# Inter-annual dispersal stability within the Atlantic salmon metapopulation from the Bay of Biscay

Anthony Fontaine, M. Vignon, Hélène Tabouret, A. Holub, G. Barranco, S.

Bosc, I. Caux, D.J. Nachón, J. Elso, P. Caballero, et al.

► To cite this version:

Anthony Fontaine, M. Vignon, Hélène Tabouret, A. Holub, G. Barranco, et al. Inter-annual dispersal stability within the Atlantic salmon metapopulation from the Bay of Biscay. Fisheries Research, 2025, 284, pp.107323. 10.1016/j.fishres.2025.107323. hal-05002432

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# Inter-annual dispersal stability within the Atlantic salmon metapopulation from the Bay of Biscay

A. Fontaine<sup>a, *, 1</sup>, M. Vignon<sup>b</sup>, H. Tabouret<sup>a</sup>, A. Holub<sup>c</sup>, G. Barranco<sup>c</sup>, S. Bosc<sup>d</sup>, I. Caux<sup>d</sup>, D.J. Nachón<sup>e</sup>, J. Elso<sup>f</sup>, P. Caballero<sup>g</sup>, C. Pécheyran<sup>a</sup>, G. Bareille<sup>a</sup>

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

## ARTICLE INFO

Guido Plaza

### Keywords:

Homing

Straying

Temporality

Life history

## ABSTRACT

Natural biocomplexity, characterized by genetic and phenotypic diversity, enhances the resilience of meta-populations by buffering against environmental fluctuations and stochastic events. This diversity acts as a natural bet hedge, where different populations may thrive under varying conditions, ensuring the overall stability and survival of the species. Regarding migratory salmonids, the portfolio effect is particularly important due to their complex life cycles and reliance on diverse and interconnected habitats along their ontogenic migration. To date, little is known about the temporal variability of dispersal movements, both in terms of intensity and direction. Yet, dispersal behavior is expected to vary over the next decades, in response to the overwhelming stressors (biotic and abiotic, from natural and anthropogenic sources) that salmonids are facing. In this study, we investigated Atlantic salmon (*Salmo salar*) dispersal patterns on a regional scale in western Europe, i.e. the Bay of Biscay, using otolith chemistry (Ba/Ca, Sr/Ca, and ⁸⁷Sr/⁸⁶Sr). Our results display significant connectivity between salmon subpopulations, particularly that of the Adour, which appears to be both a source (emigration rate: 1.3 ± 1.0 %) and a sink (immigration rate: 4.9 ± 4.3 %) for related populations. Although changes in Atlantic salmon dispersal could be expected with the acceleration and intensification of global change effects, our study shows very stable philopatric and dispersal rates over the last decade. Combined with genetic studies, our observations suggest that the metapopulation structure may help in the stability of the system, probably via a demographic but also perhaps genetic rescue effect.

## 1. Introduction

Philopatry or the “homing” to natal spawning areas is recognized to be a fundamental behavior for the maintenance of many migratory species. This ecological trait is found in many taxa, from marine mammals (Baker et al., 2013) to reptiles (Lee et al., 2007), insects (Buehlmann et al., 2013) and birds (Cava et al., 2016). Returning mature adults that migrate to natal sites favor match occurrence between locally-adapted mates in breeding areas, therefore strengthening fitness benefits for the next generations (Dittman and Quinn, 1996; Clutton-Brock and Lukas, 2011). Alternatively, dispersal or “straying” is

also a key component in the dynamic of wild populations, highly adaptative to proximate and ultimate environmental cues (Coulson, 2016; Lamarins, 2022), which can foster natural resilience (Perrier et al., 2010). Thus, populations that occupy spatially distinct environments are, to an extent, interconnected through the exchange of individuals, resulting in a metapopulation structure (Hobson et al., 2004; Schtickzelle and Quinn, 2007). From an evolutionary perspective, this dynamic network prevents natural populations from synchronous responses to environmental changes, which may ultimately threaten their overall stability (Anderson et al., 2015), while still promoting the adaptation of distinct populations to a given ecosystem. Additionally, dispersal may

<footnote><sup>a</sup> University of Pau and the Adour Region, E2S UPPA, CNRS, IPREM, MIRA, Pau, France</footnote>

<footnote><sup>b</sup> University of Pau and the Adour Region, E2S UPPA, INRAE, ECOBIOP, Saint-Pée-sur-Nivelle, France</footnote>

<footnote><sup>c</sup> MIGRADOUR, Gan, France</footnote>

<footnote><sup>d</sup> MIGADO, Le passage, d'Agen, France</footnote>

<footnote><sup>e</sup> Instituto Español de Oceanografía (IEO, CSIC), Centro Oceanográfico de Vigo, Subida a Radio Faro, 50-52, Vigo, Pontevedra 36390, Spain</footnote>

<footnote><sup>f</sup> Gestión Ambiental de Navarra (GAN-NIK). Área de Gestión Piscícola, Pamplona-Irunea, Spain</footnote>

<footnote><sup>g</sup> Servicio de Patrimonio Natural de Pontevedra, Consellería de Medio Ambiente, Xunta de Galicia, Spain</footnote>

<footnote>* Corresponding author.
E-mail addresses: anthony fontaine@univ-pau.fr, anthony fontaine56@gmail.com (A. Fontaine).</footnote>

<footnote n="1">¹ https://orcid.org/0000-0001-5779-5789</footnote>
allow individuals to colonize new or restored habitats (Willmes et al., 2021), decrease the risk of genetic isolation (Källo et al., 2022b) and potentially provide demographic and/or genetic and/or evolutionary rescue effects (Lamarins, 2022).

Along the freshwater-sea continuum, many salmonid populations exhibit a metapopulation structure (Schtickzelle and Quinn, 2007), although other strategies (e.g. freshwater residence or precocious maturation) are observed in natura (Birnie-Gauvin et al., 2019). While natural populations generally express an elevated homing rate (Lamarins and Buoro, 2024), studies have shown that salmonid species can exhibit a wide variety of life histories (Erkinaro et al., 2019), and that globally, the role of dispersal has been overlooked (Birnie-Gauvin et al., 2019; Källo et al., 2022a). Yet, the sustainability of this wide and complex diversity (i.e. biocomplexity) in life histories is crucial to prevent population synchrony and destabilizing the portfolio effect (Greene et al., 2010; Schindler et al., 2010; Satterthwaite and Carlson, 2015; Sturrock et al., 2020), especially in a global change context (Anderson et al., 2015; Herbold et al., 2018; Crozier and Siegel, 2023). However, although dispersal is a desirable trait in a healthy metapopulation (Dittman and Quinn, 1996), excessive dispersion can be detrimental for both the donor population, mainly via the loss of potential mature adults, and the receiver population with competition on spawning grounds and the loss of local genetic adaptations (Keefer and Caudill, 2014). Unfortunately, with the unprecedented increase in anthropogenic pressures (e.g. habitat alterations, pollution, hatchery releases) and environmental stressors (e.g. extreme climatic events such as floods or droughts) on aquatic ecosystems, dispersal rate of salmonid species is expected to exhibit substantial variation in the next years (Crozier et al., 2008; Sturrock et al., 2019). This variation may occur both temporally (i.e. inter-annual changes) and spatially (i.e. differences in emigration distance from natal stream) depending on the environmental stressors encountered, potentially jeopardizing healthy populations.

The Atlantic salmon (*Salmo salar*, Linnaeus 1758) is perhaps the most emblematic and iconic salmonid because of its important cultural, economic, and recreational role in our contemporary world (Thorstad et al., 2021). In response to the collapse of wild European and North-American populations in the late 1980s and the beginning of the 1990s (Chaput, 2012; ICES, 2019), hatchery programs have been developed in many rivers and watersheds where Atlantic salmon breed or used to breed (ICES, 2021). As a consequence of supportive breeding actions, many systems historically inhabited by Atlantic salmon are now “hatcher-y-dominated” and numerous undesirable effects on wild populations have been pointed out in the literature, such as the Ryman-Laikre effect (Ryman and Laikre, 1991; Hagen et al., 2021). Furthermore, the exacerbated dispersal rate of individuals from artificial reproduction (Jonsson et al., 2003) poses behavioral and genetic threats to wild populations (Quinn, 1993; Perrier et al., 2013; Nilsen et al., 2023). These ecological concerns extend beyond *Salmo salar* as similar warnings have been raised for Pacific salmon (*Zaporozhets and Zaporozhets, 2012; Ford et al., 2015*) and more broadly for salmonid species, as recently reviewed by McMillan et al. (2023). From a management perspective, as a metapopulation gathers source and sink populations by definition, one of the main tasks would therefore be to identify populations that “provide” or “receive” individuals. Considering the natural biocomplexity of wild populations and the massive effects of hatchery releases on salmon metapopulation (Johnson et al., 2012; Satterthwaite and Carlson, 2015; May et al., 2024), effective quantification and understanding of “source-sink dynamics” are crucial to preserve and maintain natural populations (Johnson et al., 2012; Willmes et al., 2021) in this very complex mosaic. Ultimately, since population structure can occur in relatively small systems (Gudmundsson et al., 2013), it emphasizes the need to address dispersal rate at multiple geographical scales, not to also mention temporal scales as the direction of gene flow varies with environmental conditions (Palstra et al., 2007).

