# Novel operational index reveals rapid recovery of genetic connectivity in freshwater fish species after riverine restoration

Jérôme G Prunier, Géraldine Loot, Charlotte Veyssiere, Nicolas Poulet, Simon Blanchet

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Jérôme G Prunier, Géraldine Loot, Charlotte Veyssiere, Nicolas Poulet, Simon Blanchet. Novel op- erational index reveals rapid recovery of genetic connectivity in freshwater fish species after riverine restoration. Conservation Letters, 2023, 16 (2), pp.e12939. 10.1111/conl.12939. hal-04124210

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# Novel operational index reveals rapid recovery of genetic connectivity in freshwater fish species after riverine restoration

Jérôme G. Prunier<fnref n="1" /> ID |

Géraldine Loot<fnref n="2" /> |

Charlotte Veyssiere<fnref n="2" /> |

Nicolas Poulet<fnref n="3" /> |

Simon Blanchet<fnref n="1" />

## Correspondence

Jérôme G. Prunier and Simon Blanchet, Centre National de la Recherche Scientifique (CNRS), UAR 2029, Station d'Ecologie Théorique et Expérimentale, 2 route du CNRS, 09200 Moulis, France.
Email: jerome.prunier@gmail.com and simon.blanchet@sete.cnrs.fr

## Funding information

Office Français de la Biodiversité; Région Occitanie Pyrénées-Méditerranée

## Abstract

Hyperfragmentation of rivers by anthropogenic barriers is a major threat to bio-diversity. Restoration policies are being adopted worldwide to mitigate these impacts, particularly those on fish connectivity. We assessed the utility of a novel genetic index of fragmentation, the *F*<sub>INDEX</sub>, by monitoring real-time responses of two fish genera to restoration operations at 11 weirs in France. The *F*<sub>INDEX</sub> outper-formed traditional genetic tools, detecting barriers more efficiently, and thereby improving estimates of recovery of connectivity following restoration. Most weirs had significant impacts on connectivity before restoration, especially the highest and steepest ones. Restoration actions systematically improved genetic connec-tivity, sometimes completely and in just a few months, with an overall halving of fragmentation levels. Our study demonstrates that current restoration poli-cies are recovering genetic connectivity efficiently, and that practitioners may benefit from the *F*<sub>INDEX</sub> as a new operational tool to assess barrier strength for nonmigratory organisms and to plan and monitor riverine restoration.

## KEYWORDS

dam removal, *F*<sub>INDEX</sub>, fish passes, genetic connectivity, genetic monitoring, low-head dams, potamodromous, restoration, weirs

## 1 | INTRODUCTION

Fragmentation of freshwater habitats by anthropogenic instream barriers such as weirs typically exerts disruptive influences on ecological processes (Pringle, 2003). It does so primarily by altering river flow regimes (Grill et al., 2019), thereby affecting upstream-downstream fluxes of energy, matter, and organisms, and reducing the quality, quantity, and accessibility of habitats that allow individ-uals to complete their life cycles (Thurow, 2016). With

more than 1 million barriers, European rivers are hyper-fragmented (Belletti et al., 2020). Many countries have adopted restrictive legislation to re-establish free-flowing rivers (Thieme et al., 2021) and to restore upstream-downstream connectivity, with a particular focus on fish mobility through barrier removal (Bellmore et al., 2017) or through the creation of fishways (Silva et al., 2018).

Properly planning the restoration of river connectivity should rely on the a priori quantification of the indi-vidual and/or cumulative impact of barriers on species

This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.

© 2023 The Authors. Conservation Letters published by Wiley Periodicals LLC.

<footnote n="1">¹Centre National de la Recherche Scientifique (CNRS), UAR 2029, Station d'Ecologie Théorique et Expérimentale, Moulis, France</footnote>

<footnote n="2">²CNRS, UPS, UMR 5174 EDB (Laboratoire Évolution & Diversité Biologique), École Nationale de Formation Agronomique (ENFA), Toulouse, France</footnote>

<footnote n="3">³DRAS, Pôle R&D écohydraulique OFBIMFT-PPRIME, Office Français de la Biodiversité, Toulouse, France</footnote>
movements, while a posteriori quantification of the effi-
ciency of implemented measures might allow improving
future technical solutions through feedback (Birnie-
Gauvin et al., 2017). However, practitioners often stumble
over technical challenges that hamper in situ mea-
surement of barrier passability. Direct methods of
monitoring movement such as video-counting, capture-
mark-recapture, and telemetry often require substantial
resources (Cayuela et al., 2018), causing practitioners to
mostly rely on less demanding and easy-to-implement
assessment methods that provide rough estimates of bar-
rier passability (Appendix S1). However, these estimates
are usually conservative and may result in suboptimal
restoration planning (Mahlum et al., 2014).

