Upload
others
View
4
Download
0
Embed Size (px)
Citation preview
Linking ecology and the identification of priority
environmental contaminants: lessons learned from river biofilms
Sergi Sabater
Multiple stressors affect river structure and function
Anomalous
water
temperature
Simplified
habitat
Poorer water
quality
than under
natural
conditions Ter River,
Mediterranean
Multiple stressors affect river structure and function
Ter River,
Mediterranean
Rising pressure on
water resources
Intensive
management: weirs,
dams, channels…
Impact on
biodiversity
Arrival of invasive
species
Postigo et al. 2009
•CEC come along
with:
• Nutrients
• DOM
• Priority
pollutants
0 50 100 150 200 250 300
2-oxo-3-hydroxy-LSD (OH-LSD)
nor-LSD
LSD
11-nor-9-carboxy-THC (nor-THC)
11-hydroxy-THC (OH-THC)
Tetrahydrocannabinol (THC)
6-Acetylmorphine (6ACM)
Heroine (HER)
Morphine (MOR)
Metamphetamine (MA)
Amphetamine (AM)
Ecstasy (MDMA)
Ephedrine (EPH)
Cocaethylene (CE)
Benzoylecgonine (BE)
Cocaine (CO)
Effluent Influent
Multiple stressors affect river structure and function
LLOBREGAT
LLo3
1
2
3
4
LLo4
1
2
3
4
LLo5
1
2
3
4
LLo6
1
2
3
4
LLo7
1
2
3
49166 ng/L
EBRO
Ebr1
1
2
3
4 Ebr2
1
2
3
4
Ebr3
1
2
3
4Ebr4
1
2
3
4
Ebr5
1
2
3
4
JÚCAR
Juc1
1
2
3
4
Juc2
1
2
3
4
Juc4
1
2
3
4
Juc6
1
2
3
4
Juc7
1
2
3
4
GUADALQUIVIR
Gua1
1
2
3
4
Gua2
1
2
3
4
Gua3
1
2
3
4
Gua4
1
2
3
4
LLo3
% Pesticides (Pest)
% Endocrine Disruptors (ED)
% Perfluorinted compounds (PFC)
% Pharmaceuticals (Pharm)
Multiple stressors affect river structure and function
B
Hydrograph of the
Llobregat River (NE
Spain) (daily
values)
Llobregat at Sant Joan Despí
01/0
1/0
7
01/0
2/0
7
01/0
3/0
7
01/0
4/0
7
01/0
5/0
7
01/0
6/0
7
01/0
7/0
7
01/0
8/0
7
01/0
9/0
7
01/1
0/0
7
01/1
1/0
7
01/1
2/0
7
01/0
1/0
8
01/0
2/0
8
01/0
3/0
8
01/0
4/0
8
01/0
5/0
8
01/0
6/0
8
01/0
7/0
8
01/0
8/0
8
01/0
9/0
8
01/1
0/0
8
01/1
1/0
8
01/1
2/0
8
01/0
1/0
9
01/0
2/0
9
01/0
3/0
9
01/0
4/0
9
Wa
ter
flo
w (
m3
/s)
0
50
100
150
200
-Sediment
mobility:
associated
mobility of
CECs
-Biological
resetting
-…..
Multiple stressors affect river structure and function
B
Hydrograph of the
Llobregat River (NE
Spain) (daily
values)
Llobregat at Sant Joan Despí
01/0
1/0
7
01/0
2/0
7
01/0
3/0
7
01/0
4/0
7
01/0
5/0
7
01/0
6/0
7
01/0
7/0
7
01/0
8/0
7
01/0
9/0
7
01/1
0/0
7
01/1
1/0
7
01/1
2/0
7
01/0
1/0
8
01/0
2/0
8
01/0
3/0
8
01/0
4/0
8
01/0
5/0
8
01/0
6/0
8
01/0
7/0
8
01/0
8/0
8
01/0
9/0
8
01/1
0/0
8
01/1
1/0
8
01/1
2/0
8
01/0
1/0
9
01/0
2/0
9
01/0
3/0
9
01/0
4/0
9
Wa
ter
flo
w (
m3
/s)
0
50
100
150
200
-lower dilution
and higher
concentration
of CECs
-Higher
biological
complexity
-…..
