Contribution of benthic invertebrate biological traits to the survey and restoration of stream

ecological quality

Cédric Mondy1,2, Nele Schuwirth2 & Philippe Usseglio-Polatera1

1: Laboratoire Interdisciplinaire des Environnements Continentaux (LIEC), CNRS UMR 7360, Université de Lorraine, Metz, France

2: Eawag – Swiss Federal Institute of Aquatic Science and Technology, Dübendorf, Switzerland

P. 2 Cédric Mondy (cedric.mondy@eawag.ch)

The Water Framework Directive (WFD: European Council 2000)

European Council. Directive 2000/60/EC. Office for official publications of the European Communities, Brussels 2000

Three steps: 1- Assessing the actual ecological quality of water bodies 2- Identifying the stressors that could have led to ecological impairment 3- Predicting which management options could potentially lead to the best improvement of ecological quality

P. 3 Cédric Mondy (cedric.mondy@eawag.ch)

Mondy C.P., Villeneuve B., Archaimbault V. & Usseglio-Polatera P. (2012). Ecological Indicators 18, 452–467.

Level of catchment anthropization

low moderate high

Step 1- Assessing the actual ecological quality

I2M2

Shannon diversity

ASPT

Polyvoltinism

Ovoviviparity

Taxonomic richness

I2M2 does not give specific information on the nature

of the main pressures involved in ecological

impairment

P. 4

Step 2- Identifying the stressors

disturbance

before after

catc

hm

en

t re

ach

H

ER

different trait combinations in communities

Cédric Mondy (cedric.mondy@eawag.ch) P. 5

Water quality Habitat quality

WQ1 – Organic matter HD1 – Transportation facilities

WQ2 – Nitrogen compounds HD2 – Riverine vegetation

WQ3 – Nitrates HD3 − Urbanization (100m)

WQ4 – Phosphorous compounds HD4 – Clogging risk

WQ5 – Suspended matter HD5 – Hydrological instability

WQ6 – Acidification HD6 – Straightening

WQ7 – Mineral micropollutants

WQ8 – Pesticides

WQ9 – PAH

WQ10 – Organic micropollutants

Risk level = two-class system ‘low’ (High or Good) vs.

‘significant’ (Moderate, Poor or Bad)

216 trait-based metrics

Trait category relative use Functional composition Functional richness and diversity Specialization and niche overlap indices SPEAR indices

For each pressure category, built a Conditional Tree Forest

model (CTF) has been built: Risk level ~ trait-based metrics

Mondy C.P. & Usseglio-Polatera P. (2013). Science of The Total Environment 461–462, 750–760.

Step 2- Identifying the stressors

P. 6

test data ‘out of bag’ (OOB) data = learning data not taken into account for a given tree of the forest (37%)

Random models AUC

Good models

Poor models

Step 2- Identifying the stressors

Cédric Mondy (cedric.mondy@eawag.ch)

Riverine Decreasing Food type Dead plants ↓

vegetation litter inputs Living microphytes ↑

impairment Substrate Macrophytes ↑

Organic litter ↓

Increasing Temperature Psychrophilic ↓

irradiation Eurythermic ↑

Transversal River channel ↓

distribution Lake ↑

Longitudinal Crenon ↓

distribution Metapotamon ↑

P. 7

Mondy C.P. & Usseglio-Polatera P. (2013). Science of The Total Environment 461–462, 750–760.

Trait response predictions

X X X

Step 2- Identifying the stressors

Trait response predictions

P. 8 Cédric Mondy (cedric.mondy@eawag.ch)

Pesticides Increasing toxicity SPEAR SPEARpesticides II (%S) ↓

SPEARpesticides V (mean

community sensitivity) ↓

Resistance and Aquatic Larvae ↓

resilience stages Adults ↑

potential Dispersal Aquatic passive ↑

Aerial active ↓

Voltinism Semivoltine ↓

Plurivoltine ↑

Mondy C.P. & Usseglio-Polatera P. (2013). Science of The Total Environment 461–462, 750–760.

Step 2- Identifying the stressors

P. 9 Cédric Mondy (cedric.mondy@eawag.ch)

Mondy C.P. & Usseglio-Polatera P. (2013). Science of The Total Environment 461–462, 750–760.

Step 2- Identifying the stressors

P. 10 Cédric Mondy (cedric.mondy@eawag.ch)

Step 3- Predicting best management options

Requirements: • Modelling benthic invertebrate

dynamics • Predicting changes in biomass

dynamics in response to changes in environmental conditions

Best management/restoration option: Stream management option (e.g. upgrading of WWTP, change in agricultural practices, hydromorphological restoration) that would lead to the highest level of ecological quality improvement for a given amount of investment.

On which pressure should we focus the effort?

P. 11 Cédric Mondy (cedric.mondy@eawag.ch)

Formulate dynamic food web model based on mass balances, use stoichiometry to bound yields of consumption rates

w,wdt

d

θ

Brν

B

Differential equations for the biomasses of all

the taxa and of organic matter

B = (B1,…,Bn) [gDM/m]

stoichiometric coefficients ν = {νij}

process rates r = (r1,…,rm) [gDM/m2/a], which

depend on parameters θ

Schuwirth N & Reichert P. Ecology 2013;94:368–79

“Streambugs model”

Step 3- Predicting best management options

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Schuwirth N & Reichert P. Ecology 2013;94:368–79

Cédric Mondy (cedric.mondy@eawag.ch)

Step 3- Predicting best management options

P. 13

Schuwirth N & Reichert P. Ecology 2013;94:368–79

Cédric Mondy (cedric.mondy@eawag.ch)

0

10

20Temperature

0

50

100

Discharge

Floo

ds

Mortality

Step 3- Predicting best management options

(m3)

(° C

els

ius)

P. 14 Cédric Mondy (cedric.mondy@eawag.ch)

Initial parameter set:

Pretty good performance of the Streambugs model

Under/over-estimate

some taxa

Lack to explain some abundances changes

Step 3- Predicting best management options

Measured floods

Observed biomass

Predicted biomass

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Step 3- Predicting best management options Baetis

Further model development should: • take into account parameter

uncertainty

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Step 3- Predicting best management options

marginal prior (dashed lines) and posterior (solid lines with grey shading) parameter distributions; the posterior resulting from conditioning with the data

Schuwirth N & Reichert P. Ecology 2013;94:368–79

Further model development should: • take into account parameter

uncertainty • learn from data about

parameters (Bayesian inference)

• use other traits

P. 17

Baetis

Further model development should: • take into account parameter

uncertainty • learn from data about

parameters (Bayesian inference)

• use other traits • be tested and calibrated in

other streams subjected to other pressure conditions

• Scenario analysis

Step 3- Predicting best management options

P. 18

Conclusions

Traits can improve: - ecological assessment - stressor identification - ecosystem forecasting Further works: - improving our understanding and description of driving processes - extending the model to other relevant processes (dispersal, emergence…) - multi-criteria decision analysis taking into account ecological, economic, and societal endpoints

P. 19 Cédric Mondy (cedric.mondy@eawag.ch)

Thank you!!