Alternatively, barrier passability may be indirectly quantified through genetic approaches (Deflem et al., 2022; Raeymaekers et al., 2009; see Muha et al. [2021] for a complementary approach based on metabarcoding). These methods, however, are not without practical limits. Assignment methods and parentage analyses require extensive sampling of individuals and moderate to high genetic differentiation between populations (Cayuela et al., 2018). The use of upstream-downstream genetic distances as a proxy for barrier effects is less restrictive, but these measures are not comparable across barriers of different age and/or separating upstream and downstream populations of differing effective sizes (Prunier et al., 2020). These caveats can prevent reliable comparisons across species and barriers. In practice, these molecular tools are therefore rarely used by practitioners (Holderegger et al., 2019), and rarely deployed to assess restoration efficiency. As a result, there is little evidence that restoration strategies deployed worldwide actually improve genetic connectivity (but see Fraik et al., 2021).

Here, we monitored responses of two common fish gen-
era to restoration operations at 11 weirs in France. We
used the F<sub>INDEX</sub>, a novel genetic index of fragmentation
designed to provide *standardized* estimates of the effects
of anthropogenic barriers on riverine connectivity by sam-
pling genotypes in the direct upstream and downstream
vicinity of barriers (Prunier et al., 2020). The F<sub>INDEX</sub> can
be computed for any species, provided that separate pop-
ulations can be identified and sampled on both sides of
a barrier, thereby excluding strict migratory fish species
such as anadromous salmonids. The method is based
on rescaling observed upstream-downstream genetic dis-
tances within their theoretical ranges of variation, taking
into account the numbers of generations elapsed since bar-
rier creation and the effective sizes of adjacent populations,
two confounding parameters when comparing genetic dis-
tances. The F<sub>INDEX</sub> can be interpreted as the amount of
reduction in gene flow, compared to the situation with
no barrier. Ranging from 0% (no barrier effect) to 100%

(impassable barrier), F<sub>INDEX</sub> estimates are directly com-
parable across species (whatever their life-history traits),
across barriers (whatever their age or typology), and over
time (allowing comparisons before and after restoration).
These properties should make the F<sub>INDEX</sub> a valuable bar-
rier assessment method for practitioners. However, its
efficiency remains untested empirically. We performed
such a test using the 11 restoration operations, each involv-
ing either removing the barrier, or creating or restoring a
fishway. If they proved to be consistently associated with
a reduction in F<sub>INDEX</sub> values, this would simultaneously
indicate that (i) the F<sub>INDEX</sub> allows proper quantification
of riverscape connectivity both before and after restora-
tion and (ii) that longitudinal restoration is an efficient
approach to trigger the rapid recovery of genetic connec-
tivity. Additionally, we investigated whether F<sub>INDEX</sub> values
varied with barrier typology and compared the F<sub>INDEX</sub> to
classical genetic approaches used to infer interpopulation
connectivity.

2 | MATERIALS AND METHODS

2.1 | Instream barriers

In coordination with practitioners, we identified 11 instream barriers (weirs <4 m high) that were to be dismantled or equipped with a fishway between 2015 and 2019 (Figure 1; Table 1). These restoration actions were carried out opportunistically, preventing us from assessing the cumulative effects of barriers on connectivity. Two of the barriers had already been equipped with fishways but these were judged ineffective by practitioners and were about to be replaced or dismantled. Barriers dated from the 15th to the 20th centuries and were located in three large French river basins, spanning a latitudinal gradient of ~800 km. Our “Typology” procedure classified barriers into four categories (“Low/Gentle,” “Low/Steep,” “High/Gentle,” and “High/Steep”) depending on barrier height (< or ≥2 m) and slope (< or ≥45°) (Appendix S2).