Multiple stressors affect river structure and function
A hierarchy of stressors
Water withdrawal-Damming-Flood pulses
Geomorphological
alterations
Biogeochemical
alterations / Pollutants
occurrence
STRUCTURE
and FUNCTION
Biological
Communities
(BIOFILMS)
Hydrological
connectivity
FREQUENCY
INT
EN
SIT
Y
(EN
ER
GY
)
Hydrological/
Geomorphological
Biological
disturbances
Nutrients and
pollutants
Land use changes
Intensity and reversibility of stressor effects
Intensity and reversibility of stressor effects
•Alterations of the hydrological cycle
•Water abstraction
•Inter-basins water transfer
•Damming
•Land-use changes /landscape changes
•Geomorphological disturbances
•Channel simplification and straightening
•Flow derivation
•Geomorphological effects of reservoirs
Most intense and irreversible effects
Hyd
rolo
gic
al and
Phys
ical- d
riven
•Species invasions
•Pollutant inputs
•Nutrient enrichment
Bio
geochem
ical and
bio
logic
al-driven
Least intense and irreversible effects
Fluvial biofilms: biological indicators of multiple
stress
•Ubiquitous in streams and rivers •The (generally) most relevant primary
producers •Heterotrophs and users of DOC
• Nutrient and organic matter recyclers
SUBSTRATUM
Polysaccharide Bacteria
Algae
Fungi ProtozoaCyanobacteria
(Modified from Romaní, 2010)
biofilms
Allow to detect acute and long-
term effects of environmental
changes
light, flow, temp.
nutrients, pH, cond., pollution
BIOFILM
Physical factors
Chemical factors
Biological factors
Water velocity
Temperature
Light availability
Nutrient
availability
pH
Community
composition
Biofilm thickness
Grazing
Competition
TOXICANT &
OTHER
STRESSORS
Potential effect
Real effect Structure
Function
Fluvial biofilms: biological indicators of multiple stress
Albert Ruhí1,2, Isabel Muñoz3, Elisabet Tornés1,2, Ramon J. Batalla1,4, Damià Vericat4, Lydia Ponsatí1, Vicenç Acuña1, Daniel von Schiller1, Gianbattista Bussi5, Félix Francés5
& Sergi Sabater1,2
WP5 PROCESS (SCARCE)
Effects of Triclosan on biofilms- Bacteria
Ricart et al. 2010. Aquatic Toxicology 100: 346-53
Effects of Triclosan on biofilms- Algae
Ricart et al. 2010. Aquatic Toxicology 100: 346-53
Albert Ruhí1,2, Isabel Muñoz3, Elisabet Tornés1,2, Ramon J. Batalla1,4, Damià Vericat4, Lydia Ponsatí1, Vicenç Acuña1, Daniel von Schiller1, Gianbattista Bussi5, Félix Francés5
& Sergi Sabater1,2
WP5 PROCESS (SCARCE)
High water flow vs low-water flow periods, summer-autumn 2011- 2012
Five sites per basin selected
for simultaneous chemical and
biological analyses
Data set used in the analysis
• Physical and Chemical parameters: flow (Q), water nutrient content (PT,
DIN), DOC, Temperature, Oxygen, pH and conductivity.
