Towards recovery

of Europe’s waters

Cyanobacterial responses to multiple stressors:

an introduction to the EU MARS Project

Laurence Carvalho

Centre for Ecology & Hydrology, Edinburgh, UK

CyanoCOST Meeting

19–21 February 2015

Sevilla, Spain

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.

The MARS project

1 UDE, Germany 13 METU, Turkey

2 AU, Denmark 14 NERC, United Kingdom

3 AZTI, Spain 15 NIVA, Norway

4 BOKU, Austria 16 NTUA, Greece

5 CHMI, Czech Rep. 17 SYKE, Finland

6 CU, United Kingdom 18 UL, Slovenia

7 DDNI, Romania 19 ULT, Portugal

8 DELTARES, Netherlands 20 APA, Portugal*

9 EMU, Estonia 21 BMLFUW, Austria*

10 IGB, Germany 22 EA, United Kingdom*

11 IRSTEA, France 23 ICPDR, international*

12 JRC, Europe 24 NARW, Romania*

*Applied partners

• Managing Aquatic ecosystems and water Resources under multiple Stress

• February 1st, 2014–January 31st, 2018

• Daniel Hering et al. (2015) Sci. of the Total Environment, 503-504: 10-21

• New concepts, methods and tools in river basin management

The MARS Conceptual Framework

Floods &

Droughts

WFD

RBMP

Multiple

Benefits Management

Focus

Methods: Addressing all scales

Water body scale: Mesocosm

factorial experiments and

time series analysis

Combined effects of

temperature,

flow & nutrients

River basin scale: 16 case study basins

Combined effects of

temperature, flow regulation,

nutrients, morphological

alteration and land use

Continental scale: Europe-wide data analysis

Relationships between

drivers, pressures, biological

impacts and ecosystem

services

Common questions across scales

What are the consequences for water supply and recreation?

What are the ecological responses to:

• Q1. extreme temperature and nutrient stress?

• Q2. extreme low flows and nutrient stress?

• Q3. extreme high flows and nutrient stress?

Do stressors act synergistically or antagonistically?

UK & DK experiments: extreme weather and nutrients

2

Models

HHFUNP

Heated

Heated | Flooding

Heated | Nutrients

Heated | Nutrients | Flooding

UHNPFHNPUF

Unheated

Unheated | Flooding

Unheated | Nutrients

Unheated | Nutrients | Flooding

HNPF

HNPF

HNPF

U

U

UUNP

UNP

UNPUNPF

UNPF

UNPF

UNPF

HNP

HNP

HNP

H

H

H

UF

UF

UFHF

HF

HF

Tre

atm

en

ts

Experimental Tanks [1-32]

50% water from Windermere, 50% rain waterSediment – Windermere and topped up with sand

Two factorial block design

2

Models

HHFUNP

Heated

Heated | Flooding

Heated | Nutrients

Heated | Nutrients | Flooding

UHNPFHNPUF

Unheated

Unheated | Flooding

Unheated | Nutrients

Unheated | Nutrients | Flooding

HNPF

HNPF

HNPF

U

U

UUNP

UNP

UNPUNPF

UNPF

UNPF

UNPF

HNP

HNP

HNP

H

H

H

UF

UF

UFHF

HF

HFT

reat

me

nts

Experimental Tanks [1-32]

50% water from Windermere, 50% rain waterSediment – Windermere and topped up with sand

Two factorial block design

Cyanobacterial responses:

• BBE Cyanotorch • Total biovolume • Dominant taxa • Functional groups • Cyanotoxins

Jessica Richardson: PhD student with University of Stirling (Peter Hunter) Nur Filiz: PhD student with University of Ankara (METU, Meryem Beklioglu)

Jun 2014 – Aug 2015

Long-term Lake Time-series

1. UK - Leven (Carvalho) 2. UK - Windermere (Maberly) 3. Estonia (T & P Nõges) 4. Germany (Ute Mischke) 5. Finland (Marko Järvinen) 6. Turkey (Meryem Beklioğlu) 7. Norway (Jannicke Moe)

Low Summer Temperature

High Summer Temperature

High Flushing

Low Flushing

Temperature anomaly

Rainfall anomaly

Response of cyanobacteria (colour represents degree of response)

Continental scale

Cyanobacteria risk map of European lakes

How do cyanobacteria respond to multiple stressors?