## 2.2 | Biological models and sampling

We focused on five abundant resident fish species from
two genera of cyprinids: minnows (*Phoxinus phoxinus* in
the Seine, *Phoxinus fayollarum* in the Loire, and *Phoxi-
nus dragarum* in the Garonne watershed) and gudgeons
(*Gobio gobio* in the Loire and the Seine and *Gobio occ-
itaniae* in the Garonne watershed; Denys et al., 2020).
Given their maximal body lengths of 140 mm in min-
nows and 200 mm in gudgeons, these fish should be
<figure><img src="image_2.png" /><figcaption>FIGURE 1 Geographic localization of studied instream barriers (black dots) in the main French watersheds</figcaption></figure>

unable to jump over barriers >0.2 m (Baudoin et al., 2014). We thus expected that swimming upstream rather than jumping across barriers was the only option available to them and that the barriers in our low/gentle category might be passable upstreamward (Pflugrath et al., 2019), whereas the steepest and/or the highest barriers would be impassable.

Sampling operations “before” and “after” restoration were performed using electrofishing, which continued until 30 adult individuals of each genus had been captured on each side of each barrier (upstream and downstream), within 200 m of the barrier. A piece of pelvic fin was sampled on each individual and stored in 96% alcohol. All fish were returned alive to their sampling sites. Both genera could be sampled at all sites except in DADRai (gudgeons only) and GLASou (minnows only). The second sampling sessions occurred on average 9.7 (± 3.9 SD) months after restoration (Table 1).

## 2.3 | Genotyping and $F_{INDEX}$ computation

We used 19 microsatellite markers in minnows and 15 in gudgeons. DNA extraction and genotyping followed published procedures (Appendix S3). For each dataset (i.e., each combination of genus and barrier; *n* = 20) and each time period (“Before” and “After”), we computed the $F_{INDEX}$ and its standard deviation (SD$_F$) as described in Prunier et al. (2020). We used the estimated generation times of 2 years in minnows and 2.5 years in gudgeons (Keith et al., 2011) to calculate numbers of generations elapsed since barrier creation. In practice, $F_{INDEX}$ values lower than 20% are interpreted as nonsignificant, indicating fully passable structures (Prunier et al., 2020; Figure 2a; Appendix S4). $F_{INDEX}$ values with a null variance (SD$_F$ = 0) are obtained when all upstream-downstream genetic distances used to compute the $F_{INDEX}$ are null ($F_{INDEX}$ = 0), similarly indicating fully passable structures.
<table><caption>TABLE 1 Main characteristics of barriers ("Lon": Longitude; "Lat": Latitude; approximate date of creation; "Height" (in m), "Slope," and presence of a "fishway"), details about restoration operations (type and date of actions), and time lag (in days) between restoration operations and second sampling sessions</caption><thead><tr><th colspan="4">Barrier characteristics</th><th colspan="4">Restoration operations</th></tr><tr><th>Code</th><th>River</th><th>Lon</th><th>Lat</th><th>Creation (year)</th><th>Height (m)</th><th>Slope (°)</th><th>Fishway</th><th>Action</th><th>Date</th><th>Time lag (days)</th></tr></thead><tbody><tr><td>CEOSal</td><td>Cear</td><td>2.57634</td><td>44.18213</td><td>1800</td><td>0.8</td><td>55</td><td>No</td><td>Removal</td><td>July 2016</td><td>427</td></tr><tr><td>LEZCas</td><td>Lézert</td><td>2.24649</td><td>44.18545</td><td>1400</td><td>1.1</td><td>25</td><td>No</td><td>Removal</td><td>October 2017</td><td>353</td></tr><tr><td>GLAMou</td><td>Gland</td><td>4.09026</td><td>49.92292</td><td>1800</td><td>1.2</td><td>90</td><td>No</td><td>Removal</td><td>July 2019</td><td>303</td></tr><tr><td>VIAPig</td><td>Viaur</td><td>2.18924</td><td>44.13735</td><td>1400</td><td>1.2</td><td>60</td><td>No</td><td>Removal</td><td>August 2017</td><td>44</td></tr><tr><td>LEZVil</td><td>Lézert</td><td>2.26765</td><td>44.19599</td><td>1800</td><td>1.9</td><td>40</td><td>Yes</td><td>Removal</td><td>October 2017</td><td>353</td></tr><tr><td>DADRai</td><td>Dadou</td><td>2.11982</td><td>43.78091</td><td>1800</td><td>2.0</td><td>90</td><td>No</td><td>Removal</td><td>June 2017</td><td>119</td></tr><tr><td>SENBur</td><td>Senoure</td><td>3.41640</td><td>45.27123</td><td>1500</td><td>2.2</td><td>40</td><td>No</td><td>Removal</td><td>September 2015</td><td>406</td></tr><tr><td>GLAPas</td><td>Gland</td><td>4.07951</td><td>49.92529</td><td>1800</td><td>2.6</td><td>90</td><td>No</td><td>Removal</td><td>July 2019</td><td>303</td></tr><tr><td>SIOBre</td><td>Sioule</td><td>3.29729</td><td>46.33353</td><td>1500</td><td>2.6</td><td>30</td><td>Yes</td><td>Fish pass restoration</td><td>October 2015</td><td>376</td></tr><tr><td>GLASou</td><td>Gland</td><td>4.11903</td><td>49.92129</td><td>1800</td><td>3.0</td><td>90</td><td>No</td><td>Removal</td><td>November 2016</td><td>344</td></tr><tr><td>SERHau</td><td>Serein</td><td>3.60304</td><td>47.92156</td><td>1830</td><td>3.5</td><td>80</td><td>No</td><td>Fish pass creation</td><td>October 2017</td><td>226</td></tr></tbody></table>