• Pollutants in water
Identified toxicants nº of compunds nº of families
Non essential metals (MN) 4 1
Essential metals (ME) 2 1
Pesticides (Pest) 49 11
Perfluorinated compounds (PFC's) 23 3
Endocrine disruptors (ED) 26 7
Pharmaceutical products (Pharm) 88 14
TOTAL 192 37
Data set used in the analysis
• Biofilm analysis
SUBSTRATUM
• Algal biomass (Chl-a)
• Diatom composition (diversity,
% abundance)
• Photosynthetic efficiency (Yeff)
• Bacterial density
• Alkaline phosphatase (AP)
Algal parameters Bacterial parameters
Influence of physical and chemical
parameters and pollutants on biofilm
parameters
Redundancy Analysis -Variance
partitioning technique
• Statistical analysis
High hydrologic
variability
• Climate change:
Glo
ba
l ch
an
ge
• Human activity:
• Seasonality:
water pollution
irregular
hydrodynamics
Water quantity and quality is altered due to global change
•↑Conductivity
•↑ Nutrients
•↑ Toxicants
(i.e. biofilms)
More frequent
extreme
hydrological
situations
Natu
ral
co
nd
itio
ns
Drougths Floods
Epilithic biofilms are
subjected to stress
conditions
High flow – Thin biofilms
POM DOM
Inorganic
nutrients Light (UV, PAR)
• High shear stress
• First stages of colonization process
• High species turnover
• High diffusion
Base flow – Thick biofilms
• High biofilm maturity
• Large extracellular matrix
• Lower diffusion
• High retention capacity
POLLUTION
Chemical stressors (nutrients, toxicants, ...) respective
relevance is altered by water flow patterns
High flow
• ↓ algal biomass (chl-a)
• ↑ bacterial densities
• Active metabolism
• ↓ bioaccumulation
• Diatom species characteristic of early
successional stages
Base flow
• ↑ algal biomass (chl-a)
• Metabolism less active
• ↑ bioaccumulation
• Diatoms tolerant to pollution
Pollutants from runoff:
Pesticides and Herbicides
Pollutants from point sources:
Industrial and pharmaceuticals
compounds
Cymbella microcephala Achnanthes biassolettiana
Navicula tripunctata Nitzchia dissipata
Hydrological-driven
responses
Higher relevance of water
pollution
Albert Ruhí1,2, Isabel Muñoz3, Elisabet Tornés1,2, Ramon J. Batalla1,4, Damià Vericat4, Lydia Ponsatí1, Vicenç Acuña1, Daniel von Schiller1, Gianbattista Bussi5, Félix Francés5
& Sergi Sabater1,2
WP5 PROCESS (SCARCE)
may alter biofilm sensitivity to toxicants ?
POTENTIAL EFFECTS ON
BIOFILMS
Flow intermittency
• ↓↓ Algal biomass / changes in species composition • ↓ Bacterial density / changes in structure composition • Overall effects on metabolism (primary production & respiration)
Corcoll et al. 2014. STOTEN in press
Intermittent Flow 2D Graph 1
X Data
2d 5d 9d 11d
AF
DW
(m
g /
cm
2)
0.2
0.3
0.4
0.5
0.6noP
P
2d 5d 9d 11d
Chl-a (
µg ·
cm
- 2)
0
1050
60
noP
P
2D Graph 1
X Data
2d 5d 9d 11d
Ba
cte
ria
(ce
lls ·
cm
-2)
1e+11
2e+11
3e+11
4e+11
5e+11
6e+11
sampling day vs FI_NPE sd
sampling day vs FI_PE
noP
P
Flow (F)*
Flow (F)*
Flow (F)
Intermittent Flow
• total biomass reduction≈ 17%
•algal biomass reduction≈ 87% • increase in bacterial density at
2d-5d
Results: Biomass 2D Graph 1
X Data
2d 5d 9d 11d
AF
DW
(m
g / c
m2)
0.2
0.3
0.4
0.5
0.6noP
P
Continuous Flow
2d 5d 9d 11d
Chl-a (
µg ·
cm
- 2)
0
10
20
30
40
50
60
noP
P
2D Graph 1
X Data
2d 5d 9d 11d
Bacte
ria (
cells
· c
m-2
)
1e+11
2e+11
3e+11
4e+11
5e+11
6e+11
sampling day vs FC_NPE mean
sampling day vs FC_PE mean
noP
P
Pharm
(P)*
Pharm (P)*
Pharmaceuticals: •total biomass reduction≈ 8%
•algal biomass reduction ≈ 20%
• non-effect on bacterial density
Pharm (P)
* p<0.05 (ANOVA 2 ways of repeated measures)
noP: no pharmaceuticals
P: pharmaceuticals exposure
P x F (multi stress)
• no significant effects
P x F
P x F
P x F
Results: Metabolism
noP: no pharmaceuticals
P: pharmaceuticals exposure
* p<0.05 (ANOVA 2 ways of repeated measures)
Pharmaceuticals: • ↑ NPP ≈ 8%→ ↑ unicel green-algae
• ↑CR ≈ 20%→↑ heterotrophic contr.