In the North-East Atlantic Ocean, the Bay of Biscay represents the southern range of European Atlantic salmon populations, with

individuals from northern Portugal and north-western Spain being at the edge of the species distribution (Hatfield, 2020). From the Bretagne region (north-west France) to the Galicia (northern Spain), seven regional populations are identified: Bretagne, Allier, Garonne-Dordogne, Adour-Nivelle, Bidasoa (as part of the Gipuzkoa region), Asturias and Galicia (Fig. 1) (Blanco et al., 2005; Saura et al., 2006; Perrier et al., 2013); and they all exhibit very different characteristics in term of size (annual number of returning adults), composition (proportion of one sea winter (1SW) and multi-sea winter (MSW) individuals), habitat configuration (patchy riverine ecosystems and/or large rivers) and structure (proportion of wild and hatchery-origin fish). Interestingly, over the 2010–2020 decade, most populations continued to decline or remained stable at low levels of abundance (Almodóvar et al., 2023; Lebot and Rivot, In press), except for the Garonne-Dordogne and the Adour, both of which recorded a global increase in the number of returning adults, which is unrivaled in the Bay of Biscay. Among the hypotheses that can be drawn to explain these observations, increased dispersal from the surrounding populations would be a plausible assumption in the context of the ongoing climate change (e.g. higher immigration rate in the Garonne-Dordogne and the Adour resulting from less favorable environmental conditions in other regions) and the intensive hatchery programs (i.e. higher dispersal rate of hatchery-origin individuals) in adjacent basins (Perrier et al., 2011a, 2013; Almodóvar et al., 2023). To date, a limited number of studies have examined the level of dispersal in the Bay of Biscay for Atlantic salmon and most of these results rely on genetics (Valiente et al., 2010; Perrier et al., 2011b, 2013), tagging (Leunda et al., 2013) or the combination of genetics and tagging (Consuegra and García de Leániz, 2007; Kuparinen et al., 2010). However, tagging programs often rely on stocked individuals only and genetics generally provide information on effective dispersal (gene flow), yet, an increase in dispersal does not necessarily translate into successful breeding of migrants. Besides these two methods, otolith chemistry has been demonstrated to be a robust and reliable tool, complementary to genetics and telemetry, to quantitatively assess the emigration and immigration rates (Willmes et al., 2021; Källo et al., 2023).

Otoliths or “ear stones” are calcified structures located in the inner ear of teleost fish (Panfili et al., 2002) that grow incrementally, at a daily rate, making them natural tags of fish movements (Campana, 1999; Campana and Thorrold, 2001; Elsdon and Gillanders, 2003). In sufficiently contrasted environments, based on the geochemical differences among estuarine and freshwater habitats, otolith chemistry has been successfully used for more than 20 years to track the life histories and investigate the metapopulation structures of migratory fish species (Tabouret et al., 2010; Martin et al., 2013b; Randon et al., 2018; Nachón et al., 2020; Teichert et al., 2022). Among the multiple elements that can be measured in otoliths, strontium (Sr), barium (Ba), and the isotopic ratio of Sr ($^{87}$Sr/$^{86}$Sr) are the most employed to reconstruct life histories as they primarily reflect past environmental conditions, outbalancing the “physiological noise” (Hüssy et al., 2020; Bareille et al., 2024). Regarding salmon, Pacific salmon species (*Oncorhynchus* spp.) have received more attention in otolith studies (Barnett-Johnson et al., 2008; Hamann and Kennedy, 2012; Brennan et al., 2015; Sturrock et al., 2015; Willmes et al., 2021) compared to Atlantic salmon, especially in western Europe. Yet, Atlantic salmon populations from the Bay of Biscay are essential to study as populations at the edge of the species distribution are more vulnerable to global change (Lassalle et al., 2009; Valiente et al., 2010; Nicola et al., 2018). In the Adour system, a previous study has demonstrated the potential of otolith chemistry to distinguish Atlantic salmon origins on a river scale (i.e. intra-basin, Martin et al., 2013a), hence providing first insights into dispersal rates in relatively small areas (Martin et al., 2013b). However, one limitation of this study is the lack of perspectives considering the exchange of individuals in the Bay of Biscay on a larger spatial scale, particularly in a global change context where dispersal is expected to increase with extreme climate events. Specifically, in our contemporary world, where environmental
<figure><img src="image_4.png" /><figcaption>Fig. 1. Map of the sampling sites (regional scale, n = 9; river scale, n = 33) used in this study, from North to South: Bretagne, Allier, Dordogne, Garonne, Adour, Nivelle, Bidasoa, Asturias and Galicia. The number of rivers available from each watershed is specified in brackets.</figcaption></figure>

and anthropogenic pressures are constantly increasing, both on continents and seas (e.g. chemical and light pollution, overfishing, global warming, match-mismatch dynamics, introduction of exotic species), we could expect a shift in migratory flows, in terms of quantity but also of connectivity (i.e. direction and intensity). This study examines Atlantic salmon dispersal patterns and metapopulation dynamics among the

large rivers draining the southern Bay of Biscay (North-East Atlantic Ocean): the Garonne-Dordogne, Adour, and Nivelle locations. Using otolith chemistry to determine fish origin, with chemical signatures representing every main population from the Bay of Biscay, we address two main objectives: (i) to investigate the spatial philopatry and dispersal rates within each so-called population in the southern Bay of

<caption>**Table 1**</caption>

Geographical and ecological characteristics of the locations used in this study. Population sizes range from 2011 to 2020 to ensure comparable information between sites, as estimations were not available for some locations prior to 2011.

<table><thead><tr><th>Geographic area</th><th>Substrate type</th><th>Maximum length (km)</th><th>Distance to the Adour estuary (km)</th><th>Distance to the Garonne-Dordogne estuary (km)</th><th>Atlantic salmon population size</th><th>Hatchery releases (10<sup>5</sup>/year)</th></tr></thead><tbody><tr><td>Bretagne</td><td>Granitic/metamorphic (Paleozoic)</td><td>149</td><td>~550</td><td>~300</td><td>11100 ± 1600<sup>a</sup></td><td>&lt; 0.1<sup>h</sup></td></tr><tr><td>Allier</td><td>Metamorphic (Paleozoic)/volcanic</td><td>1006</td><td>~440</td><td>~210</td><td>660 ± 260<sup>b</sup></td><td>6.0 – 8.0</td></tr><tr><td>Dordogne</td><td>Mixed sedimentary/metamorphic</td><td>483</td><td>~230</td><td></td><td>380 ± 160<sup>c</sup></td><td>3.6 – 10</td></tr><tr><td>Garonne</td><td>Mixed metamorphic (Paleozoic)/ sedimentary (Cenozoic)</td><td>525</td><td>~230</td><td></td><td>130 ± 50<sup>c</sup></td><td>4.0 – 8.0</td></tr><tr><td>Adour</td><td>Mixed sedimentary (Cenozoic)/ metamorphic (Paleozoic)</td><td>308</td><td></td><td>~230</td><td>5200 ± 1200<sup>d</sup></td><td>2.4 – 7.4</td></tr><tr><td>Nivelle</td><td>Sedimentary (Cenozoic)</td><td>39</td><td>~19</td><td>~250</td><td>100 ± 25<sup>d</sup></td><td>Null</td></tr><tr><td>Bidasoa</td><td>Sedimentary (Cenozoic)</td><td>66</td><td>~27</td><td>~240</td><td>475 ± 130<sup>e</sup></td><td>0.5 – 1.0</td></tr><tr><td>Asturias</td><td>Sedimentary (Cenozoic)/ metamorphic (Paleozoic)</td><td>145</td><td>~330</td><td>~560</td><td>3100 ± 620<sup>f</sup></td><td>2.4 – 5.3</td></tr><tr><td>Galicia</td><td>Mainly granitic</td><td>340</td><td>~700</td><td>~930</td><td>120 – 470<sup>g</sup></td><td>~1.5<sup>g</sup></td></tr></tbody></table>

Notes: Distances between each geographic area was estimated along the coast line. In addition, only one sub-basin (Pau) out of the three (Pau, Oloron, and Nives) is subjected to hatchery releases in the Adour area. Data of Atlantic salmon population sizes are from

<footnote>a Lebot and Rivot (In press);</footnote>

<footnote>b https://www.logrami.fr;</footnote>

<footnote>c https://www.migado.fr;</footnote>

<footnote>d https://www.migradour.com;</footnote>

<footnote>e Navarre government;</footnote>

<footnote>f NASCO (2020);</footnote>

<footnote>g Caballero Javierre (2020);</footnote>

<footnote>h Mainly smolt stage, see https://www.observatoire-poissons-migrateurs-bretagne.fr</footnote>
Biscay and (ii) to assess the temporal variation in the exchange of in-
dividuals using a ten-year collection of otoliths from the
Garonne-Dordogne and Adour systems. We expect that large and sus-
tainable populations should act as a source of individuals for adjacent
small populations and dispersal rate may vary temporally (both
increasing and decreasing) because of the pressure of multiple envi-
ronmental stressors.