Note: Most of the dams were created to supply mills (flour mills, sawmills, etc.), although they have since disappeared. Only two dams currently used for hydroelectric production (SIOBre and SERHau) have not been dismantled.
<figure><img src="image_3.png" /><figcaption>FIGURE 2 $F_{INDEX}$ interpretation (a) and main results of the before-after genetic monitoring (b and c). For each barrier (in columns), bars represent $F_{INDEX}$ values with CI$_{95\%}$ as computed before (in green) and after (in blue) restoration in gudgeons (b) and minnows (c). Slashes indicate no data in both panels (b) and (c). Outlined bars represent significant barrier effects ($F_{INDEX} > 20\%$). Blue stars indicate a significant change in $F_{INDEX}$ values after restoration (nonoverlapping CI$_{95\%}$). Double blue stars indicate the full recovery of connectivity following restoration (see details in Appendix S8). Panel (d) also provides few details about barriers (in green) and restoration (in blue) for direct comparisons with $F_{INDEX}$ values (see Table 1). Barriers are sorted by increasing height</figcaption></figure>

## 2.4 | $F_{INDEX}$ validity, barrier effects, and restoration efficiency

To assess the validity of the $F_{INDEX}$, we tested that $F_{INDEX}$ values after restoration were consistently and significantly lower than $F_{INDEX}$ values before restoration.

Additionally, we compared the $F_{INDEX}$ to the outputs of classical genetic approaches used to infer inter-population connectivity, namely STRUCTURE (Pritchard et al., 2000), GeneClass2 (Piry, 2004), and BAYESASS (Wilson & Rannala, 2003). Details are provided in Appendix S5.
To assess barrier effects and restoration efficiency, we adopted a meta-analytical approach that allows combining effect sizes from several studies into an overall effect size, while taking into account the uncertainty associated with each study: studies with high uncertainty are given less weight in the calculation of the overall effect size than studies with low uncertainty (Borenstein, 2009; Appendix S6). Here, we considered each dataset as an independent study providing two effect sizes: the $F_{INDEX}$ values ($\pm$ SD$_F$, i.e., estimates of uncertainty) before and after restoration. In each dataset, the $F_{INDEX}$ value before restoration was used as the effect size $F$ of fragmentation. Similarly, the raw difference between $F_{INDEX}$ values computed after and before restoration was used as the effect size $\Delta F$ ($\pm$ SD$_\Delta$) of restoration for each dataset. The raw difference $\Delta F$ measures the change in the amount of fragmentation following restoration, and constitutes an index of restoration per se. A $\Delta F$ value lower than 0% indicates a significant reduction in fragmentation following restoration, except when both before and after effect sizes of fragmentation $F$ are lower than 20%, in which case the effect size of restoration is also deemed nonsignificant (Appendix S4).

