# Genetic Rescue in Action: Long-Term Monitoring Reveals Admixture-Driven Fitness Gains in a Translocated Plant

Olivier Brisset, Léa Auclair, Jean-baptiste Mihoub, Stefano Mona, Alexandre Robert, José Utge, Paul Verdu, Nathalie Machon

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To cite this version:

Olivier Brisset, Léa Auclair, Jean-baptiste Mihoub, Stefano Mona, Alexandre Robert, et al.. Genetic Rescue in Action: Long-Term Monitoring Reveals Admixture-Driven Fitness Gains in a Translocated Plant. Conservation Letters, 2025, 18 (6), pp.e70012. 10.1111/con4.70012. hal-05411254

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LETTER

OPEN ACCESS

# Genetic Rescue in Action: Long-Term Monitoring Reveals Admixture-Driven Fitness Gains in a Translocated Plant

Olivier Brisset<fnref n="1" /> ID | Léa Auclair<fnref n="1" /> ID | Jean-Baptiste Mihoub<fnref n="1" /> ID | Stefano Mona² ID | Alexandre Robert<fnref n="1" /> ID |
José Utge<fnref n="3" /> ID | Paul Verdu<fnref n="3" /> ID | Nathalie Machon<fnref n="1" /> ID

**Correspondence:** Olivier Brisset (olivier.brisset@mnhn.fr)

**Received:** 8 April 2025 | **Revised:** 27 October 2025 | **Accepted:** 17 November 2025

**Keywords:** fitness | genetic rescue | hybridization | inbreeding depression | mixed-source translocation | plant conservation

## ABSTRACT

Mixed-source translocation can reduce immediate extinction risk by increasing population size, genetic diversity, and individual fitness, but their long-term consequences remain debated. To rescue the perennial plant *Arenaria grandiflora* from extinction in the Fontainebleau Forest (northern France), local and non-local (Chinon, Central France) plants were multiplied in vitro, and clones were translocated to unoccupied sites in their original area of distribution. Therefore, the translocated populations presented higher genetic diversity compared to either source populations. Since then, two decades of demographic and genetic monitoring revealed that the genetic diversity of the restored populations remained stable while the relative amount of non-local genetic ancestry increased over generations. We found no evidence of maladaptation and, instead, a higher fitness of admixed individuals compared to individuals resembling either local or non-local gene-pools. This study provides unique insights into the dynamics between admixture and fitness in a conservation translocation program spanning approximately five generations.

## 1 | Introduction

Rapid environmental changes can cause the decline of numerous plant and animal species, inducing loss of genetic diversity (Pennisi 2025; Shaw et al. 2025) and inbreeding depression (Amos and Balmford 2001; Briscoe et al. 2002; Blomqvist et al. 2010), which may amplify the risk of local extinction. Genetic drift and inbreeding depression have been extensively documented to significantly undermine population viability (Frankham 1999; O'Grady et al. 2006; Allendorf et al. 2022). While erosion of genetic variability through genetic drift poses long-term challenges to persistence and adaptability, the reduction of fitness caused by inbreeding depression can critically threaten persistence in the

short-term (Keller and Waller 2002; Bijlsma and Loeschcke 2012; Harrisson et al. 2019).

Genetic rescue aims to address genetic issues occurring in a small population, such as inbreeding and loss of diversity, by bringing in new genes from another population (Whiteley et al. 2015; Fitzpatrick et al. 2023). It may be achieved by mixed-source translocation, consisting of translocation of individuals from multiple populations in order to increase population size and maximize genetic diversity while preserving local gene pool by including local individuals. These operations are expected to yield multiple benefits. First, increasing population size reduces the risk of extinction from demographic stochasticity and Allee

<footnote n="1">¹Centre d'Ecologie et des Sciences de la Conservation (CESCO), Muséum national d'Histoire naturelle, Centre National de la Recherche Scientifique, Sorbonne Université, Paris, France | ²Institut de Systématique, Evolution, Biodiversité (ISYEB), EPHE-PSL, Université PSL, MNHN, CNRS, SU, UA, Paris, France |</footnote>

<footnote n="3">³UMR7206 Eco-anthropologie, CNRS, MNHN, Université Paris Cité, Paris, France</footnote>