## 2. Materials and methods

2.1. *Sampling sites and population characteristics*

2.1.1. *The southern Bay of Biscay*

Three distinct areas are home to Atlantic salmon in the southern Bay of Biscay, namely the Garonne-Dordogne to the north and the Adour and Nivelle to the south (André et al., 2021). The Garonne-Dordogne system gathers two large-river sub-basins, i.e. the Dordogne and Garonne, that join together at the Bec d'Ambés to form the estuary of Gironde (Carry et al., 2023). The sub-population of the Dordogne is about 3-times larger than the Garonne's sub-population (Table 1), and they are both largely set on fish from hatchery releases (Vandeputte et al., 2021). In the last decade, the number of mature salmon returning to the Dordogne sub-basin was found to increase (https://www.migado.fr) whereas it stagnated in the Garonne sub-basin. Since geochemical signatures strongly differ between the Garonne which drains substrates from the Pyrenean mountains and the Dordogne which flows over rocks from the Massif Central mountains (Willmes et al., 2018), and considering the significant difference in sub-population dynamics, these two geographical areas have been examined independently. About 230 km to the south (Fig. 1), the Adour system represents a medium-size basin (Table 1) which can be separated into three sub-basins: Nives, Oloron, and Pau (Bareille et al., 2017). The Adour basin hosts the largest population in the southern part of France (Martin et al., 2013a) and returning mature adults can be found in thirteen rivers (Table 1, Fig. 1). Nevertheless, the population is not equally distributed across sub-basins as approximately 70 % of the mature salmon that have escaped the fisheries would ascend to the Oloron sub-basin, 20 % to Pau, and only 10 % to the Nives (https://www.migradour.com/). Additionally, native and self-sustaining sub-populations are found in the Oloron and the Nives while the Pau sub-population is still being restored with hatchery programs (Table 1). Finally, a few kilometers southwest of the Adour estuary, the Nivelle represents a small coastal river (Fig. 1) and carries the smallest, native and self-sustaining French Atlantic salmon population (Table 1). Initially clustered with the Adour (Perrier et al., 2013), a further study, with a larger sample size, has shown that the genetic structure of the Nivelle population was significantly different from the Adour (Perrier et al., 2014). Hence, combined with the distinct watershed system and population characteristics from those of the Adour, the Nivelle has been investigated independently.

2.1.2. *Additional sampling sites in the Bay of Biscay*

The other salmon collected in this study have been chosen to cover
most of the populations potentially connected to those of the Garonne,
Dordogne, Adour, and Nivelle. Southwards from the main salmon stocks
under study, along the Bay of Biscay, we included samples from the
Bidasoa and two Spanish regions, i.e. the Asturias-Cantabria (northern
part of Spain) and the Galicia (north-western part of Spain, i.e. the
southern limit of the Atlantic salmon distribution area) (Fig. 1). They
mainly consist of small coastal rivers (Table 1) and hatchery programs
are, here again, important to conserve and preserve natural populations
(although several studies show that stocking practices are questionable
or ineffective if wild individuals and suitable habitats are found
(Laffaille, 2011; Bacon et al., 2015)). Northwards from the main salmon
stocks under study along the Bay of Biscay, the Allier basin is defined by
large and long rivers intensively sustained by fry stocking, in contrast to
the Bretagne river system that is shaped by many small coastal rivers and

where populations are mostly sustained by natural reproduction (Table 1). Based on abundance, count, or fishing catch data (from references in Table 1), we estimated that the sampled rivers account for, on average, 92 %, 87 %, 62 %, and 60 % of the returning salmon in the Bretagne, Allier (Loire), Asturias, and Galicia regions, respectively. However, no independent river-specific data are available to provide an estimate for the Cantabrian region (Almodóvar et al., 2023). A summary of watershed characteristics, population sizes, and possible management actions are found in Table 1.

2.2. *Sample collection and analysis*

2.2.1. *Otolith sampling*

A total of 1404 and 304 sagittal otoliths (referenced as otolith from hereafter) were collected from adult salmon at the Adour-Nivelle and Garonne-Dordogne sites, respectively. Since the presence of vaterite can lead to significant differences in chemical composition compared to aragonite (Pracheil et al., 2019), fully vateritic otoliths were excluded, accounting for 10 % and 14 % of the samples from the Adour-Nivelle and Garonne-Dordogne sites, respectively. The present study takes advantage of a previous dataset available from Martin et al. (2013a), where the authors demonstrate the reliability of otolith chemistry to assign a geographical origin (river-scale) for Atlantic salmon in the Adour system. Here, we merged the data produced in this previous study (exclusively from the Adour basin; assignment database: *n* = 168 parr from twelve rivers, unknown origin database: *n* = 180) with a new dataset (assignment database: *n* = 345; unknown origin database: *n* = 1362) including samples from the nine regions of the Bay of Biscay aforementioned. Additional samples in the assignment database include both juvenile and adult otoliths as juvenile were not available at some locations (Bretagne, Allier, Garonne-Dordogne, Bidasoa, Asturias, and Galicia). Parr were only accessible for the Adour, Nivelle, and partially for the Allier basin. Details of the complete otoliths collection used in this study (assignment database: *n* = 513; unknown origin database: *n* = 1542) are presented in Table 2.

2.2.1.1. Assignment database. This dataset was exclusively composed to create and train a discriminant model allowing us to further infer the natal origin from an unknown individual. In the Adour basin, new otoliths of salmon juveniles (n = 38, from nine rivers) were collected in 2014 and 2017 during the survey of juvenile abundance. Parr otoliths from the Nivelle (n = 8, from 2011) were provided by the Institut Na- tional de Recherche pour l'Agriculture, l'Alimentation et l'Environnement (INRAe) at Saint-Pée-sur-Nivelle (ECP INRAe, 2018). In the Allier basin, otoliths of smolts (n = 8) were sampled in 2008 by a rotary screw trap as they emigrated from the Allier river and adults (n = 41) were captured in 2013 during their spawning migration (both stages were collected for sustaining programs). The otoliths of adult salmon from other systems (Bretagne, n = 58; Garonne-Dordogne, n = 103; Bidasoa, n = 19; Astu- rias, n = 24 and Galicia, n = 46) were recovered from 2007 to 2015 from recreational anglers, institutions that collect broodstocks during their spawning migration for hatchery programs and carcass surveys. In the Bidasoa, otoliths have been collected from nasally tagged (using coded wire tags (CWT)) individuals in this river (information supplied by the Navarre government). Individuals from the Garonne-Dordogne originate from fish farm batches certified by parental genetic analysis, and whose site of release is known (Vandeputte et al., 2021). We also verified that the geochemical signatures of otoliths from the Garonne-Dordogne were consistent with those of the waters in which fry had been released. Finally, for all remaining locations, i.e. Bretagne, Asturias, and Galicia, only otoliths from adults for which the chemical composition of the otolith matched that of the river of capture were selected.

2.2.1.2. *Unknown origin database.* To investigate the dispersal behavior
<caption>**Table 2**</caption>

Number, origin (capture site), and sampling year(s) of Atlantic salmon (*Salmo salar*) used in the assignment database and the unknown origin database in this study.

