linking ecology and the identification of priority ... · • the receptor as a whole (the...

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


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


LLOBREGAT

LLo3

1

2

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4

LLo4

1

2

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4

LLo5

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LLo6

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LLo7

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3

49166 ng/L

EBRO

Ebr1

1

2

3

4 Ebr2

1

2

3

4

Ebr3

1

2

3

4Ebr4

1

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Ebr5

1

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4

JÚCAR

Juc1

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Juc2

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Juc4

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Juc6

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Juc7

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GUADALQUIVIR

Gua1

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Gua2

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Gua3

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Gua4

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3

4

LLo3

% Pesticides (Pest)

% Endocrine Disruptors (ED)

% Perfluorinted compounds (PFC)

% Pharmaceuticals (Pharm)


B

Hydrograph of the

Llobregat River (NE

Spain) (daily

values)

Llobregat at Sant Joan Despí

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Wa

ter

flo

w (

m3

/s)

0

50

100

150

200

-Sediment

mobility:

associated

mobility of

CECs

-Biological

resetting

-…..


B

Hydrograph of the

Llobregat River (NE

Spain) (daily

values)

Llobregat at Sant Joan Despí

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7

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Wa

ter

flo

w (

m3

/s)

0

50

100

150

200

-lower dilution

and higher

concentration

of CECs

-Higher

biological

complexity

-…..


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


& Sergi Sabater1,2


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


& Sergi Sabater1,2


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.

linking ecology and the identification of priority ... · • the receptor as a whole (the...

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