75-95.
• Phillips, G., Pitt, J.-A. 2015. A comparison of European freshwater nutrient
boundaries used for the Water Framework Directive: A report to ECOSTAT.
• Ptacnik, R., A. Solimini & P. Brettum, 2009. Performance of a new
phytoplankton composition metric along a eutrophication gradient in Nordic
lakes. Hydrobiologia 633(1):75-82.

30. Potential improvements
• Use future land-use scenarios for more realistic future TP
scenarios (MARS)
• Full ecological status for all lakes
• Non-linear relationships in empirical model
• More predictors (e.g. TN, precipitation)
• Compare outcome for different lake types
• Info from MARS studies of single lakes
(cf. Carvalho et al., this session)
• Expand to larger parts of Europe
(cf. Richardson et al. in prep)