<footnote>Olivier Brisset and Léa Auclair shared first co-authorship.</footnote>

<footnote>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.</footnote>

<footnote>© 2025 The Author(s). *Conservation Letters* published by Wiley Periodicals LLC.</footnote>
effects (Allee and Allee 1931; Berec et al. 2007). It also theoretically
compensates for the effect of genetic drift by introducing new
alleles and increasing the frequency of potentially adaptive
variants in declining populations. Furthermore, translocations
of exogenous individuals may also improve fitness by increasing
heterozygosity in the following generation, as previously widely
documented in animal (Clarke et al. 2024) and plant species (Van
Rossum et al. 2020; Van Rossum and Le Pajolec 2021).

Beyond all the expected outcomes of conservation translocations
from ecological, ethical, or socio-economical perspectives, there
is still a scientific controversy on whether this kind of program is
beneficial or detrimental for population dynamics and viability
(Maurice et al. 2013). Indeed, translocated individuals may fail
to reproduce with local ones due to phenetic differences affect-
ing reproduction, or due to genetic incompatibilities (Bateson
1909; Dobzhansky 1937; Edmands 2002; Pickup & Young 2008;
Widmer et al. 2009). When translocations involve individuals
from diverse origins, outbreeding depression can also occur,
lowering the fitness of hybrid offspring through the production
of intermediate, low-fitness phenotypes, or by disrupting co-
adapted gene complexes (Montalvo and Ellstrand 2001; Edmands
& Timmerman 2003, 2007; Fishman and Sweigart 2018). This
can take place especially through genetic swamping, where local
adaptive alleles are replaced by maladaptive exogenous alleles
(Weeks et al. 2011). These potential negative effects on local pop-
ulations temper enthusiasm for translocation as a conservation
strategy, raising ethical and empirical concerns about its impact
on preserving local biodiversity (Edmands 2007; Frankham et al.
2011; Rohwer and Marris 2015; Todesco et al. 2016). Although an
increasing number of studies are investigating the consequences
of genetic rescue, few have documented the outcomes of such
translocation programs from a demographic and evolutionary
perspective over several generations (Menges 2008; Godefroid
et al. 2011, Frankham 2016, Bell et al. 2019).

*Arenaria grandiflora* L. (Caryophyllaceae) is a short-lived peren-
nial plant that grows in increasingly larger clumps, producing
more flowers each year until it reaches senescence and dies.
Based on the size of the flower, its pollination is believed to be
entomophilous, with species from the Syrhidae family often
observed on flowers during field sessions. The generation time
of the plant has been estimated at approximately four years,
based on current knowledge of its life cycle and a matrix
population model done previously (unpublished data). The *A.
grandiflora* population originally occurring in the Fontainebleau
Forest (northern France) experienced a drastic decline and was
suspected to be highly inbred and/or to suffer from increased
fixation of deleterious alleles due to intense genetic drift (Bottin
et al. 2007). A genetic rescue program was initiated to preserve
the local diversity of *A. grandiflora* through the translocation of
a mixture of local and non-local plants. To this end, individuals
from the local Fontainebleau population and a genetically distinct
non-local population from Chinon (Loire Valley, about 230 km
from Fontainebleau), the only other remaining lowland popula-
tion, were cloned ex-situ and translocated to three unoccupied
sites of the Fontainebleau Forest located about 10 km apart
within the historical range of the species. Since then, the translo-
cated populations have been closely monitored, with annual
assessments of multiple fitness components and periodic genetic
sampling. This plant translocation program involving the mixing

of individuals from local and non-local sources offers a rare
opportunity to examine the consequence of admixture dynamics
on genetic diversity and individual fitness over approximately
five generations and more than two decades. Understanding the
benefits and drawbacks of this type of translocation is crucial for
effective conservation management, especially in the context of
human-induced habitat loss and fragmentation as well as climate
change.