We computed the overall effect sizes of fragmentation $\bar{F}$ and of restoration $\bar{\Delta F}$ by taking into account different sources of variation: within-datasets (SD$_F$ or SD$_\Delta$, with dataset ID as random factor), between-datasets, and across covariates (Appendix S6). Covariates were “Typology” (four levels: see above), “Genus” (two levels: minnows or gudgeons), and, in the case of the overall effect size of restoration $\bar{\Delta F}$, the “Time lag” (in days) between restoration and the second sampling session. For each overall effect size, we performed meta-regressions with all possible interactions among covariates, and identified the best-fit model based on Akaike criterion (Burnham & Anderson, 2002). Covariates identified as significant moderators in the best-fit models were kept as random factors in final models without moderator to get estimates of the overall effect sizes of fragmentation $\bar{F}$ and of restoration $\bar{\Delta F}$ along with their 95% confidence interval (CI$_{95\%}$). We similarly computed the overall effect sizes of fragmentation $\bar{F}'$ and of restoration $\bar{\Delta F}'$ using only datasets with a significant barrier effect ($F_{INDEX} > 20\%$) before restoration ($n = 11$). The relative amounts $\bar{G}$ ($n = 20$) and $\bar{G}'$ ($n = 11$) of genetic recovery were finally computed as the ratio of fragmentation effect sizes after ($\bar{F} + \bar{\Delta F}$) and before ($\bar{F}$) restoration.

### 3 | RESULTS

$F_{INDEX}$ values ranged from 0% to 68.1% before restoration, with a significant barrier effect ($F_{INDEX} > 20\%$) in 11 out of 20 combinations of a genus and a barrier (Appendix

S7). Except when barriers had no effect before restoration ($F_{INDEX} < 20\%$), in which case restoration had no effect either, we observed a systematic and significant decrease in $F_{INDEX}$ values after restoration (Figure 2), with new $F_{INDEX}$ values ranging from 0% to 49.1%. This finding held true whatever the nature of restoration actions. Moreover, the $F_{INDEX}$ outperformed all other genetic tools in quantifying upstream-downstream differences both before and after restoration (Appendix S5).

Before restoration, genera showed contrasted responses to barriers: all barriers but GLAMou and LEZVil had a significant impact on connectivity, but only VIAPig and GLAPas impacted both genera (Figure 2). Nevertheless, “Genus” was not identified as a significant moderator of the overall effect size of fragmentation $\bar{F}$ (Table 2). The best-fit model for $\bar{F}$ only included “Typology” (Appendix S8), with steep barriers $\ge 2$ m showing a significant overall effect size $\bar{F}$ (45.5%, CI$_{95\%}$ > 20%), a value significantly higher than that of gentle weirs <2 m (Table 2; Figure 3). Once typology was taken into account, the overall effect size of fragmentation $\bar{F}$ across all barriers was estimated as 27.7%. It reached a significant value of $\bar{F} = 48.5\%$ (CI$_{95\%}$ > 20%) when only considering significant barriers (Figure S1).

The observed effect sizes of restoration $\Delta F$ ranged from -55.4% to +11.7% (Appendix S7; see Appendix S4 for the interpretation of $\Delta F > 0$). As for $\bar{F}$, the best-fit model for the overall effect size of restoration $\bar{\Delta F}$ only included “Typology” (Appendix S8), with restoration of steep barriers $\ge 2$ m ($\bar{\Delta F} = -27.4\%$, CI$_{95\%}$ < 0%) significantly more efficient than that of gentle weirs, whatever their height (Table 2; Figure 3). $\bar{\Delta F}$ was not significant for gentle weirs <2 m. Neither “Genus” nor “Time lag” were identified as significant moderators of $\bar{\Delta F}$ (Table 2). Once typology was taken into account, the overall effect size of restoration was $\bar{\Delta F} = -14.0\%$ (CI$_{95\%}$ < 0%) across all barrier and $\bar{\Delta F}' = -25.6\%$ (CI$_{95\%}$ < 0%) when only considering significant barriers (Figure S1). The relative amounts of genetic recovery following restoration were $\bar{G} = 49.4\%$ (all datasets) and $\bar{G}' = 47.2\%$ (significant barriers only; Figure S1).