Intermittent Flow
• NPP→ ↓ algal biomass
• CR → ↓total biomass
Intermittent Flow 2D Graph 5
X Data
2d 5d 9d 11d
NP
P (
mg O
2 ·
cm
-2·d
-1)
-1
0
1
2
3
4
sampling time vs FI_NPE sd
sampling time vs FI_PE
noP
P
2D Graph 6
X Data
2d 5d 9d 11d
CR
(m
g O
2 ·
cm
-2·d
-1)
0.0
0.4
0.8
1.2
1.6
2.0
sampling time vs FI_NPE sd
sampling time vs FI_PE
noP
PFlow (F)*
Flow (F)*
Continuous
Flow
2D Graph 5
X Data
2d 5d 9d 11d
NP
P (
mg
O2
· c
m-2
·d-1
)
-1
0
1
2
3
4
sampling time vs FC_NPE mean
sampling time vs FC_PE mean
noP
P
2D Graph 6
X Data
2d 5d 9d 11d
CR
(m
g O
2 ·
cm
-2·d
-1)
0.0
0.4
0.8
1.2
1.6
2.0
noP
P
noP
P
Pharm
(P)*
Pharm
(P)*
P x F • ↑↑ NPP→ ↑ unicel. green-algae
• ↑ CR → ↑ heterotrophic contr.
P x F*
P x F*
Acute effects are mixed to those long-term:
Pharmaceutical bioaccumulation in biofilms-
ng / gDW Cont_P Int_P
2d 11d 2d 11d
Carbamazepine 2.72 (0.44) 0.9 (1.55) 0.94 (1.63) 1.31 (2.03)
Sulfamethoxazole 9.03 (3.73) 13.79 (8.40) 11.28 (0.98) 16.48 (8.59)
Erythromycin nd nd nd nd
Metoprolol 47.87 (9.98) 83.08 (9.98)* 35.09 (6.22) 90.57 (9.15)*
Atenolol nd nd nd nd
Ibuprofen nd nd nd nd
Diclofenac 13.45 (4.15) nd* 17.76 (8.92) <LOQ*
Gemfibrozil nd nd nd nd
Hidrochlorothiazide 28.43 (7.76) 25.82 (7.94) 29.95 (13.45) 39.86 (29.86)
• Bioaccumulation similar when exposed to Continuous and Intermittent flow
• Bioaccumulation: Metropolol ˃ Hydrochlorothiazide ˃ Sulfamethoxazole ≈Diclofenac ˃
Carbamazepine
• Metroprolol bioaccumulation ↑ over time (↑40-60%)
•Diclofenac bioaccumulation ↓ over time → acclimated biofilm?
* p<0.05
• The receptor as a whole (the biological community) needs
to be included in the effect-based decisions of prioritisation
•Complexity includes the variety of stressors, their co-
occurrence, the geographical-specificities, and the biological
complexity (community-based)
•Long-term effects (bioaccumulation) may have unknown
consequences
Potential implications
•Uncertainty is part of the real environment; adding
uncertainty estimates into the process to include community-
based, nature-based responses
•Moving from effect-based values to management options
requires including estimates of uncertainty
Potential implications
THANKS TO
Natàlia Corcoll, Maria Casellas, Belinda Huerta, Helena Guasch, Vicenç Acuña, Sara Rodríguez-Mozaz, Albert Serra-Compte, Damià Barceló, Mira Petrovic, Lydia Ponsati, Albert
Ruhi, Dani von Schiller, Marta Terrado, Elisabet Tornés
Scarce Consolider-Ingenio 2010 (CSD2009-00065). “Assessing and Predicting Effects On Water Quantity and Quality In Iberian
Rivers Caused By Global Change (2009-2014)” and Spanish Ministry of Economy and Competitiveness.