# Exploring the environmental factors mitigating past river restoration ecological success
## COSAR project

Mathieu Floury, Blandine Charrat, 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, with more emphasis following WFD

* 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 seem to 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 needs for better considering the context
Objectives & steps

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

* Gather and harmonize a large dataset of ecological monitoring for restoration projects across Europe

* Use available European dataset to derive environmental context variables

* Climate, land-use, demography, continuity

* Consider possible land-use legacy effects

* Develop statistical models to tackle this complexity
# Ecological dataset – monitoring sites

## Restoration

226 sites from
6 countries

Restored 1988 - 2018
1 to 25 replicates
(mean = 4)

20 different
restoration measures
(max. 16 at one site)

## Biological indicators

Fish
140 sites
1,000 samples and
>100 species

Macroinvertebrates
48 sites
800 samples and
>1300 species

Both
38 sites

<figure><img src="image_1.png" /></figure>
Ecological dataset – set of candidate metrics

<table><thead><tr><th></th><th>Type of metric</th><th>Fish</th><th>Invertebrates</th></tr></thead><tbody><tr><td rowspan="3">Diverse<br>y</td><td rowspan="3">Taxonomy-<br>based</td><td>Taxonomic richness</td><td></td></tr><tr><td>Piélou's evenness</td><td></td></tr><tr><td>Taxonomic entropy (Rao)</td><td></td></tr><tr><td rowspan="3">Sensitiv<br>y</td><td rowspan="3">Trait-based</td><td>Functional richness</td><td></td></tr><tr><td>Functional evenness</td><td></td></tr><tr><td>Functional entropy (Rao)</td><td></td></tr><tr><td rowspan="2">Function<br>al<br>features</td><td rowspan="2">Guilds</td><td>%richness of taxa intolerant to habitat|water quality|oxygen degradation</td><td>%richness EPT taxa</td></tr><tr><td>%abundance of taxa tolerant to habitat|water quality|oxygen degradation</td><td>%abundance GOLD taxa</td></tr><tr><td></td><td rowspan="3">Community-<br>weighted-<br>means</td><td>Rheophily | Limnophily</td><td></td></tr><tr><td></td><td>Stenothermy</td><td></td></tr><tr><td></td><td>Pref. for phytal | psammal | lithal habitats</td><td></td></tr></tbody><tfoot><tr><td colspan="4">→ 15 ecological metrics → [site x date]</td></tr></tfoot></table>
# Ecological dataset – Effect size approach

* From the meta-analysis framework

* Measure of observed effect

* Allows the comparison of measurements with different scales (across metrics, designs...)