### 4 | DISCUSSION

Quantifying the impact of barriers on river connectivity and the efficiency of restoration operations is a prerequisite for allocating resources toward mitigating the most impactful barriers, for informing trade-offs between ecological and socioeconomic issues and for refining restoration techniques (Hermoso et al., 2012; Silva et al., 2018). This quantification is a difficult task, because operational tools allowing standardized comparisons among species and
<table><caption>TABLE 2 Results of random meta-analyses for the overall effect sizes of fragmentation $\bar{F}$ and of restoration $\Delta F$ for all datasets (n = 20)</caption><thead><tr><th>Overall effect size</th><th>Random effects</th><th>Moderator</th><th>Modalities (INT = Intercept)</th><th>n</th><th>EST</th><th>SE</th><th>ci.low</th><th>ci.high</th><th>Zval</th><th>pval</th></tr></thead><tbody><tr><td rowspan="4">F̄</td><td rowspan="2">Genus|Dataset</td><td rowspan="2">Genus</td><td>Gudgeons (INT)</td><td>10</td><td>23.80</td><td>8.76</td><td>6.62</td><td>40.98</td><td>2.71</td><td>0.007</td></tr><tr><td>Minnows</td><td>10</td><td>7.87</td><td>11.85</td><td>-15.35</td><td>31.09</td><td>0.665</td><td>0.506</td></tr><tr><td rowspan="2">Typology|Dataset</td><td rowspan="2">Typology</td><td>Low/Gentle (INT)</td><td>4</td><td>7.39</td><td>6.03</td><td>-4.42</td><td>19.20</td><td>1.23</td><td>0.220</td></tr><tr><td>Low/Steep</td><td>6</td><td>14.44</td><td>12.09</td><td>-9.26</td><td>38.13</td><td>1.19</td><td>0.232</td></tr><tr><td></td><td></td><td></td><td>High/Gentle</td><td>4</td><td>22.90</td><td>15.45</td><td>-7.37</td><td>53.18</td><td>1.48</td><td>0.138</td></tr><tr><td></td><td></td><td></td><td><strong>High/Steep</strong></td><td>6</td><td><strong>38.10</strong></td><td><strong>11.38</strong></td><td><strong>15.80</strong></td><td><strong>60.41</strong></td><td><strong>3.35</strong></td><td><strong>8.1x10<sup>-4</sup></strong></td></tr><tr><td></td><td>Typology|Dataset</td><td></td><td></td><td>20</td><td>27.74</td><td>5.90</td><td>16.16</td><td>39.31</td><td>4.70</td><td>2.6x10<sup>-6</sup></td></tr><tr><td>F̄'</td><td>Typology|Dataset</td><td></td><td></td><td>11</td><td>48.50</td><td>4.31</td><td>40.06</td><td>56.94</td><td>11.26</td><td>2.1x10<sup>-29</sup></td></tr><tr><td rowspan="4">ΔF</td><td rowspan="2">Genus|Dataset</td><td rowspan="2">Genus</td><td>Gudgeons (INT)</td><td>10</td><td>-14.02</td><td>6.75</td><td>-27.24</td><td>-0.79</td><td>-2.08</td><td>0.038</td></tr><tr><td>Minnows</td><td>10</td><td>0.00</td><td>8.25</td><td>-16.17</td><td>16.17</td><td>0.00</td><td>1.000</td></tr><tr><td rowspan="2">Typology|Dataset</td><td rowspan="2">Typology</td><td>Low/Gentle (INT)</td><td>4</td><td>-1.88</td><td>9.06</td><td>-19.64</td><td>15.88</td><td>-0.21</td><td>0.836</td></tr><tr><td>Low/Steep</td><td>6</td><td>-10.57</td><td>10.45</td><td>-31.04</td><td>9.90</td><td>-1.01</td><td>0.312</td></tr><tr><td></td><td></td><td></td><td>High/Gentle</td><td>4</td><td>-6.50</td><td>9.69</td><td>-25.49</td><td>12.50</td><td>-0.67</td><td>0.503</td></tr><tr><td></td><td></td><td></td><td><strong>High/Steep</strong></td><td>6</td><td><strong>-25.55</strong></td><td><strong>12.78</strong></td><td><strong>-50.60</strong></td><td><strong>-0.49</strong></td><td><strong>-2.00</strong></td><td><strong>0.046</strong></td></tr><tr><td></td><td>Time lag|Dataset</td><td>Time lag</td><td>(INT)</td><td>20</td><td>-27.15</td><td>11.41</td><td>-49.52</td><td>-4.78</td><td>-2.38</td><td>0.017</td></tr><tr><td></td><td></td><td></td><td>Time lag</td><td>20</td><td>-14.01</td><td>4.11</td><td>-22.06</td><td>-5.97</td><td>-3.41</td><td>6.4x10<sup>-4</sup></td></tr><tr><td>ΔF'</td><td>Typology|Dataset</td><td></td><td></td><td>11</td><td>-25.56</td><td>4.53</td><td>-34.45</td><td>-16.67</td><td>-5.64</td><td>1.7x10<sup>-8</sup></td></tr></tbody></table>