Factors affecting response: • Landscape-setting (altitude, land-use) • Lake type (area, depth, alkalinity,

colour) • Weather • Nutrients • Trophic structure (macrophytes/fish)

Response • Mean summer biovolume • Peak summer biovolume

-6 -4 -2 0 2

-20

-15

-10

-50

5

lRettime

s(lR

ett

ime,1

)

-3 -2 -1 0 1

-20

-15

-10

-50

5

lAlkalinity

s(lA

lkalin

ity,3

.1)

1 2 3 4 5

-20

-15

-10

-50

5

lColour

s(lC

olo

ur,

3.8

4)

0 1 2 3 4 5 6 7

-20

-15

-10

-50

5

lTotalP

s(lT

ota

lP,1

)

-6 -4 -2 0 2

-20

-15

-10

-50

5

lRettime

s(lR

ett

ime,1

)

-3 -2 -1 0 1

-20

-15

-10

-50

5

lAlkalinitys(lA

lkalin

ity,3

.1)

1 2 3 4 5

-20

-15

-10

-50

5

lColour

s(lC

olo

ur,

3.8

4)

0 1 2 3 4 5 6 7

-20

-15

-10

-50

5

lTotalP

s(lT

ota

lP,1

)

Exploratory analysis of 134 UK lakes

•non-linear relationship with alkalinity and colour

•positive linear

relationship with retention time and TP

Ln retention time

“Biovo

lume”

“Biovo

lume”

“B

iovo

lume”

“Biovo

lume”

Ln alkalinity

Ln colour Ln Total Phosphorus

(optima ≈1 m.equiv. l-1)

(optima ≈10 Pt. l-1)

(>50 days)

(> 20 µg l-1)

Carvalho et al., 2011. Science of the Total Env., 409: 5353–5358

Quantile regression of 800 European lakes

0.5 1.0 1.5 2.0 2.5 3.0 3.5

0.0

0.5

1.0

1.5

2.0

log10 Total Phosphorous

Cya

no

ba

cte

ria

l b

iovo

lum

e

log

10

mm

3L

1

nl

0.95

0.90

0.75

0.50

WHO Medium/High Risk

WHO Low/Medium Risk

Log Total Phosphorus (µg L-1)

Log C

ya

nob

act

eri

a b

iovolu

me (

mm

3 L

-1)

Large scatter due to other limiting factors:

• light (colour, mixing) • temperature • nitrogen • flushing • grazing

Upper quantile represents maximum capacity (limiting effect of TP)

Phosphorus targets to minimise health risks

Carvalho et al. 2013. Journal of Applied Ecology, 50: 315-323 Sustaining recreational quality of European lakes: minimising the health risks from algal blooms through phosphorus control.

% la

kes

exce

ed

ing

he

alth

th

resh

old

20 µg L-1 TP 10% above low risk

50 µg L-1 TP 40% above low risk

80 µg L-1 TP 50% above low risk

Multi-lake survey – uncertainty

Arquillo de San Blás, Med GIG

Rostherne Mere, CB GIG

Nøklevann, N GIG

32 lakes from 11 countries

Metric uncertainty: Results

Rostherne Mere, CB GIG

Nøklevann, N GIG

Metric Country Waterbody Station Sample Analyst Error

(sub-

sample)

Total

within

Total

between

Optimal predictor

Chlorophylla 0 0.96 0.01 0.01 - 0.02 0.04 0.96 TP, depth, latitude

PTI 0 0.88 <0.01 0 0.04 0.07 0.12 0.88 TP, depth, altitude

SPI 0 0.65 0.03 0 0.19 0.13 0.35 0.65 depth, altitude

MFGI 0 0.86 0.02 <0.01 0.05 0.08 0.14 0.86 depth, altitude

Evenness 0 0.69 0.04 0 0.17 0.1 0.31 0.69 TP alkalinity

Cyanobacteria 0.09 0.86 0.01 0 0.02 0.03 0.06 0.94 TP, depth

• Between lake variability greatest - significantly related to nutrient pressure and depth

• Within-lake, sampling and analytical sources of variability were minimal

Carvalho et al. 2013. Hydrobiologia, 704: 127-140. Strength and uncertainty of lake phytoplankton metrics for assessing eutrophication impacts in lakes.

MARS Summary

• Multistressor conditions are no exception,

but the norm

• Weather extremes bridge risk management

and RBMP

• Responses to multiple stressors affected by

regional landscape and lake-specific factors

• Tools needed that highlight uncertainties for

services at all scales

We welcome collaborative activities!

Towards recovery

of Europe’s waters

Laurence Carvalho

Centre for Ecology & Hydrology,

Edinburgh, UK

laca@ceh.ac.uk