Cyanobacterial responses to multiple stressors: an ... · Cyanobacterial responses to multiple...
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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