Note: In presence of a qualitative moderator, EST is the estimate of the overall effect size for the intercept (INT) and the deviation from the intercept for the alternate modalities. In presence of a quantitative moderator, EST is the estimate of the overall effect size for the intercept (INT) and the estimated slope of the moderator. In absence of moderator, EST is the final estimate of the overall effect size taking into account all sources of variation (including the previously identified significant moderators as random effects, when applicable). The overall effect sizes of fragmentation $\bar{F}$' and of restoration $\Delta F$' are also provided for significant barriers only ($F > 20\%$ before restoration; $n = 11$). The table includes standard error (SE) and $CI_{95\%}$ (ci.low and ci.high) around EST, the Wald-type Z statistic (Zval), and the associated p-value (pval). Although not the best fitting models (Appendix S2), results of meta-regressions with “Genus” and “Time lag” as unique moderators are also provided for comparison.
<figure><img src="image_4.png" /><figcaption>FIG URE 3 Overall effect sizes of fragmentation F (top panel) and of restoration ΔF (bottom panel), predicted from intercept-free random-effect meta-regressions with barrier typology as a moderator, and from random-effect meta-regressions without moderator. Also provided are the overall effect sizes F' and ΔF' computed without moderator for significant barriers only (n = 11). Outlined bars are significant effect sizes according to CI95% (compared to 20% for F and to 0% for ΔF; Appendix S4)</figcaption></figure>

barriers are lacking. Here, we showed that the *F*<sub>INDEX</sub>, a standardized genetic index of fragmentation for nonmigratory organisms that is capable of working across species and barriers, consistently detected recovery of connectivity after barrier removal (Bednarek, 2001), outperforming other traditional genetic tools.

Before restoration, we found a significant barrier effect
in 11 out of 20 fish/barrier combinations, with two barriers
showing no impact on any genus, two barriers signifi-
cantly impacting both genera, and five barriers showing
large differences between fish genera in response to frag-
mentation. This great variability in the local response of
fish to the presence of barriers (Blanchet et al., 2010;

Deflem et al., 2022) reinforces the need for a reliable
and easy-to-implement barrier assessment tool. In absence
of fishways, individuals are expected to take advantage
of floods to cross barriers (Keller et al., 2012). However,
such propitious conditions are not encountered every
year and similarly at all locations, which may explain
why F<sub>INDEX</sub> values differed across datasets and why the
effect of fragmentation was genus independent. However,
F was significantly influenced by the typology of barriers,
with steep barriers ≥2 m showing an overall effect size
38% higher than gentle barriers <2 m (Table 2; Figure 3).
This 2 m threshold should of course be refined with addi-
tional datasets, but it provides a relevant benchmark for
practitioners to adjust restoration planning based on bar-
rier characteristics.

With this effect of typology taken into account, we esti-
mated an overall 27.7% reduction in gene flow on either
side of a barrier compared to a situation with no barrier,
and 48.5% when only considering significant barriers, sug-
gesting an overall halving of gene flow. Diagnosis based
on a standardized tool such as the F<sub>INDEX</sub> may help man-
agers easily quantify and compare barrier effects across
species and barriers, and thus orientate their restora-
tion efforts toward the most problematic structures. Of
course, we acknowledge that other ecological and socioe-
conomic indicators should be considered in restoration
planning (Hermoso et al., 2012). The F<sub>INDEX</sub> might also
help evaluate the species-specific efficiency of fishways—
an important step to drive future technical developments
(Foulds & Lucas, 2013). For example, we estimated that
the two fishways that were removed or replaced during
our study had been beneficial in only three of the four
combinations of fish and barrier (Figure 2), illustrating the
challenge of designing passes adapted to different species
(Birnie-Gauvin et al., 2019; Silva et al., 2018).

We quantified an overall 14% decrease in fragmenta-
tion levels following restoration (27.4% in the case of the
steepest barriers ≥2 m) and 25.6% in the case of the most
problematic ones. Interestingly, whether considering all
barriers or only the most problematic ones, the relative
amount of genetic recovery (Ḡ or Ḡ') was always close to
50%, indicating that restoration operations led to an over-
all halving of fragmentation levels. This systematic gain
in connectivity was achieved within only a few months.
Barrier removal notably led to the full recovery of genetic
connectivity at three localities, ensuring the full mixing
of individuals, and thus of allelic frequencies, within a
year. The F<sub>INDEX</sub> proved sensitive to the direct move-
ments of individuals and allowed quantifying restoration
efficiency even in the absence of gene flow sensu stricto
(i.e., movement followed by reproduction), which con-
stitutes an important operational asset. However, not all
restoration actions proved equally efficient, the recovery
of connectivity being only partial in several situations.
This is the case of the two new fishways that resulted in
an 8%–12% recovery in genetic connectivity in minnows.
These reductions are highly encouraging, although fur-
ther temporal monitoring is needed to determine the final
gain in connectivity following restoration. Although bar-
rier removal is expected to be more efficient than fishway
creation (Birnie-Gauvin et al., 2019), other removal actions
only led to a partial recovery of connectivity, sometimes
even after a year (e.g., minnows at CEOSal). It is not clear
why close-range genetic mixing is sometimes slow despite
the absence of any barrier to movement. For instance, weir
removal might result in changes in habitat characteristics