<table><thead><tr><th colspan="3">Sampling site</th><th colspan="3">Assignment database</th><th colspan="2">Unknown origin database</th></tr><tr><th>Geographic area</th><th>Sub-basin</th><th>River</th><th>Number of known origin juveniles</th><th>Number of known origin adults</th><th>Years of collection</th><th>Number of unknown origin adults</th><th>Years of collection</th></tr></thead><tbody><tr><td rowspan="12">Adour</td><td rowspan="5">Nives</td><td>Arnéguy</td><td>20</td><td></td><td>2011, 2014, 2017</td><td rowspan="12">1259</td><td rowspan="12">2009–2018</td></tr><tr><td>Béhérobie</td><td>21</td><td></td><td>2011, 2014, 2017</td></tr><tr><td>Laurhibar</td><td>9</td><td></td><td>2011, 2014, 2017</td></tr><tr><td>Nive</td><td>6</td><td></td><td>2011</td></tr><tr><td>Aspe</td><td>14</td><td></td><td>2009, 2010, 2014, 2017</td></tr><tr><td rowspan="5">Oloron</td><td>Lourdios</td><td>12</td><td></td><td>2009, 2010</td></tr><tr><td>Oloron</td><td>22</td><td></td><td>2009, 2010, 2014, 2017</td></tr><tr><td>Ossau</td><td>31</td><td></td><td>2009, 2010, 2014, 2017</td></tr><tr><td>Saison</td><td>15</td><td></td><td>2009, 2010, 2014, 2017</td></tr><tr><td>Verts</td><td>10</td><td></td><td>2009, 2010</td></tr><tr><td rowspan="2">Pau</td><td>Ouzom</td><td>16</td><td></td><td>2006, 2009, 2010, 2014, 2017</td></tr><tr><td>Pau</td><td>30</td><td></td><td>2006, 2009, 2010, 2014, 2017</td></tr><tr><td rowspan="2">Dordogne</td><td rowspan="2">Dordogne</td><td>Vézère</td><td>60</td><td></td><td>2007–2012</td><td rowspan="2">261</td><td rowspan="2">2007–2016</td></tr><tr><td>Vézère</td><td>13</td><td></td><td>2007–2012</td></tr><tr><td rowspan="2">Garonne</td><td rowspan="2">Ariège</td><td>Garonne- Neste</td><td>15</td><td></td><td>2007–2012</td><td rowspan="2">22</td><td rowspan="2">1997–2014</td></tr><tr><td>Garonne- Neste</td><td>15</td><td></td><td>2007–2012</td></tr><tr><td>Nivelle</td><td rowspan="2">Allier</td><td>Nivelle</td><td>8</td><td></td><td>2011</td><td rowspan="2">22</td><td rowspan="2">1997–2014</td></tr><tr><td rowspan="2">Allier</td><td>Alagnon- Sioule</td><td>8</td><td></td><td>2009–2013</td></tr><tr><td rowspan="5">Asturias</td><td rowspan="2">Allier</td><td>Deva-Cares</td><td>8</td><td></td><td>2009–2013</td><td rowspan="5"></td><td rowspan="5"></td></tr><tr><td>Sella</td><td>33</td><td></td><td>2012, 2013</td></tr><tr><td rowspan="2">Allier</td><td>Nansa-Pas- Ason</td><td>15</td><td></td><td>2012, 2013</td></tr><tr><td>Bidasoa</td><td>19</td><td></td><td>2012, 2013</td></tr><tr><td rowspan="7">Bidasoa Bretagne</td><td rowspan="2">Aven</td><td>Bidasoa</td><td>2</td><td></td><td>2015</td><td rowspan="7"></td><td rowspan="7"></td></tr><tr><td>Blavet</td><td>2</td><td></td><td>2015</td></tr><tr><td rowspan="2">Elle</td><td>Jet</td><td>27</td><td></td><td>2015</td></tr><tr><td>Odet</td><td>5</td><td></td><td>2015</td></tr><tr><td rowspan="2">Elle</td><td>Scorff</td><td>12</td><td></td><td>2015</td></tr><tr><td>Lerez</td><td>10</td><td></td><td>2015</td></tr><tr><td rowspan="4">Galicia</td><td rowspan="2">Minho</td><td>Lerez</td><td>11</td><td></td><td>2008–2013</td><td rowspan="4"></td><td rowspan="4"></td></tr><tr><td>Tambre</td><td>4</td><td></td><td>2008–2013</td></tr><tr><td rowspan="2">Ulla</td><td>Tambre</td><td>12</td><td></td><td>2008–2013</td></tr><tr><td>Ulla</td><td>19</td><td></td><td>2008–2013</td></tr></tbody></table>

of Atlantic salmon populations in the southern Bay of Biscay, new otoliths from MSW adults have been collected in the Garonne-Dordogne (n = 261, from 2007 to 2016), the Adour (n = 1079, from 2012 to 2018) and the Nivelle (n = 22, from 1997 to 2014) systems (Table 2). In the Adour, we merged the 180 otoliths of adult salmon published by Martin et al., (2013b) with our new otolith collection to reassign every individual (n = 1259) using the same method. Atlantic salmon heads were provided by professional fishermen in the Adour estuary as well as recreational anglers (Bretagne, Adour, and Nivelle), carcass surveys on spawning grounds (Adour and Nivelle) and organizations that harvest salmon during their spawning migration for rearing programs (Allier, Garonne-Dordogne, Bidasoa, Asturias and Galicia).

### 2.2.2. Otolith preparation and chemical analysis

Otoliths were prepared following the protocol described by Tabouret et al. (2010). Briefly, otoliths were embedded in resin epoxy (Araldite 2020, Escil) before being grounded to highlight the nucleus. Once prepared, otoliths were analyzed for $^{86}$Sr, $^{138}$Ba, and $^{43}$Ca as it is largely recognized that Sr and Ba otolith concentrations are primarily influenced by environmental concentrations (Walther and Thorrold, 2006; Walther and Limburg, 2012). Elemental concentrations were obtained using an inductively-coupled plasma mass spectrometer (ICP-MS DRC II, Perkin Elmer) coupled to a high-repetition-rate infrared femtosecond laser ablation (fs-LA Alfamet-Novalase, France) system and Sr isotopic ratio ($^{87}$Sr/$^{86}$Sr) was measured on the same otolith using a multi-collector inductively-coupled plasma mass spectrometer (Nu-Plasma MC-ICP-MS) coupled to the same laser. The elemental and isotopic

analyses were performed at the *Institut des Sciences Analytiques et de Physico-Chimie pour l'Environnement et les Matériaux* (IPREM), and the analytical parameters were set according to Martin et al. (2013a). During the analyses, the signal of Ca was used as an internal standard and we applied the theoretical concentration of Ca in otolith (0.383 g of Ca per g of otolith (Brown and Severin, 2009)) to standardize the measured elemental concentrations as follows: Ba/Ca and Sr/Ca. Calibration was performed using three NIST standards (610, 612, and 614) and accuracy was checked with two certified referenced materials (CRM), the NIES 22 and the FEBS 1, both obtained from the otoliths of marine fish (*Lutjanus sebae* and *Lutjanus campechanus*, respectively). Standard deviations were 4.9 ± 3.0 % and 7.4 ± 4.9 % for Sr (NIES 22 and FEBS 1 respectively), and 5.1 ± 5.4 % and 5.8 ± 4.3 % for Ba (NIES 22 and FEBS 1 respectively). The detection limits were 0.02 µg·g⁻¹ for Ba and 0.06 µg·g⁻¹ for Sr. Regarding the quantification of $^{87}$Sr/$^{86}$Sr, the width of the laser spot was set according to the concentration of Sr previously acquired (min: 50 µm; max: 180 µm) to make sure that the voltage we measured was above the 2 V threshold and thus, exploit reliable results.

#### 2.2.2.1. Ethical statement.
Every otolith analyzed in this study were collected from monitoring and carcass surveys or from amateur fishermen. Each salmon was sampled according to animal welfare laws, guidelines and policies as approved by sampling permits or the number of individual tag.
## 2.3. Data analysis

In order to be consistent with previous data treatment, we applied the method presented by Martin et al. (2013b) to select the portion of elemental transect that corresponds to juvenile freshwater residency (i.e. portion of the profile out of the maternal influence and before the sharp increase of Sr, characterizing sea migration) to investigate geographical natal origin for each individual. Individual average values of Sr/Ca, Ba/Ca, Sr/Ba, and $^{87}$Sr/$^{86}$Sr within this portion were used to assess the origin of salmon. Since environmental chemical signatures are known to vary with time (Walther and Thorrold, 2009), we included several years and seasons of sampling in our assignment database to account for temporal variation (Table 2). Among statistical methods preferably used to assign geographical origin for fish, a machine-learning algorithm such as Random Forests (RF) was found to be one of the most efficient models to perform natal prediction (Mercier et al., 2011; Jones et al., 2017; Maguffee et al., 2019; Morissette et al., 2021; Bassi et al., 2023; Stewart et al., 2023) compared to traditional methods such as Linear or Quadratic Discriminant Analyses (LDA and QDA respectively). Briefly, RF are unsupervised ensemble methods that operate by recursively splitting a dataset into an inbag fraction (or a training set) and an outbag fraction (or a test set), here based on microchemical data. The final classification relies on aggregating the splits from multiple trees (the random forest), and as more trees are included, the generalization error tends to stabilize (Breiman, 2001). Here, we used the randomForest R package (version 4.7–1.1) to investigate the natal origin of returning Atlantic salmon. For more realistic investigations, discriminant performances were evaluated only based on individuals meeting some reassignment criteria (i.e. the greatest relative probability of reattribution must be superior or equal to 0.5 and at least two times higher than the second highest probability). Every individual who did not meet this

condition was disregarded. A schematic illustration of the successive steps and parameters followed in this study is presented in Fig. 2. We ultimately inferred per locality the natal origin of adult individuals and as in any discriminant analysis, the related confusion matrix was used to quantify the uncertainty in the natal assignment. From the matrix of proximity provided by the RF, a distance matrix is established to form an ascending hierarchical classification in order to visually assess the locations that are preferably discriminated, or, that are conversely disentangled with difficulty. Finally, we used the specificity (i.e. which measures the ability of the discriminant analysis to correctly identify observations that do not belong to a particular group) and the sensitivity (i.e. which measures the ability of the discriminant analysis to correctly identify observations belonging to a particular group) provided by the model in order to account potential error in geographic assignments. All statistical analyses were conducted using the R (version 4.2.1) software.