2 | Material and Methods

## 2.1 | Population Description and Sampling
Strategy

2.1.1 | Population Description

The translocation program of *A. grandiflora* in the Fontainebleau
Forest was launched in 1997 following the decline of native pop-
ulations in the early 1990s. Translocated individuals originated
from cuttings sampled in 1997 from wild plants in two popu-
lations: the original declining population from Fontainebleau,
which was declared extinct in 2014, and a population from
Chinon, hereafter referred to as the “local” and “non-local”
source populations, respectively. For two years, cuttings from
nine local and from 11 non-local plants were propagated in
vitro to produce 1308 clonal individuals. In 1999, these clonal
individuals were planted in three unoccupied suitable sites in
Fontainebleau Forest, sufficiently distant from one another to
avoid gene flow: “Bois Rond” (BR), “Cuvier Chatillon” (CC), and
“Queue de Vache” (QV), with distances: BR–CC = 10 km, BR–
QV = 2 km, and CC–QV = 8 km. The pollination distance of
Syrphidae usually ranges from a few meters to 2 km (Schönrogge
et al. 2006; Schweiger et al. 2007). Three identical founding
populations comprising 30% non-local and 70% local clones (total
= 436 plants per site) were translocated, one at each site. Bottin
et al. (2007) provides the detailed description of the translocation
program. At the BR site, the translocated population went extinct
in 2004 due to extensive herbivory by rabbits and was removed
from this study. The first naturally occurring seedlings were
observed in 2002, and new seedlings have been observed every
year since then. The last original founder was observed in
2020.

2.1.2 | Demographic Data Sampling

Since 2000, annual surveys were undertaken in late spring
(flowering period) to monitor the demography of each popula-
tion. Every year, we recorded all individuals that we detected,
measured their size (i.e., clump diameter), counted their number
of flowers and labeled each new individual. Based on these
measurements and knowledge about the plant's life cycle, we
classified each individual into three life stages: juvenile, adult,
and senescent (Supporting Information 1). We estimated three
fitness components: (i) reproductive effort as the annual number
of flowers per individual; (ii) growth as the size of an individual
in year *n* + 1 relative to its size in year *n*; and (iii) survival as the
probability of adult survival (Sa) between consecutive monitoring
sessions.
### 2.1.3 | Biological Sampling, Sequencing, and Genotyping

To monitor the evolutionary trajectory of the translocated populations, we sampled living leaves for DNA extraction and genotyping in 2007, 2011, 2014, and 2021 (details on sequenced individuals are presented in Table S1). We also genotyped clones from all the non-local and local founder individuals, except one for which tissues were no longer available at the time of the sequencing.

We genotyped all individuals at 10 species-specific microsatellite markers following the PCR and genotyping protocols of Zavodna et al. 2009, 2015). We discarded one locus (AGA2-73) due to apparent tetraploidy and another (AG9-164) due to significant departure from Hardy-Weinberg equilibrium.

## 3 | Statistical Analyses

### 3.1 | Population Genetics

#### 3.1.1 | Genetic Diversity and Population Differentiation

We estimated Allele count (Ac), local and non-local private alleles, expected ($H_{exp}$) and observed heterozygosity ($H_{obs}$) (Nei 1972), and inbreeding coefficient ($F_{IS}$) (Wright 1949), in source and translocated populations using *Arlequin 3.5* (Excoffier and Lischer 2010) and the *hierfstat* (Goudet 2005) R package. Individual heterozygosity was computed as an individual's percentage of observed heterozygous sites. We compared the above genetic diversity indexes between populations and sampling years (Supporting Information 2). To monitor the outcome of the translocation program, we computed pairwise $F_{ST}$ (Cockerham and Weir 1986) across our temporal sampling and estimated significance of pairwise $F_{ST}$ using 1000 permutations with *Arlequin 3.5*. Specifically, we contrasted the $F_{ST}$ between the two populations sampled in the same year against the differentiation accumulated through time within both of them. Comparisons including source populations were also computed.

#### 3.1.2 | Dynamics of Admixture

To detect possible changes over time in the proportions of genotypes originating from either contributing (local or non-local) source population (referred to as “genetic ancestry”), we performed supervised Bayesian clustering analyses with *STRUCTURE 2.3* (Pritchard et al. 2000; Falush et al. 2003), assuming $K = 2$, assigning source individuals from local and non-local sources to separate clusters, and using the “admixture” model (more details in Supporting Information 3). To assess temporal changes in admixture, we applied a generalized mixed-effects model (GLMM) to test how the year of first observation influenced ancestry proportions in the two translocated populations (Supporting Information 4).

