# A clustering approach on multiple metrics to investigate environmental factors enhancing restoration success
## COSAR project

Blandine Charrat, Mathieu Floury, Alienor Jeliazkov, Nina Kaiser, Céline Le Pichon, Martin Palt, Stefan Stoll, Evelyne Tales, Ralf Verdonschot, Christine Weber, et al.

► To cite this version:

Blandine Charrat, Mathieu Floury, Alienor Jeliazkov, Nina Kaiser, Céline Le Pichon, et al. A clustering approach on multiple metrics to investigate environmental factors enhancing restoration success COSAR project. SEFS14 - 14th Symposium for European Freshwater Sciences, Jul 2025, Bolü, Turkey. *hal-05433334*

HAL Id: hal-05433334

https://hal.inrae.fr/hal-05433334v1

Submitted on 28 Dec 2025

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# A clustering approach on multiple metrics to investigate environmental factors enhancing restoration success
## COSAR project

Blandine Charrat, Mathieu Floury, Alienor Jeliazkov, Nina Kaiser, Céline Le Pichon, Martin Palt, Stefan Stoll, Evelyne Tales, Ralf Verdonschot, Christine Weber & Jérémy Piffady

ANR-21-BIRE-0001COSAR
# Recent restoration in Europe

* Many streams are degraded by multiple stressors (incl. Pollution, channelisation, loss of habitats, water abstraction...)

* Efforts are put trying to restore these ecosystems

* General trend:

* A global increase in ecological quality slowing down (Haase et al., 2023)

* Ecological successes from restoration seem to remain low (Dos Reis Oliveira et al., 2020)

* Restoration outcomes depend on the landscape context

* Multiple environmental and biological drivers acting at different spatial scales (Friberg et al., 2016, De Vries et al., 2019)

* Enhancing more efficient restoration requests for better considering the context
Classical approach of restoration effect assessment

* Explore trends and responses from a (set of) particular metric(s), expected to answer

* Taxonomical : richness, diversity, evenness...

* Functional traits : diversity, redundancy, CWMs...

* Large number of individual metrics / pseudo-independent relationships

* Do they all tell the same story ? Do in-between metrics comparison tell new stories?

<figure><img src="image_2.png" /></figure>
Classical approach of restoration effect assessment

* Explore trends and responses from a (set of) particular metric(s), expected to answer

* Taxonomical : richness, diversity, evenness...

* Functional traits : diversity, redundancy, feeding behavior, sensitivity,...

* Large number of individual metrics / pseudo-independent relationships

* Do they all tell the same story ? Do in-between metrics comparison tell new stories?

Is it possible to identify responses patterns and to associate them to the
responsible environmental context and measures ?
# Ecological dataset – monitoring sites

## Restoration sites

62 sites
(FR=20 / GER=7 / NED=25)
Out of 220 available sites

Restored 1988-2018
Monitored 3-6 years post

20 different possible
restoration measures
Up to 16 at one site

<figure><img src="image_3.png" /></figure>
Environmental context – scales and topics

<figure><img src="image_4.png" /></figure>

<table><thead><tr><th>Environmental variable</th><th>Mean</th><th>Sd</th></tr></thead><tbody><tr><td>Natural landscape (% of buffer occupancy)</td><td>21</td><td>28</td></tr><tr><td>Intensive agriculture (% of Basin occupancy)</td><td>40</td><td>25</td></tr><tr><td>Altitude (m)</td><td>189</td><td>276</td></tr><tr><td>Area basin (km²)</td><td>209</td><td>312</td></tr><tr><td>Obstacle density (number of structures on the river)</td><td>0.67</td><td>0.8</td></tr></tbody></table>

<figure><img src="image_5.png" /></figure>

<table><thead><tr><th>Restoration variable</th><th>Not implemented</th><th>Implemented</th></tr></thead><tbody><tr><td>Continuity</td><td>39%</td><td>61%</td></tr><tr><td>Instream</td><td>50%</td><td>50%</td></tr><tr><td>Floodplain</td><td>73%</td><td>27%</td></tr><tr><td>Planform</td><td>21%</td><td>79%</td></tr><tr><td>Targeted scale restoration</td><td>Regional</td><td>Local</td></tr><tr><td></td><td>53%</td><td>47%</td></tr></tbody></table>
# Ecological dataset – monitoring sites

## Restoration sites

62 sites
(FR=20 / GER=7 / NED=25)
Out of 220 available sites

Restored 1988-2018
Considered monitoring 3-6
years post

20 different possible
restoration measures
Up to 16 at one site

## Ecological metrics – computed on fauna lists

### Taxonomical

- Richness

- Eveness

- % EPT

### Traits-based

- Functional Diversity

- Functional Eveness

- Feeding groups diversity

- CWM for sensitive taxa

- Rheophilic

- Stenothermic

<figure><img src="image_6.png" /></figure>
# Ecological dataset – Effect size approach

* From the meta-analysis framework

* Measure of observed effect

* Necessary to allow comparisons of measurements with different scales (across metrics, designs...)

$$Response ratio = \frac{M_{Restored}}{M_{Unrestored}}$$

$$Effect size = log(Response ratio)$$

* But

* loss of the related starting and end values

* And potential loss of information (due to 0s)

$$SpsRich = 4$$

$$SpsRich = 3$$

$$LRR = \log(3/4) = -0.29$$

$$\Delta = -25\%$$

<figure><img src="image_7.png" /></figure>

$$FunDiv = 2$$

<figure><img src="image_8.png" /></figure>

<figure><img src="image_9.png" /></figure>

$$FunDiv = 3$$

$$LRR = \log(3/2) = 0.41$$

$$\Delta = +50\%$$
# Statistical analysis steps

* Identify typical responses patterns

* Clustering

* Need a comprehensive dataset – no NAs

* Identify the contextual features that might enhance higher ecological outcomes from restoration

* Between-group-analysis

<figure><img src="image_10.png" /></figure>
# A clustering approach based on multiple metrics - results

<figure><img src="image_11.png" /></figure>

* Only 2 clusters

* High + effects vs. No effect

* Most of sites exhibit low effects

* Taxo richness increases

* But not EPT

* Communities become more sensitive to flow and temperature regimes

* While functional diversity and evenness tends to decrease

* Only few individuals per new taxa

* Lag-effect of functional responses?
# Identifying the environmental factors - results

<figure><img src="image_12.png" /></figure>

* Larger effects in

* Larger scale projects

* Targeting facies diversity

* In more degraded landscapes

* Local instream measures tend to have low effects

<figure><img src="image_13.png" /></figure>
# Conclusions

* A first attempt to integrate multiple metrics responses in a single framework

* clustering the responses patterns works

* Mixing taxonomic and functional metrics is informative to understand the evolution of the system

* Difficult to disentangle the effects

* A large dataset but many loss on process

* Need larger ones to get finer grain typical responses patterns

* LRR allow large scale comparisons but

* Smaller dataset partly due to LRR

* hide the starting and final states

* large effects, but does it mean reaching a good state?

* Find a way to introduce potential group baselines to allow more precise conclusions
Thanks for your attention