In scientific literature, depending on the methods used to study fish origin and migration (e.g. otolith chemistry, genetics, tagging, telemetry, capture – marking – recapture (CMR)), “homing and straying” patterns are mainly estimated and interpreted in two ways. First, homing can be assessed according to the collection site, exploring all possible origin (A, B, or C) of fish caught at location A for example. The immigration rate (IR), which is also referred to as the “receiving potential” in the literature, can then be examined. Second, homing can be defined using the number of fish assigned to location A, regardless of the capture site. This method focuses on investigating the “fidelity” itself and the emigration rate (ER) of each location considered, often referred to in the literature as the “donor potential”. Depending on the chosen approach, it is crucial to precisely define the objective(s) in light of the results, as the two approaches will not provide the same information (Lamarins and Buoro, 2024). In this study, we have performed the analyses following the two methods (as described in Table 3), however, our

<figure><img src="image_5.png" /><figcaption>**Fig. 2.** Schematic overview of the statistical processes followed in this study to assign a geographical origin for returning Atlantic salmon using a Random Forests (RF) approach. **Step 1:** A total number of 513 fish, with certified origin, is used to build/train the model and estimate the relative probability of attribution to each geographical area, or each cluster. (A) The dataset is partitioned into an inbag fraction (i.e. training set, 75 % of the total dataset randomly sampled with replacement, used to build the classification trees) and an outbag fraction (i.e. test set, the remaining 25 % of the total dataset, used to test the predictions). (B) Classification trees provided by the RF analysis, set to $\beta$= 10000 trees. (C) Predictions of natal origin for each individual from the outbag fraction. (D) For cross-validation purposes, 5000 bootstrapped in/outbag fractions were computed. (E) A confusion matrix is then produced, by aggregating all bootstraps, to provide a detailed quantification of the models’ performances. Operations (A) to (E) are then repeated for each possible clustering combinations, from $k$ = 2 to the maximum number of possibilities (i.e. the number of initial geographical locations, here $k$ = 9). (F) The model selected to predict the natal origin of unknown origin salmon is the one that provides the best trade-off between the global error and the number of fishes conserved. **Step 2:** (G) The assignment database is used to predict geographic origin for the 1542 unknown origin adult salmon.</figcaption></figure>
<table><caption>**Table 3**
Glossary of the terms and their associated meanings used in this study.</caption><thead><tr><th>Approach "Homing - Immigration rate"</th><th>Approach "Fidelity - Emigration rate"</th></tr></thead><tbody><tr><td><p><strong>Philopatry:</strong> Referring to the ecological concept that describes an individual's ability to migrate back to their natal stream (i.e. a philopatric behavior).</p><p><strong>Homing rate (HR)</strong> - Based on one specific location (A), the homing rate examines, out of the absolute number of returning adults, the proportion of individuals that had hatch in this location (A).</p><p><strong>Immigration rate (IR)</strong> - Based on one specific location (A), the immigration rate considers, out of the absolute number of returning adults, the proportion of individuals that are non-native to this location (A).</p></td><td><p><strong>Fidelity rate (FR)</strong> - The fidelity rate explores, out of the absolute number of returning adults assigned to location A regardless of the capture site (A, B and C), the proportion of individuals captured in the location A.</p><p><strong>Emigration rate (ER)</strong> - Based on the absolute size of one given population (A), the emigration rate provides the proportion of individuals that would disperse into other populations (B or C).</p></td></tr></tbody></table>

sampling does not allow to robustly study the “complete” fidelity and *ER*, as it ideally requires a homogeneous sampling effort across all geographic areas likely to receive individuals (Table 2). Therefore, hereafter we present and discuss our results regarding the investigation of (i) the homing rate (*HR*) and *IR* in the Adour, Garonne, and Dordogne populations, with potential sources of donors from all over the Bay of Biscay, whereas (ii) the fidelity rate (*FR*) and *ER* were focused on exchanges between the Adour, Garonne and Dordogne. This second point is partial by definition, as we cannot estimate *ER* into unsampled systems, and is therefore likely to represent the upper range of fidelity and the lower range of *ER*.

# 3. Results

## 3.1. Model performances

According to the assignment database, the best trade-off between the number of individuals assigned to a geographical origin and the generalization error was a model resulting in seven clusters (Fig. 3A). Resultingly, the possible clusters or locations of attribution were as follows: Adour-Asturias, Allier, Bidasoa, Bretagne-Galicia, Dordogne, Garonne, and Nivelle. As displayed in Fig. 3B, the Bidasoa and the Nivelle present the lowest probabilities of correct reattribution (0.57 and 0.68 respectively) compared to the other clusters. More particularly, individuals from the Bidasoa are in part attributed to the Adour-Asturias (0.15), the Dordogne (0.15), and the Nivelle (0.13) while an important proportion of the individuals from the Nivelle is improperly attributed to the Adour-Asturias cluster (0.32). The performances of correct reattributions ranged from 0.87 to 1 for the other five geographic areas. In total, 1450 salmon were reassigned to a natal origin out of the 1542 individuals (i.e. 94.0 %), with strong differences between locations (Garonne-Dordogne: 88.9 %; Adour: 95.1 % and Nivelle: 63.6 %). Hereafter, we only present and interpret the data according to the individuals that were successfully attributed to one location. To facilitate the results' interpretation, every fish assigned to the Adour-Asturias

cluster was interpreted as originating from the Adour in the following sections. Although we could misinterpret some individuals, we are confident that this assumption is negligible considering the amount of salmon collected in the Adour (n = 1259) and the minor amount of fish that would disperse from the Asturias to the Adour as highlighted by genetic (Blanco et al., 2005; Borrell et al., 2008) and tagging studies (Leunda et al., 2013). Indeed, according to Leunda et al. (2013), approximately 10 % of marked salmon ascending the Bidasoa between 1996 and 2012 were identified as strayers from the Cantabrian and Asturian rivers. Therefore, considering that around 400 adult salmon return to the Bidasoa each year, the potential number of individuals originating from these rivers is estimated at 40 per year. Assuming that the same contingent (n = 40) of adults disperses from Cantabria and Asturias to the Adour, this would represent less than 1 % of the annual population returning to this area (see Table 1).

## 3.2. Spatial philopatry and dispersal rates throughout the southern Bay of Biscay

The proportion of individuals with their geographical origin according to their capture site is presented in Fig. 4 and Table 4. In the Garonne-Dordogne system, even if the majority of returning adults were assigned to the Dordogne or the Garonne (HR: 76.6 ± 13.6 % and 78.1 ± 13.9 % respectively), a significant number of fish was attributed to the Adour-Asturias cluster (IR: 13.1 ± 13.4 %). Our results suggest that almost all immigrants would originate from one of the most proximate locations (Adour) as salmon assigned to the Allier, the Bidasoa, and the Bretagne-Galicia clusters were much less represented (IR: 1.6 ± 2.7 %) and we have not identified any fish from the Nivelle. Considering the Dordogne and Garonne separately (Table 4), most of the immigrants from the Allier and Bidasoa mainly dispersed into the Dordogne while those from Bretagne-Galicia were equally found in both sub-basins. Nevertheless, the number of individuals attributed to the Allier, the Bidasoa, and the Bretagne-Galicia cluster tend to be underestimated while the number of fish from the Dordogne and Garonne appears to be
<figure><img src="image_6.png" /><figcaption>**Fig. 3.** (A) Agglomerative hierarchical clustering of the geographic areas under study, resulting from the assignment database. The proximity matrix arising as a byproduct of the RF was converted into a distance matrix to visually investigate the locations with the highest discriminant performances. In this panel, color coding is only used to represent the location or groups of locations considered in this study. According to the Ward D2 distance, locations with a discrimination value close to 1 are readily distinguishable by the model, whereas sites with a discrimination value close to 0 are very difficult to differentiate. (B) Confusion matrix presenting the probabilities of reattribution for each location investigated in this study, using the assignment database. The certified (or known) origin is provided in line while the origin predicted by the model is shown in columns. The specificity and the sensitivity of the model are provided in heatbars and empty boxes represent values < 0.01. The generalization error (unweighted average) resulting from this model is 0.16.</figcaption></figure>

moderately overestimated (Fig. 4A). Regarding fish collected in the Adour (Fig. 4B), we estimated a homing rate of 95.1 %, even if the possible associated errors would indicate a trend to minimize the proportion of fish originating from any potential donor population. In opposition to the Garonne-Dordogne, our results indicate that immigrants would come from all over the Bay of Biscay, regardless of the distance between geographic areas, although the proximate locations would contribute slightly more (1.5 ± 0.9 and 1.1 ± 1.0 % for the Bidasoa and the Garonne respectively) than the others (<1 %). Surprisingly, in the Nivelle (Fig. 4C, Table 4), most of the sampled fish were attributed to the Adour-Asturias cluster (71.4 %), with only 2 individuals out of 14

assigned (14.3 %) being attributed to the Nivelle. However, considering the very low sample number in this area (n = 22) in addition to the high percentage of possible misattribution to the Adour (32 %), results must be interpreted with caution. This is also the reason why the Nivelle was not included in the inter-annual analyses performed in the following sections.