### 3.2 | Genetic Influence on Fitness

We studied the influence of individual genetic ancestry and heterozygosity on three fitness components: reproductive effort,

growth, and survival. We set up the ancestry proportions of clones to 0 (local) and 1 (non-local). Analyses comprised around 60% of translocated clones and 40% of offspring individuals from subsequent generations over the years. Clones for which genotypes were unavailable were excluded from heterozygosity analyses but not from ancestry because their origins (non-local or local) were still known (Supporting Information 5). We tested the effect of ancestry proportions both as linear and quadratic relationships, and the interaction with populations (CC and QV), using AICc score as statistical support and considering more complex models supported only if $\Delta$AICc $\ge$ 2. Only linear effects and the interaction with populations were tested for heterozygosity. To assess whether the ancestry effect was driven by first-generation offspring (F1), we added a binary variable in the ancestry models for the three fitness components. Individuals with ancestry proportions between 0.4 and 0.6 and born in or before 2010 were considered as F1 (Supporting Information 6). To take into account the fact that translocated individuals may have reduced fitness due to translocation regardless of their genotype, we included a binary variable in the ancestry models, called translocated, indicating whether each individual was translocated or not.

#### 3.2.1 | Reproductive Effort and Growth

A first dataset comprising 3772 phenotypic observations from 1241 plants was used to test the effect of ancestry proportions on the reproductive effort. A second reduced dataset composed of 3395 phenotypic observations from 1039 plants served to test the effect of individual heterozygosity, as heterozygosity was not known for all individuals. We fitted GLMMs using the *glmmTMB* R package (Brooks et al. 2017), applying a negative binomial distribution with a zero-inflation component including only an intercept term.

We restricted growth analysis to cases where plants were measured in two consecutive years (3112 and 2228 observations from 1053 and 918 individuals respectively for ancestry proportions and heterozygosity). We used GLMMs (*glmmTMB* R package) with a gamma distribution and a logarithmic link.

For both growth and reproductive effort models, plant ID and year of sampling were included as random factors. The logarithm of the plant diameter was incorporated as a continuous variable, the translocated populations (CC or QV), and F1 and translocated variables were incorporated as fixed-effects categorical variables.

#### 3.2.2 | Survival

Capture-Mark-Recapture (CMR) analyses were conducted to assess the probabilities of survival and transition between life stages (juvenile, adult, and senescent), considering the imperfect detection probability of individual plants due to small size and surrounding vegetation cover. We applied a multi-state Arnason-Schwarz model, assigning each state to a life stage with the *Rmark* package (Laake 2013) (Supporting Information 7). We conducted 22 CMR sessions (one per year) between 2000 and 2021. The CMR dataset eventually included 1395 genotyped individuals, comprising 843 clonal plants from the two source populations and
<table><caption>**TABLE 1** | Intra-population genetic diversity summary-statistics and average ancestry proportions from the non-local source population in each year of sampling.</caption><thead><tr><th rowspan="2">Year<br>Location</th><th colspan="2">1999</th><th colspan="2">2007</th><th colspan="2">2011</th><th colspan="2">2014</th><th colspan="2">2021</th></tr><tr><th>Fontainebleau</th><th>Chinon</th><th>QV</th><th>CC</th><th>QV</th><th>CC</th><th>QV</th><th>CC</th><th>QV</th><th>CC</th></tr></thead><tbody><tr><td>P</td><td>9</td><td>100</td><td>691</td><td>130</td><td>779</td><td>100</td><td>600</td><td>86</td><td>276</td><td>118</td></tr><tr><td>N</td><td>9</td><td>10</td><td>186</td><td>83</td><td>220</td><td>58</td><td>145</td><td>38</td><td>66</td><td>16</td></tr><tr><td>Ac</td><td>2.38</td><td>4.88</td><td>6</td><td>5.5</td><td>5.75</td><td>5.25</td><td>5.5</td><td>5.13</td><td>5.75</td><td>4.88</td></tr><tr><td>LP</td><td>8</td><td>0</td><td>7</td><td>7</td><td>7</td><td>7</td><td>7</td><td>7</td><td>7</td><td>6</td></tr><tr><td>NLP</td><td>0</td><td>29</td><td>28</td><td>26</td><td>26</td><td>23</td><td>24</td><td>22</td><td>21</td><td>19</td></tr><tr><td>H<sub>obs</sub></td><td>0.32</td><td>0.54</td><td>0.66</td><td>0.62</td><td>0.62</td><td>0.63</td><td>0.63</td><td>0.6</td><td>0.56</td><td>0.56</td></tr><tr><td>H<sub>exp</sub></td><td>0.39</td><td>0.66</td><td>0.63</td><td>0.64</td><td>0.63</td><td>0.63</td><td>0.63</td><td>0.63</td><td>0.65</td><td>0.67</td></tr><tr><td>F<sub>IS</sub></td><td>0.18</td><td><strong>0.19</strong></td><td>-0.05</td><td>0.02</td><td>-0.01</td><td>0.01</td><td>-0.02</td><td>0.01</td><td><strong>0.13</strong></td><td><strong>0.12</strong></td></tr><tr><td>M<sub>anc</sub></td><td>0</td><td>1</td><td>0.52</td><td>0.49</td><td>0.57</td><td>0.51</td><td>0.57</td><td>0.57</td><td>0.62</td><td>0.65</td></tr><tr><td>V<sub>anc</sub></td><td>0</td><td>0</td><td>0.04</td><td>0.06</td><td>0.03</td><td>0.03</td><td>0.02</td><td>0.02</td><td>0.01</td><td>0.01</td></tr></tbody></table>