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

$$ \text{Effect size} = \log(\text{Response ratio}) $$

$$ \text{SpsRich} = 4 $$

$$ \text{SpsRich} = 3 $$

$$ \begin{aligned} \text{LRR} &= \log(3/4) \\ &= -0.29 \end{aligned} $$

$$ \Delta = -25\% $$

<figure><img src="image_2.png" /></figure>

$$ \text{FunDiv} = 2 $$

<figure><img src="image_3.png" /></figure>

$$ \text{FunDiv} = 3 $$

$$ \begin{aligned} \text{LRR} &= \log(3/2) \\ &= 0.41 \end{aligned} $$

$$ \Delta = +50\% $$
Environmental context – scales and topics

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

<figure><img src="image_6.png" /></figure>

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

<table><thead><tr><th>Context data</th><th>Variable</th><th>Unit</th><th>Station</th><th>Riparian buffer<br>L=500m - W=2x25m</th><th>Landscape buffer<br>L=3000m - W=2x100m</th><th>Surrounding buffer<br>L=10km (isodist.) - W=2x100m</th><th>Upstream sub-catchment</th><th>Drainage basin</th></tr></thead><tbody><tr><td rowspan="20">Bioclimatic variables</td><td>Annual Mean Temperature</td><td>°C</td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>Mean Diurnal Range</td><td>°C</td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>Isothermality (x100)</td><td>Unitless</td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>Temperature Seasonality (st. dev. x100)</td><td>°C</td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>Max Temperature of Warmest Month</td><td>°C</td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>Min Temperature of Warmest Month</td><td>°C</td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>Temperature Annual Range</td><td>°C</td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>Mean Temperature of Wettest Quarter</td><td>°C</td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>Mean Temperature of Driest Quarter</td><td>°C</td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>Mean Temperature of Warmest Quarter</td><td>°C</td><td>x</td><td>x</td><td>x</td><td></td><td>x</td><td>x</td></tr><tr><td>Mean Temperature of Coldest Quarter</td><td>°C</td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>Annual Precipitation</td><td>mm</td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>Precipitation of Wettest Month</td><td>mm</td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>Precipitation of Driest Month</td><td>mm</td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>Precipitation Seasonality (coef. of var.)</td><td>Unitless</td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>Precipitation of Wettest Quarter</td><td>mm</td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>Precipitation of Driest Quarter</td><td>mm</td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>Precipitation of Warmest Quarter</td><td>mm</td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>Precipitation of Coldest Quarter</td><td>mm</td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>Altitude</td><td>Elevation</td><td>m</td><td>x</td><td></td><td></td><td></td><td></td><td></td></tr><tr><td rowspan="2">River Typology</td><td>Bottom-Up</td><td>Unitless</td><td>x</td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>Top-Down</td><td>Unitless</td><td>x</td><td></td><td></td><td></td><td></td><td></td></tr><tr><td rowspan="3">Network</td><td>Stream Power Index</td><td>Unitless</td><td>x</td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>Stream Transport Index</td><td>Unitless</td><td>x</td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>Drainage Density</td><td>km/km²</td><td></td><td></td><td></td><td>x</td><td>x</td><td></td></tr><tr><td rowspan="2">Landuse</td><td>Actual Landuse Percentage</td><td>%</td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>Historic Landuse Percentage</td><td>%</td><td></td><td>x</td><td>x</td><td></td><td>x</td><td>x</td></tr><tr><td>Human Footprint</td><td>Human Impact Index</td><td>Unitless</td><td>x</td><td>x</td><td></td><td>x</td><td>x</td><td></td></tr><tr><td>Demography</td><td>Human Population</td><td>#</td><td>x</td><td>x</td><td></td><td>x</td><td>x</td><td></td></tr><tr><td rowspan="2">Nutrients</td><td>Nitrogen Concentration Class</td><td>g/L</td><td></td><td>x</td><td>x</td><td></td><td>x</td><td>x</td></tr><tr><td>Phophorus Concentration Class</td><td>g/L</td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td rowspan="2">Connectivity</td><td>Obstacle Density</td><td>#/km</td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>Obstacle Number per Class</td><td>#</td><td></td><td></td><td>x</td><td></td><td></td><td></td></tr><tr><td>Protected Areas</td><td>Natura2000 / Ramsar / CDDA</td><td>%</td><td>x</td><td>x</td><td></td><td>x</td><td>x</td><td></td></tr></tbody></table>
Classical approach

* General Additive Model (GAM) applied to taxonomic and functional diversities Log Response Ratio

$$ 
\begin{align*}
\text{LRR} \sim 0 &+ \text{Resto_type} \\
&+ \text{s(Pres\_Pop\_ldscp, bs = "tp", k = 4)} \\
&+ \text{s(Pres\_Crop\_basin, bs = "tp", k = 4)} + \text{s(Past\_Crop\_basin, bs = "tp", k = 4)} \\
&+ \text{s(Obstacle\_density, bs = "tp", k = 4)} \\
&+ \text{s(Area\_basin, bs = "tp", k = 4)} + \text{s(Annual\_Tre, bs = "tp", k = 4)} \\
&+ \text{s(Project\_Age, bs = "tp", k = 4)} \\
&+ \text{s(Design, bs = "re")}
\end{align*}
$$

* For fish and macroinvertebrates
A GAM approach - results

<figure><img src="image_8.png" /></figure>
A GAM approach - results

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

(Full circle = R²)

<figure><img src="image_10.png" /></figure>

(Full circle = total variance)

* Effects are small and difficult to disentangle – local unexplained effects?

* Interactions in responses are not considered

* Could we work on several metrics jointly
# A clustering approach based on multiple metrics - results

* Create groups based on ecological metrics responses (bundles)

* According to their mean response across sites.

* Mean metric value btw. years before and 6 after restoration

* On invertebrates (86 sites)

* After filtering data: 65 sites

* K-means clustering method

* Assign sites to clusters

<figure><img src="image_11.png" /></figure>
# A clustering approach based on multiple metrics - results

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

* Introducing context effect

* Similar results as GAM approach

* Target more degraded landscapes - more potential ?

* Recreate instream habitats is unsufficient

<figure><img src="image_13.png" /></figure>
Conclusion

* Difficult to disentangle effects, even with large datasets

* Creating subsets of sites is a hard task, and often leads to statistically small groups

* LRR allow large scale comparisons

* But hide the starting and final states

* Identify the sites were effects are large, but does it reach a good state?

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

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