Estimations for the fidelity rate and the emigration rate over the 2009–2016 period between the populations of the Adour, Dordogne, and Garonne rivers are displayed in Fig. 5. The Adour and Dordogne populations both demonstrated relatively high fidelity rates (98.7 ± 0.9 and 92.4 ± 7.6 % respectively), while the Garonne population exhibited
<figure><img src="image_7.png" /><figcaption>**Fig. 4.** Number of individuals reattributed to each possible cluster according to their capture site: (A) Gironde, (B) Adour, and (C) Nivelle. The confidence intervals have been determined using the specificity and the sensitivity provided by the model for each cluster. Note that the y-axis is in the log-scale for (B), whereas a linear scale is provided for (A) and (C).</figcaption></figure>

a much lower rate (71.0 ± 24.9 %). Although individuals from the Adour represent the largest part of emigrants identified in the Garonne-Dordogne, it only suggests an emigration rate of 0.9 ± 0.6 and 0.3 ± 0.4 % (for the Dordogne and Garonne respectively), in relation to its population size (Table 1). Inversely, while we identified a low immigration rate in the Adour from the Garonne and Dordogne, based on population sizes, it represents an emigration rate of 26.6 ± 25.7 and 4.8

± 7.0 % for the Garonne and Dordogne respectively. Finally, lower migratory flows are observed between the two Garonne-Dordogne sub-populations (ER ~ 2.5 %).

### 3.3. Temporal variation in the exchange of individuals in the Garonne-Dordogne and Adour systems

Inter-annual variation of homing and immigration rates in the Garonne-Dordogne and the Adour basins with their associated errors are displayed in Fig. 6. In the Garonne-Dordogne, an elevated immigration rate (from 25.0 % to 39.7 %) is estimated in the first years of sampling (from 2007 to 2009) and tends to stabilize around 13 % in the following years (13.4 ± 6.0 %, from 2010 to 2016). As anticipated in the previous section, the homing rate is definitely higher in the Adour compared to the Garonne-Dordogne, and that is for every year. In the Adour, with the exception of 2011 (83.2 %), the homing rate remains remarkably stable over the 10-year period of study, being always greater than 94 %. With regards to both geographic areas, even if the overall trend in homing and immigration rates is stable, the inter-annual variation is much higher in the Garonne-Dordogne system (σ=12.4) than in the Adour (σ=4.3).

To better identify the contribution of each location to the inter-annual variation of immigration rate in the Garonne-Dordogne and the Adour, the proportion of fish allocated to each cluster according to their geographical origin is presented in Fig. 6C and D. In the Garonne-Dordogne, immigrants from the Allier are observed in two independent years (2013 and 2015), individuals from the Bretagne-Galicia cluster in three independent years (2009, 2014, and 2016) while fish from the Bidasoa are found in four years of sampling. Besides, salmon from the Adour-Asturias cluster are identified in every investigated year. As previously described, the proportion of fish from the Adour-Asturias is quite massive in the first years of sampling (from 25.0 % to 34.8 % between 2007 and 2009) and stabilizes in the 2010–2016 period (9.7 ± 5.7 %). Interestingly, the relative proportion of fish captured in the Garonne-Dordogne and assigned to the Adour-Asturias cluster mirrors the global curve of homing rate observed in Fig. 6A. In the Adour, a significant proportion of immigrants from the Allier is detected in 2011, corroborating the one exception observed in Fig. 6B. Surprisingly, no fish from this region were observed in any other years of the study, making it an isolated case. In contrast, the proportion of fish captured in the Adour and assigned to the Bidasoa, the Bretagne-Galicia, and the Garonne is more equally distributed over the studied period. Fish determined as immigrants from the Dordogne and the Nivelle are observed in similar years (2011 and 2016–2018).

## 4. Discussion

The effective estimation of philopatric and dispersal rates in salmonid metapopulations remains an important methodological and analytical challenge in fisheries science (Keefer and Caudill, 2014; Chat et al., 2022; Lamarins and Buoro, 2024). In the Bay of Biscay, to the best of the author's knowledge and based on a recent literature review conducted by Lamarins and Buoro (2023), where the authors examined studies that effectively estimate dispersal rates in wild salmonid species including Atlantic salmon, except a few investigations using genetics (Consuegra et al., 2005; Valiente et al., 2010; Perrier et al., 2011b) or a combination of tagging and modeling (Kuparinen et al., 2010), no studies have been carried out to quantitatively estimate dispersal rates of Atlantic salmon over a regional scale. Yet, such information would be highly valuable in complement to genetics as it would provide a quantitative estimation of the reproductive success (i.e. effective dispersal) over time.

### 4.1. Metapopulation structure in the southern Bay of Biscay

In the Garonne-Dordogne system, our results indicate an unexpected
<caption>**Table 4**</caption>

Summary of homing and immigration rates (mean ± standard deviation, in %) for each receiving population considered. Estimations are provided for the 2009–2018 period in the Adour, the 2007–2016 period in the Dordogne and the Garonne, and the 1997–2014 period for the Nivelle.

<table><thead><tr><th rowspan="2"></th><th rowspan="2"></th><th rowspan="2">N</th><th colspan="7">Sources of immigrants</th></tr><tr><th>AD</th><th>ALL</th><th>BID</th><th>BRE-GAL</th><th>DOR</th><th>GAR</th><th>NVL</th></tr></thead><tbody><tr><td rowspan="4">Receiving populations</td><td>AD</td><td>1197</td><td>95.1 ± 4.3</td><td>0.8 ± 2.5</td><td>1.5 ± 0.9</td><td>0.5 ± 0.8</td><td>0.5 ± 0.7</td><td>1.1 ± 1.0</td><td>0.6 ± 0.9</td></tr><tr><td>GAR-DOR</td><td>232</td><td>15.1 ± 11.3</td><td>1.3 ± 3.0</td><td>2.1 ± 2.9</td><td>1.4 ± 2.5</td><td>80.1 ± 12.4</td><td></td><td>0</td></tr><tr><td>DOR</td><td>156</td><td>17.3 ± 15.1</td><td>1.6 ± 3.8</td><td>2.5 ± 3.5</td><td>1.0 ± 2.1</td><td>76.6 ± 13.6</td><td>1.0 ± 2.0</td><td>0</td></tr><tr><td>GAR</td><td>76</td><td>14.6 ± 13.3</td><td>0</td><td>0.7 ± 2.3</td><td>1.1 ± 3.5</td><td>5.5 ± 8.5</td><td>78.1 ± 13.9</td><td>0</td></tr><tr><td></td><td>NVL</td><td>14</td><td>71.4</td><td>0</td><td>7.1</td><td>0</td><td>7.1</td><td>0</td><td>14.3</td></tr></tbody></table>

Note: No standard deviation can be presented for the Nivelle, as the number of individuals was too weak for inter-annual comparisons. Abbreviations: AD, Adour; ALL, Allier; BID, Bidasoa; BRE-GAL, Bretagne-Galicia; DOR, Dordogne; GAR, Garonne; N, number of individuals assigned; NVL, Nivelle.

<figure><img src="image_8.png" /><figcaption>**Fig. 5.** Fidelity and emigration rates, based on population sizes, examined for the Adour (A), the Dordogne (B), and the Garonne (C) populations. Estimations are provided for the 2009–2016 period as it combines a substantial sampling effort in every investigated system. In each Sankey diagram, the geographical attribution predicted by the model is displayed on the left side, while the capture site is shown on the right side. The thickness of the lines represents the proportions, providing insights for visual assessment of the fidelity and emigration rates for each location.</figcaption></figure>

high proportion of immigrants (19.9 ± 12.4 %). Although some studies have estimated immigration rates ranging from 25 % to 30 % in Atlantic salmon populations (Fraser et al., 2007; Palstra and Ruzzante, 2010; Jonsson and Jonsson, 2017), and even over 50 % (Lamarins and Buoro, 2024), this value more likely represents the upper range of what is found in the literature (Lamarins and Buoro, 2024). Several hypotheses can be drawn to explain this elevated immigration rate. First, as detailed in Keefer and Caudill (2014), it is crucial to consider the “absolute number of strays”. In our case, most of the immigrants would originate from the Adour population, which is about 10-times larger than the Garonne-Dordogne population (Table 1). Hence, although the number of fish attributed to the Adour (n = 33) represents 15.1 % of the fish captured in the Garonne-Dordogne system over the 10-year period, it only suggests an emigration rate of 1.3 % from the Adour, based on population sizes. This observation corroborates previous studies demonstrating a substantial proportion of dispersal from large to small salmon populations (Brenner et al., 2012; Johnson et al., 2012; Bett et al., 2017). Additionally, the immigration distance separating the Adour and the Garonne-Dordogne estuaries (Table 1, ~230 km) is within the range of common observations for anadromous salmonids (between 100 and 300 km) (Leunda et al., 2013; Lamarins and Buoro, 2024), which supports our investigation. Second, the Garonne-Dordogne population is largely hatchery-dominated (Vandeputte et al., 2021) and relies on stocking programs that have taken place since the 1980s, after the population completely disappeared in the late 1800s (Thibault, 1994). A solid corpus of research has now demonstrated that salmon from hatcheries disperse more than their wild counterparts (Jonsson et al., 2003; Johnson et al., 2012; Jonsson and Jonsson, 2017; Sturrock et al., 2019; Nilsen et al., 2023), which could explain the modest estimated homing rate in this location.