Note: QV: Queue de Vache, CC: Cuvier Chatillon, P: Population census size, N: Genotyping sample size, Ac: Allele count, LP: Number of local private alleles, NLP: Number of non-local private alleles, Hobs: Mean observed heterozygosity across loci, Hexp: Expected heterozygosity, FIS: Inbreeding coefficient. Significant departure from HWE equilibrium (Fis) is indicated in bold, Manc: Average non-local ancestry, Vanc: Individual variance of the non-local ancestry proportion.

552 offspring individuals from subsequent generations. Prior to model comparison, a goodness of fit assessment was performed with the software U-CARE (Choquet et al. 2009) (Supporting Information 8).

Model Selection Procedure: All model comparisons were based on AICc, setting the threshold for a significant pairwise difference if ΔAICc ≥ 2. We conducted a two-step selection procedure (Grosbois et al. 2008). First, we identified the most parsimonious structure to models survival probabilities (S), detection probabilities (P), and transition probabilities (ψ) adult survival by testing the non-genetic effects of time, translocated populations, and life stage (Supporting Information 9). We then kept the best model structure from step one, to assess whether ancestry proportions and heterozygosity (included as individual covariates) influenced Sa probabilities. Heterozygosity was tested on a subset of 1163 individuals. Since most juvenile and senescent plants are too small for genetic sampling, investigating the effects of genetic ancestry and heterozygosity was possible only for Sa.

# 4 | Results

## 4.1 | Genetic Diversity and Population Differentiation

The non-local source was about two times more genetically diverse than the local one in terms of Ac and heterozygosity, but FIS values were similar, and significant within only non-local source (p-value = 0.02). The non-local source presented more private alleles (29) than the local one (8) (Table 1). The proportion of private alleles from the non-local source increased from 0.16 to about 0.27 between 1999 and 2021, while the proportion from the local source reduced from 0.21 to about 0.10 in both populations (Figure S1). As expected, due to the mixed-source nature of the translocated populations, Ac per locus, observed and expected heterozygosity were higher in the translocated populations compared to the local source population, and remained stable across

the years (Table 1; Tables S2-S4). Inbreeding coefficients (FIS) in the translocated populations were not significantly different from 0 between 2007 and 2014, but became significant in 2021, reaching 0.13 and 0.12 in QV and CC, respectively (Table 1). The pairwise FST between translocated populations at each sampling time ranged from 0 to 0.010 showing no temporal trend through time and being significant only in 2011 (Table S5). Conversely, FST progressively increased through time within each population, reaching 0.043 and 0.035 in QV and CC respectively when comparing the two most distant years.