(Bednarek, 2001), locally inducing a temporary repelling
effect on fish. Nevertheless, we expect the full recovery of
genetic connectivity in the coming years.

Despite its strong operational potential, the F<sub>INDEX</sub> pos-
sesses limitations, some of which could be circumvented
by complementary use of other monitoring methods when
necessary (e.g., telemetry). First, the F<sub>INDEX</sub> assumes sim-
ilar amounts of upstreamward and downstreamward gene
flow (i.e., symmetry; Prunier et al., 2020). We did not detect
any sign of asymmetry (Appendix S9), but asymmetry is
notoriously difficult to assess from genetic data, especially
at such small spatial scales (Sundqvist et al., 2016): passive
downstreamward movements might have remained unde-
tected, explaining why F<sub>INDEX</sub> values never exceeded 70%,
even for the steepest barriers ≥2 m. Future developments
are required to take into account the influence of asym-
metry on F<sub>INDEX</sub> estimates. Second, the F<sub>INDEX</sub> cannot be
used to assess barrier effects on migratory fish species or
on heavily stocked populations, because individuals sam-
pled on either side of a barrier would not form natural
populations per se, making pairwise measures of genetic
differentiation meaningless. However, a large diversity of
freshwater organisms remains qualified for the F<sub>INDEX</sub>
(nonstocked resident fish species, macroinvertebrates).

There are currently at least 16 million weirs and dams
in the world (Lehner et al., 2011). Barrier removal is
seen as an economically viable solution to ensuring pub-
lic safety or restoring the viability of aquatic ecosystems
(Bellmore et al., 2017). However, the number of construc-
tions of new dams in developing economies, as well as
the number of retrofits of nonpowered dams, particularly
in Europe and the United States, is expected to continue
to increase in the coming decades, to meet the grow-
ing needs for food and hydroelectric production (Hansen
et al., 2021; Zarfl et al., 2015). In this context, reconciling
the development of dams and the connectivity of rivers
appears to be a major challenge that will need to be tack-
led worldwide (Thieme et al., 2021). Our study provides
strong proof-of-concept that river restoration through bar-
rier removal or fishway creation can translate into the
rapid recovery of genetic connectivity in different fish
species, and we strongly encourage public policies and
practitioners to continue in this direction (Blanchet &
Tedesco, 2021). To support them in this task, we illus-
trate the efficiency of the F<sub>INDEX</sub>, an operational barrier
assessment method providing standardized estimates of
fragmentation. We believe that the F<sub>INDEX</sub> fills a gap both
in diagnosing barrier effects, paving the way for informed
restoration planning, and in evaluating the efficiency of
implemented measures (either planned or opportunistic),
a prerequisite for refining restoration techniques through
feedback. The large-scale deployment of this methodol-
ogy could also make it possible to lift the veil on the
complex links between individual crossing success, life-
history traits of organisms, barrier typologies, and cumu-
lative barrier effects, thus bringing new perspectives as to
the best means to restore river connectivity.

ACKNOWLEDGMENTS

This study was financially supported by the Office Français
de la Biodiversité and the Région Occitanie. We warmly
thank Michael C. Singer for manuscript proofreading and
all the members of local environmental agencies that have
been involved in this project.

ORCID

Jérôme G. Prunier https://orcid.org/0000-0003-4110-2567

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## SUPPORTING INFORMATION

Additional supporting information can be found online in the Supporting Information section at the end of this article.

**How to cite this article:** Prunier, J. G., Loot, G., Veyssiere, C., Poulet, N., & Blanchet, S. (2023). Novel operational index reveals rapid recovery of genetic connectivity in freshwater fish species after riverine restoration. *Conservation Letters*, e12939.
https://doi.org/10.1111/conl.12939