Hence, a moderate homing rate combined with a large adjacent population that “provides” a realistic proportion of individuals would strengthen our observations. Third, several studies have suggested that rather than environmental variation, geological and ecological riverine characteristics would be the primary determinant of dispersal in Atlantic salmon (Perrier et al., 2011b; Bowlby et al., 2016; Almodóvar et al., 2023). On one hand, according to this assumption and considering the similar geochemical signatures of the Garonne sub-basin and the Adour, both originating from the Pyrénées, the exchange of individuals between these two locations could therefore be favored. On the other hand, because the use of otolith chemistry is based on river geochemical signatures, this hypothesis can also be interpreted as a source of potential mixing between location, which is moderately the case in our study (13 % of individuals originating from the Garonne may be incorrectly assigned to the Adour, see Fig. 3B).

In the Adour basin, we determined a strong homing rate (95.1 %) compared to the Garonne-Dordogne system (80.1 %). This result was expected as previous studies based on genetics have suggested that 86 % of fish caught in the upstream parts of the Adour system (i.e. recently recolonized habitats) had parents originating from downstream sites (Perrier et al., 2014), which supports the hypothesis of a population composed mainly by wild, local fish. Our results also confirm previous estimations of homing rates for wild Atlantic salmon populations (Jonsson et al., 2003; Palstra and Ruzzante, 2010), reinforcing the higher philopatric behavior in wild populations. Surprisingly, immigrants identified in the Adour emigrate from adjacent but also far-distant systems, in similar proportions, and although the immigration rate is estimated to be 4.9 %, the absolute number of fish attributed to other locations (n = 66) is considerable given the population sizes (see Table 1), particularly for the Nivelle and the Bidasoa to the south,
<figure><img src="image_9.png" /><figcaption>**Fig. 6.** Homing and immigration rates (%) determined in the Gironde (A) and the Adour (B) in addition to the relative proportion of individuals collected in the Gironde (C) and the Adour (D) according to their natal origin, over the periods of study (2007–2016 and 2009–2018, respectively). The associated error in (A) and (B) was determined using the sensitivity and specificity of the model performed in this study and the sample size for each year in both locations is displayed at the top of each bar in (C) and (D).</figcaption></figure>

and the Garonne-Dordogne and the Allier to the north. These migratory patterns mirror previous research demonstrating that large populations could also receive individuals from smaller, adjacent populations (Consuegra et al., 2005; Almodóvar et al., 2023). Combined with the donor potential we previously described from the Adour population to the Garonne-Dordogne, this attractive aspect would strengthen the buffering role of a large population for adjacent populations that are smaller, and highly stocked, both in terms of source and sink dynamics.

In the Nivelle river, our results showed an unprecedented immigration rate, with 71.4 % of the fish collected being attributed to the Adour and 7.1 % to both the Bidasoa and the Dordogne. Nevertheless, these observations must be interpreted with caution due to the limited sample size of the Nivelle. Hence, any misattribution could drastically change the aforementioned results, especially considering the substantial possible error between the Nivelle and the Adour (32 % of individuals originating from the Nivelle may be incorrectly assigned to the Adour, see Fig. 3B). Yet, even if we consider the more optimistic scenario from our model, the Nivelle population would still largely rely on fish emigrating from the Adour system. A previous study from Valiente et al. (2010) has established that 12–23 % of returning salmon in the Nivelle are emigrating from the Bidasoa. In Perrier et al. (2014), the authors also suggest a possible gene flow from the Adour to the Nivelle, as they found a lower census population size (i.e. total number of returning adults) than the effective population size (i.e. total number of spawners), although the hypothesis of a substantial contribution of precocious parr was also advanced as an explanation. In this study, our results would indicate an emigration rate of 1.4 % of the Adour population into the

Nivelle, but the reproductive success of the emigrants is unknown. This is where we see the value of combining several tools such as genetics and otolith chemistry, as our observations support the hypotheses of Perrier et al. (2014), which ultimately suggest an effective dispersal from the Adour to the Nivelle. Additionally, although barely detected, the concurrent low homing rate and high immigration rate observed in the Nivelle could suggest a demographic Allee effect (Stephens and Sutherland, 1999; Stephens et al., 1999; Rougier et al., 2012), where lower population density leads to decreased rates of population growth.

Consequently, in the three systems that compose the southern Bay of Biscay, the connectivity between populations appears relatively important, and stable, both in terms of intensity and direction. The observed biocomplexity of the metapopulation might help in the sustainability of each population, particularly for the Nivelle and the Garonne-Dordogne, potentially *via* demographic and genetic rescue effects. Indeed, combined with genetic studies, our observations suggest a key role of emigration from the Adour to maintain a sustainable, viable population in the Nivelle. Additionally, the Spanish populations appear to contribute only minimally to the observed exchanges, likely due to their low abundance levels, in addition to the distance separating them from the main rivers in the study area. Similarly, the populations from the northern Bay of Biscay (i.e. Bretagne and Allier) provide few (to the Adour) or almost no (to the Garonne-Dordogne and Nivelle) individuals, which would confirm the lower connectivity between populations that are more distant (Lamarins and Buoro, 2024).
## 4.2. Inter-annual variability of philopatry and dispersal

If the study of salmonid dispersal is recognized to be overlooked in the scientific literature (Kállo, 2023; Lamarins and Buoro, 2024), finding information about the temporal variations in dispersal is considerably more challenging. In our study, although we could notice a greater inter-annual variability in the Garonne-Dordogne system than in the Adour basin over a 10 year period, the overall philopatric and dispersal rates remain stable over the years.

### 4.2.1. Garonne-Dordogne

Over the 2009–2016 period, both the homing (from 60 % to 96.6 %) and the fidelity approach (from 65.7 % to 100 %) provided relatively similar results to estimate the philopatry. What most characterizes the temporal evolution of homing and fidelity rates in this location is the great variability from one year to another. One potential explanation can be advanced to explain our results. As previously hypothesized, the Garonne sub-basin could be more attractive to salmon from the Adour compared to the Dordogne as both the Adour and the Garonne originate from the Pyrénées, hence displaying similarities in physical and chemical water properties, which in turn, can favor dispersal (Perrier et al., 2011b; Keefer and Caudill, 2014; Almodóvar et al., 2023). Therefore, in sampling years when the proportion of individuals caught in the Garonne (relative to the total number of individuals caught in the Garonne-Dordogne) is greater, we could observe an associated higher proportion of immigrants from the Adour. Here, a decreasing proportion of fish is attributed to the Garonne over the years meanwhile an inverse relationship is observed for the Dordogne, which is likely due to the sampling effort (see Table 2). Overall, regarding the Garonne-Dordogne system, although the population is rather small and largely depends on hatchery releases, we do not observe any pattern of decreasing philopatric behavior. Additionally, we suggest that salmon from the Adour play a key role in the Garonne-Dordogne population, even though we cannot determine the existence of potential gene flow, as this information is not available for the individuals included in this study. In fact, Perrier et al. (2013) have demonstrated that the Garonne-Dordogne population is genetically distinct from the Adour population, which suggests that effective dispersal is unlikely, or extremely scarce. Nevertheless, they also indicate a higher genetic admixture in their recent cohorts (in the beginning of the 2000s) compared to their historical cohorts (between the 1960s and the 1980s), which could reveal an increasing gene flow in the last decades.