## 4.2 | Dynamics of Admixture

The distribution of individual ancestry proportions varied between years and between translocated populations (Table 1; Figure 1). In 2007, the mean non-local ancestry among individuals in the two translocated populations (QV: 0.52 and CC: 0.49) was higher than the proportion of non-local clones among translocated individuals in 1999 (i.e., 1/3). The year of first observation had a positive impact on the ancestry proportion of the individuals in both models (including original clones or not) (model without original clones: estimate = 0.047 p-value < 0.01) (Tables S6 and S7). Translocated population and interaction effects were not significant. Non-local ancestry proportions increased over time to reach 0.62 and 0.65 in 2021 in QV and CC, respectively. Finally, the variance of non-local ancestry proportion decreased over time (Table 1; Figure 1). This was confirmed in both models, as the year of first observation had a significant positive impact on precision. Interaction between translocated population and year was significant and suggested a faster decrease in CC (Tables S6 and S7).

## 4.3 | Reproductive Effort and Growth

We found strong evidence of a quadratic relationship between reproductive effort and genetic ancestry in interaction with the translocated populations (p-values < 0.001 and ΔAICc = 35.75
<figure><img src="image_3.png" /><figcaption>**FIGURE 1** | Ancestry proportions in the two translocated populations. Individual ancestry proportions as genotype membership proportions from local (green) and non-local (violet) inferred with supervised STRUCTURE clustering in the translocated populations of A: Queue de Vache and B: Cuvier Châtillon for each sampling year. Dotted lines indicate the median non-local ancestry.</figcaption></figure>

compared to the non-additive linear model and ΔAICc = 38.63 compared to the quadratic additive model). In CC, predicted reproductive effort peaks at 36 flowers [95% CI: 28; 46] for 0.53 [0.50; 0.56] genetic ancestry proportion of non-local plants (p-value < 0.001). Minimums of reproductive effort, between 18 and 21 flowers, were associated with non-admixed plants from both origins. No significant relationship was found in QV. In addition, reproductive effort is on average higher in QV compared to CC (p-value < 0.001) but there was no evidence of an effect of the variables translocated or F1 (Figure 2A; Table S8). Interestingly, between the two linear models, the one with additive effects was the most parsimonious (ΔAICc = -1.68) and provided some evidence of a positive and significant relationship between the non-local ancestry and the reproductive effort in both translocated populations (β = 0.11, SE = 0.05, p-value = 0.02) (Table S9).

We also found a moderate statistical support for a quadratic relationship between growth and genetic ancestry (p-value = 0.03), with additive effect between translocated populations (ΔAICc = 3.28 compared to the linear and additive model; ΔAICc = 3.76 compared to the quadratic and non-additive model). Again, plants with a mixed ancestry (0.47 [0.44; 0.51]) presented twice as fast growth compared to non-admixed clones. Moreover, growth was 15% higher in QV than CC (Table S10; Figure 2B) and was positively correlated to the logarithm of the plant diameter (β = 0.63; SE = 0.02; *p* < 0.001). Notably the F1 and translocated variables had a significant negative effect on the growth rate while controlled by the ancestry proportions (respectively: β = -0.23, SE = 0.05, *p*-value < 0.001 and *β* = -0.28, SE = 0.10, *p*-value <

0.01, Table S10). The linear model did not reveal any significant relationship between growth and ancestry (Table S11).

## 4.4 | Survival

The first step of model selection for non-genetic predictors identified three informative predictors: translocated populations, life stages, and time as additive parameters on survival probabilities. Informative predictors for detection and transition probabilities were time and life stages (Table S12). Adult survival (Sa) varied through time and was higher in QV (average = 0.95 [0.93; 0.96]) than in CC (0.86 [0.83; 0.90]) (Figure S2). Detection of adults was on average high 0.75 [0.71; 0.83] and varied between 0.43 [CI 95% = 0.21; 0.68] and 0.89 [0.85; 0.92] among years (Figure S3).

We found no evidence of an effect of the ancestry as linear or quadratic effect on Sa (ΔAICc = -0.58). However, translocated individuals had a significantly lower Sa than non-translocated individuals (ΔAICc = 8.96).

In contrast to ancestry proportions, we found no evidence of a relationship between any fitness components and individual heterozygosity (Tables S13–S15), despite a significant Pearson correlation of 0.49 (*p*-value < 0.001) between individual ancestry and heterozygosity.