### 4.2.2. Adour

In the Adour system, our results indicate a remarkable stability of philopatry and dispersal over the years. Although it has been suggested that homing behavior could be negatively affected because of climate change and hatchery releases (Bett et al., 2017), our research corroborates previous studies which highlighted that, for populations composed of wild individuals, or a large majority of them, homing rates are stable over time (Jonsson et al., 2003; Palstra et al., 2007). From an eco-evolutive perspective, such stability may indicate a portfolio effect, which favors the persistence of the whole population *via* the diversity of inter-individual life histories. According to this theory, the potential role of connectivity is difficult to appreciate, as it would require a better knowledge of the reproductive success of immigrants. Nevertheless, as we identified an important portion of salmon from the Adour into the Garonne-Dordogne, the temporal stability of this large population may help buffering demographic and/or genetic variability of adjacent populations, therefore reinforcing the biocomplexity of the system as a whole, i.e. the metapopulation structure, over time. Contrarily to the Garonne-Dordogne, immigrants identified in the Adour come from every location we sampled across the Bay of Biscay, which contrasts with previous scientific literature detailing a preferential neighboring dispersal (Hamann and Kennedy, 2012; Chat et al., 2022). Particularly, individuals from the Allier were only observed in one year (2011), and

in relatively large percentages (8.0 %). In 2011, the rivers in the Allier basin witnessed historically low water levels in its rivers because of a severe drought, rendering conditions extremely unfavorable for adult migration to breeding grounds (Keefer and Caudill, 2014; Bett et al., 2017). Thus, episodic dispersal into what appears to be the most attractive basin, in response to these unfavorable environmental conditions, would be a plausible hypothesis to explain this result. Albeit the immigration rate is fairly low in the Adour system (4.9 %), the relative importance for regular donor populations (namely the Garonne-Dordogne to the north and the Nivelle and the Bidasoa to the south) is considerable. Indeed, most of these donor populations are small in size, and although we do not have sufficient data to robustly infer the emigration rates from the Nivelle and Bidasoa to the Adour, we can reasonably assume that these rates are substantial, as identified for the Garonne-Dordogne (13.8 %). Such estimations would be consistent with what is observed in the literature (Lamarins and Buoro, 2024). Therefore, our results would suggest a stable, considerable asymmetric dispersal, from the smallest (Garonne-Dordogne, Nivelle, and Bidasoa) to the largest (Adour) populations. To the best of the author's knowledge, only a couple of studies have evaluated the temporal stability of effective dispersal (i.e. gene flow) in salmon populations (Fraser et al., 2007; Palstra et al., 2007; Palstra and Ruzzante, 2010). Here, we demonstrate an inverse relationship between the relative importance of emigration rate (i.e. 1.3 % from the Adour into the Garonne-Dordogne and 13.8 % from the Garonne-Dordogne into the Adour) and the immigration rate (i.e. 18 % from the Adour into the Garonne-Dordogne and 1.6 % from the Garonne-Dordogne into the Adour).

Hence, our study reinforces the importance of (i) considering the absolute number and the relative proportion of migrants, especially when investigating the connectivity between small and large populations, (ii) considering temporal variation in dispersal (for example in the Adour, we only detected salmon from the Allier in one sampling year but individuals from the Bidasoa and the Garonne-Dordogne were much more frequent) and (iii) assess dispersal mechanisms on a regional scale (e.g. “freshwater ecoregion”, Gillis et al., 2023) as dispersal may be primarily influenced by environmental stochasticity (Palstra et al., 2007). Finally, the initial hypothesis that dispersal may be responsible for the positive dynamics observed in the Dordogne and the Adour systems between 2010 and 2020 (i.e. increasing number of returning mature adults) has not been verified considering the temporal stability of homing and immigration rates highlighted in this study. Other explanations, such as improved (1) efficiency of rearing programs and/or (2) access to spawning grounds, or (3) environmental conditions more resilient to climate change, should be examined in the future.

## 4.3. Limitations and perspectives

The authors acknowledge that several factors can limit our interpretations. First of all, a key consideration in our study is the uncertainty associated with natal origin assignments, as classification performance inherently depends on model specificity and sensitivity. Geographic assignments always carry some degree of misclassification, particularly in regions where environmental chemical signatures overlap or where sample size constraints limit model resolution. In this study, we accounted for these sources of uncertainty by including multiple years and seasons in our assignment database and by using Random Forests, a machine-learning approach that has demonstrated superior performance in geographic classification tasks compared to traditional discriminant analyses. Additionally, we applied stringent reassignment criteria, requiring the highest probability of reattribution to be at least 0.5 and at least twice as high as the second-highest probability, ensuring that individuals with ambiguous assignments were excluded from the analysis. While some uncertainty remains, particularly in areas with close environmental similarities, we are confident in the robustness of our results given the strong discrimination power observed between key natal groups and the consistency of our findings with independent
genetic and tagging studies. Future improvements could focus on expanding the reference dataset to enhance geographic resolution, employing ensemble modelling approaches to further quantify uncertainty, and integrating complementary tracers (e.g., stable isotopes or genetics) to refine assignments, as recently demonstrated by Källo et al. (2023). Another limitation lies in the sample design itself. It is nearly impossible to properly sample exhaustively every possible river or tributary where fish may access or stem from (Källo et al., 2022b). In addition, Atlantic salmon have a relatively wide period of upstream migration (roughly from February to September) making it very difficult to collect a representative sample from all potential spawners. Here, because fishing is mostly prohibited in the latest period of upstream migration, where almost all 1SW are migrating (André et al., 2021), we mainly captured MSW salmon. Yet, previous studies have shown that considering the age structure is essential when investigating patterns of dispersal (Palstra and Ruzzante, 2010), as it could explain short-term genetic variation between cohorts. Therefore, it would be relevant to account for the age structure, especially considering the diversity of systems within the Bay of Biscay with salmon populations mainly composed of MSW (e.g. Allier, Garonne-Dordogne), or 1SW (e.g. Bretagne), while others are more proportionately distributed (e.g. Adour).

Besides these methodological limits, we identify three main perspectives in our study. First, several authors have pointed out the importance of spatial-scale definitions in dispersal investigations. Here, we assessed philopatric and dispersal rates at a regional scale. However, in their study, Perrier et al. (2014) have demonstrated a clear genetical structure within the Adour basin. Even if this result suggest a limited gene flow between sub-basins, which is in accordance with Lamarins and Buoro (2024), it would be relevant to study the movement behaviors at both large (i.e. regional) and small (i.e. sub-basins) spatial scales. In this study, our sample size across sub-basins is not suitable for such investigation (see Figure S1). Second, because we used otolith chemistry to investigate dispersal, we cannot infer on potential demographic or genetic effects as we do not know if the observed dispersal is effective, with subsequent gene flow (Masson et al., 2018). Again, coupling otolith chemistry with other tools would be desirable to access such information. For example, several studies have shown that upmigrating salmon may express exploratory or temporary dispersal (Keefer and Caudill, 2014; Peterson et al., 2016; Bett et al., 2017). Finally, in parallel to this last point, it would be essential to depict the proportion of wild from hatchery-fish in dispersal, as numerous studies have detailed a higher dispersal rate for hatchery-origin salmon compared to their wild counterparts (Jonsson et al., 2003; Brenner et al., 2012; Johnson et al., 2012; Keefer and Caudill, 2014; Bett et al., 2017). Therefore, the investigation of the role of hatcheries in Atlantic salmon populations from the southern Bay of Biscay would be the next step.

## 4.4. Management implications

In their review, Keefer and Caudill (2014) suggested that “the emphasis should perhaps be reversed, as many of the most pressing management and ecological questions related to straying are from the perspective of the receiving population”. Here, our study highlighted a clear metapopulation structure for Atlantic salmon in the Bay of Biscay, and more particularly the main populations. Our estimations would indicate a key role of the Adour population to the Garonne-Dordogne population, both in terms of donors and receivers. Hence, it emphasizes the need to jointly conserve and protect large and small populations, and consider management implications with the exchange of individuals between locations, especially considering the temporal stability we observed in dispersal. Although one genetic study has demonstrated a low admixture rate between the Garonne-Dordogne and the Adour populations (Perrier et al., 2013), suggesting an ineffective dispersal, indirect effects could still be observed via competition behaviors for habitats and feeding or resources of sexual mating for example (Essington et al., 2000).

## Declaration of Competing Interest

Fontaine A.: Formal analysis, Methodology, Validation, Investigation, Writing – original draft, Writing – review & editing, Visualization; Vignon M.: Methodology, Validation, Writing – review & editing, Visualization; Tabouret H.: Formal analysis, Investigation, Writing – review & editing; Holub A.: Resources, Investigation; Barranco G.: Resources; Bosc S.: Resources, Writing – review & editing; Caux I.: Resources; Nachón D.J.: Resources, Writing – review & editing; Elso J.: Resources, Writing – review & editing; Caballero P.: Resources, Writing – review & editing; Pécheyran C.: Resources; Bareille G.: Conceptualization, Validation, Writing – review & editing, Supervision, Project administration, Funding acquisition.

## Declaration of Competing Interest

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

## Acknowledgements

The authors are very grateful to the regional water agency (Agence de l'Éau Adour-Garonne), the regional administration (Région Nouvelle-Aquitaine), the Office français de la biodiversité (OFB), the North Atlantic Salmon Conservation Organization (NASCO) and the Centre National de la Recherche Scientifique (CNRS) for funding this study. We would like to thank all suppliers of salmon otoliths: MIGRADOUR, Fédérations de pêche 29, 64 and 65, Barthouil and Jean-Marc Casteigt companies, Patrick Balesta, INRAe, Bretagne Grands Migrateurs (BGM), Migrateurs Garonne Dordogne Carenté Seudre (MIGADO), the Conservatoire National du Saumon Sauvage (CNSS) and Loire Grands Migrateurs (LOGRAMI) and recreational anglers. Finally, we thank Jordan Lacoste, Fanny Claverie and Gaëlle Barbotin for their precious help in the preparation and the analyses of otoliths over the last years.

## Appendix A. Supporting information

Supplementary data associated with this article can be found in the online version at doi:10.1016/j.fishres.2025.107323.

## Data availability

Data will be made available on request.

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