## 5 | Discussion

Our results show that the mixed-source translocation program of *A. grandiflora* in the Fontainebleau Forest successfully increased genetic diversity compared to each of the clonal sources translocated in 1999. Importantly, genetic ancestry was a predictor of two out of the three fitness components. Reproductive effort and growth were maximized for individuals resulting from the admixture between the local and non-local individuals. Moreover, we found a gradual increase in the proportion of non-local (Chilon) genetic material over time in the translocated populations, whereas we expected the initial 30% proportion—corresponding to the introduced non-local clones—to remain stable, as demonstrated by Verdu and Rosenberg (2011). Importantly, these results were obtained in both translocated populations, and no genetic divergence was observed through time between the two translocated populations, which is contrary to the expected effects of genetic drift. This may suggest that similar hybridization-related evolutionary processes, such as convergent natural selection, independently operated in both translocated populations.

Hybridization between clones of local and non-local origin occurred from the very first years after translocation and was not limited to the production of F1 individuals, despite a strong genetic divergence between sources (*F*<sub>ST</sub> > 0.3, Table S5). Quadratic models relating fitness to ancestry proportions outperformed linear models for the yearly number of flowers in the CC population and for growth in both populations. These models associated maximum fitness to plants having an approximately 50/50 mixed ancestry from both sources, suggesting a heterosis effect following translocation (Charlesworth and Willis 2009; Birchler et al. 2010; Waller 2021; Okubo and Kaneko 2022). Here, outbreeding may have masked the effects of deleterious
<figure><img src="image_4.png" /><figcaption>**FIGURE 2** | Predicted A. Reproductive effort (annual number of flowers) and B. Growth (yearly individual growth rate). Plants with ancestry proportions of 1 and 0.5 are respectively clones from non-local founders and plants with a mixed ancestry between non-local and local founders. The lines indicate the fitted values, with associated 95% confidence intervals. The maxima of the quadratic functions are marked by vertical dashed lines, with their 95% confidence intervals represented by horizontal grey rectangles. Detailed model output is provided in Tables S8 and S10. The density plots below the prediction graphs show the distribution of observations for A and B. Due to the presence of shared alleles, no plants including clones were assigned to an ancestry of <0.09 or >0.91. We set up clones manually to 0 or 1 of non-local ancestry, explaining the gap between admixed and clones in ancestry. Both models were controlled for the mean of the log of the plant diameter, F1 and the translocated variable.</figcaption></figure>

recessive mutations responsible for inbreeding depression in both source populations (Bottin et al. 2007). Surprisingly, individual heterozygosity was not found to be a significant predictor of fitness, even though its effect has been well established in many other studies (Feuerstein et al. 2024). Our study may lack the necessary statistical power to detect this effect due to the limited number of microsatellites available for analysis. Heterosis was likely not driven only by F1 hybrids and rather extended beyond the first generation. If so, we would indeed expect the F1 variable to capture most of the variance, substantially reducing the quadratic effect of ancestry on fitness components. Instead, the F1 effect was non-significant for reproductive effort, and was even negative in the growth model, potentially reflecting a more complex relationship than a simple quadratic pattern constrained by two parameters.

Heterosis can partly explain the increase of non-local ancestry over time observed in both populations. However, non-local ancestry further rises to around 65% over time, instead of stabilizing around 50%, which is consistent with our findings that individuals with higher non-local ancestry tend to produce more flowers. Given the large number of new alleles introduced by non-local individuals, some may be advantageous, potentially explaining both the persistent fitness benefits and the increasing non-local ancestry over time through adaptive introgression (Suarez-Gonzalez et al. 2018). In light of the new questions raised by our findings, we advocate that future research should incorporate high-density genome-wide distributed markers such as SNPs which would enable the evolutionary outcomes of the translocation program to be studied in greater detail.

Interestingly, our results suggest that the observed variation in survival is more likely driven by the translocation process itself rather than genetic ancestry, with plants which experienced

translocation having reduced probability of survival relative to subsequent generations. Moreover, translocated plants displayed lower survival independently of genetic background. Physiological stress due to the translocation process itself, with in-vitro plants being exposed to new conditions at the translocation sites, could have negatively influenced survival. Indeed, various parameters, often species- and habitat-specific, have been shown to impact the survival of translocated plants (D'Agostino et al. 2024). However, because clones constitute a large proportion of our dataset, and because the translocation effect and genetic ancestry are correlated, and thus potentially confounded, future studies would require more plants from subsequent generations with various ancestries to better disentangle these effects.

From a conservation perspective, our results are in line with earlier research (Pickup et al. 2013; Frankham 2016; Van Rossum et al. 2020), advocating that mixed-source translocation involving a more genetically diverse non-local source appears to have positive outcomes, revealing no signs of maladaptation or outbreeding depression, but instead conferring an immediate gain of fitness that persists over multiple generations. Nevertheless, the increase in non-local ancestry over time in both translocated populations suggests that the introduction of non-local individuals might not necessarily enable the local gene pool to be preserved in the long term. This raises broader philosophical and ethical considerations regarding the ultimate goals of conservation, whether the primary objective is to maintain the presence of a population or to conserve its unique genetic diversity (Rohwer and Marris 2015). Preventing this genetic swamping has been shown to require considering not only the number of non-local individuals but also the size and spatial arrangement of translocated populations, underscoring the need for preliminary assessments to balance genetic diversity gains with the conservation of local gene pools (Harris et al. 2019; Bragg et al. 2021).
Eventually, census sizes of both translocated populations declined to approximately 100 individuals in 2021, potentially due to a very low germination or to poor seedling survival. These two demographic components could unfortunately not be considered in our study since very young plants typically were not sampled for genotyping because of their extremely small size (just a few millimeters in height). *A. grandiflora* may also be suffering from climate change, particularly in these lowland environments from which natural shifting to higher altitudes to track better conditions is not possible. Thus, the success of genetic rescue operations must also include a proactive adaptive component to anticipate global changes, which is an important consideration regarding assisted gene flow (Aitken and Whitlock 2013).

To conclude, such long-term, individual-based monitoring programs of plant translocations are extremely important despite being still too rare. Such data collection, spanning approximately five generations, brings new insights about the genuine relationships between genetic admixture and demography, and are therefore essential for assessing the effectiveness of mixed-source translocation and assisted gene flow strategies.

## Acknowledgment

We sincerely thank the numerous interns and staff members from CESCO (Centre d'Écologie et des Sciences de la Conservation) who contributed to data collection over the 25 years of this project. A special acknowledgment goes to Claire Stragier, Adrien Noirault, Arthur Fauviau, Nelida Padilla, Marie Lamour and Simon Leriche whose work was important conducting the initial data analyses. Their contributions significantly helped shape the first insights of this study. We thank the technical platforms "Paléogénomique et Génétique Moléculaire P2GM" from the MNHN at the Musée de l'Homme and "Service de Systématique Moléculaire SSM" from the MNHN, for conducting molecular genetics analyses.

## Funding

This research was financially supported by the Conservatoire Botanique National du Bassin Parisien and benefited from technical and financial support from the Office National des Forêts (ONF). Funding was also provided by the French National Research Agency (ANR) through the FLORES program (Project ANR-22-CE02-0010). JBM, AR and NM benefited from the support of the EU program Biodiversa+ and Water JPI joint call 2020-2021, under the BiodivRestore ERA-NET Cofund (TRANSLOC, GA number 101003777). Furthermore, the École Normale Supérieure de Lyon contributed by funding a six-month internship.

## Conflicts of Interest

The authors declare no conflicts of interest.

## Ethics Statement

This study was conducted in accordance with ethical guidelines for plant research and conservation. The translocation and genetic sampling of *Arenaria grandiflora* were authorized by the Conseil National de la Protection de la Nature (CNPN), Ministry of Ecological Transition and Territorial Cohesion, France), which granted permission for the partial destruction of this endangered species for scientific research purposes of the program in 1997. All procedures aimed to minimize impact on natural populations while ensuring the collection of valuable conservation data. No human participants or vertebrate animals requiring ethical approval were involved in this study.

## Data Availability Statement

Data and scripts are available in article Supplementary Materials.

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**Supporting Information**

Additional supporting information can be found online in the Supporting Information section.

Supplementary material: conl70012-sup-0001-

SuppMat.docx Supplementary material: conl70012-sup-0002-data.zip