# Weak fine-scale genetic structure and considerable life-history diversity in a near-pristine subarctic Atlantic salmon (Salmo salar) river

María I. Cádiz, Marion Sinclair-Waters, Jaakko Erkinaro, Panu Orell, Ana S. Lindeza, Morgane Frapin, Amaïa Lamarins, Victoria L. Pritchard, Craig R. Primmer

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María I. Cádiz, Marion Sinclair-Waters, Jaakko Erkinaro, Panu Orell, Ana S. Lindeza, et al.. Weak fine-scale genetic structure and considerable life-history diversity in a near-pristine subarctic Atlantic salmon (Salmo salar) river. Conservation Genetics, In press, 10.1007/s10592-025-01717-y. hal-05228481

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# Weak fine-scale genetic structure and considerable life-history diversity in a near-pristine subarctic Atlantic salmon (*Salmo salar*) river

María I. Cádiz<fnref n="1" />☐· Marion Sinclair-Waters<fnref n="1" />,<fnref n="2" />☐· Jaakko Erkinaro<fnref n="3" />☐· Panu Orell<fnref n="3" />☐· Ana S. Lindeza<fnref n="1" />☐· Morgane Frapin<fnref n="1" />☐· Amaïa Lamarins<fnref n="1" />☐· Victoria L. Pritchard<fnref n="1" />,<fnref n="4" />☐· Craig R. Primmer<fnref n="1" />☐

Received: 4 February 2025 / Accepted: 1 July 2025

© The Author(s) 2025

## Abstract

The Näätämö River, in the far north of Europe, hosts a large Atlantic salmon (*Salmo salar*) stock with high socio-economic value. The catchment has near-pristine environmental conditions, although there are recent signs of stock declines. It nevertheless offers a good opportunity to monitor the status of a near pristine wild salmon system. This study aimed to characterize the fine-scale genetic structure of Atlantic salmon in the Näätämö River and evaluated the possibility to assign individuals to their population of origin. We genotyped juveniles sampled from eight locations using a 60 K single nucleotide polymorphism (SNP) panel to characterize within-river genetic diversity and structure. We also tested the performance of a previously designed 180 SNP panel to assign individuals to their population of origin. The genetic structure of the Näätämö River salmon population appears weak (FST ranging from 0.001–0.035), possibly due to natural straying. The performance of the predesigned panel in assigning individuals to their population of origin was moderate to high (82–85%), but customization could further enhance its accuracy. Additionally, we described the life-history diversity of salmon within the river using a long-term scale archive of c. 22,000 individuals collected over 48 years, identifying 59 unique life-history strategies. We suggest that to protect the self-sustaining salmon population in the Näätämö River, it would be important to manage the salmon fishery and conserve the population by incorporating up-to-date knowledge of stock status, considering their genetic structure, substantial gene flow and diverse life history.

**Keywords** Genetic diversity and structure · Life-history strategies · Conservation · *Salmo salar*

## Introduction

Intraspecific variation, including genetic and phenotypic diversity within and among populations, is an essential component of biodiversity, and its dynamics over time and space can influence the fate of a population (Mimura et al. 2017; Des Roches et al. 2018, 2021; Shaw et al. 2025).

Genetic diversity provides the raw material for evolutionary processes (Laikre et al. 2020; Hoban et al. 2020), enhancing organisms ability to adapt to changing environmental conditions and thereby contributing to the long-term survival of species (Agashe 2009; Laikre et al. 2020; Hvilsom et al. 2022). Phenotypic variation, particularly in life-history strategies (e.g., age at maturity, migration time and time spent in sea vs. freshwater) enhances resilience through the portfolio effect (Schindler et al. 2010), enabling populations to resist or quickly recover from environmental perturbations by spreading risk temporally and spatially (Pelletier et al. 2020). Human-driven pressures (e.g., harvest, landscape change, climate change, pollution and introduction of predator/prey/host/competitor) are reshaping variation within species by driving rapid evolutionary changes and extirpation of wild populations across freshwater, marine, and terrestrial ecosystems worldwide (Palkovacs et al. 2012). Recent studies have shown that since the Industrial Revolution, the genetic diversity within wild populations

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

<footnote>María I. Cádiz
maria.cadiz-escobar@helsinki.fi</footnote>

<footnote n="1">¹ Organismal and Evolutionary Biology Research Programme, Faculty of Biology & Environmental Sciences, University of Helsinki, Helsinki, Finland</footnote>

<footnote n="2">² UMR AGAP Institut, Univ Montpellier, CIRAD, INRAE, Institut Agro, Montpellier, France</footnote>

<footnote n="3">³ Natural Resources Institute Finland (LUKE), Oulu, Finland</footnote>

<footnote n="4">⁴ Institute for Biodiversity & Freshwater Conservation, UHI Inverness, Inverness, UK</footnote>
has declined by nearly 6% (Leigh et al. 2019). Despite its
ecological and evolutionary importance and growing threat
due to human activities, intraspecific diversity remains
under-assessed by global surveys (Laikre et al. 2020). There
is, therefore, an increasing need to better understand and
quantify intraspecific diversity, which can facilitate effec-
tive conservation and management strategies for wild popu-
lations (Des Roches et al. 2021), especially those subjected
to harvesting.

Genomic tools in conservation allows quick assess-
ment of intraspecific genetic variation (Leigh et al. 2019).
Assessing key genetic variation parameters (e.g., heterozyg-
gosity, number of alleles, population divergence and gene
flow, among others) (Mimura et al. 2017) provides essential
information to monitor population status and contribute to
management plans. Understanding the extent of the genetic
structure has implications for conservation and management
(Primmer et al. 2006). For example, the genetic structure of
Atlantic salmon populations has been found to vary consid-
erably, going from systems with no structure at all, such as
Miramichi River (Wellband et al. 2019), to complex struc-
tured systems within a single river, as observed in the Teno
River (Vähä et al. 2017), each requiring distinct manage-
ment considerations. Genetic stock identification (GSI) is
a tool that contributes to the conservation and management
of fisheries by providing valuable insights into the stock
composition and origins of populations (Ensing et al. 2013;
Hess et al. 2011; Vähä et al. 2017; Svenning et al. 2019).
In addition, the application of GSI has extended to help as
a forensic tool to identify illegal harvesting (Nielsen et al.
2012), determine the source of escaped salmon (Glover et
al. 2008), and reveal instances of fishing competition fraud
(Primmer et al. 2000).

Atlantic Salmon (*Salmo salar*) is an iteroparous fish with
ecological, cultural, and economic importance across its
range. This species exhibits diverse life-history strategies
(Erkinaro et al. 2019), which vary in the time spent in fresh-
water and the duration of marine migration associated with
returning to spawning. Anadromous Atlantic salmon typi-
cally spend 1–3 years in freshwater (1–3 RA; river or smolt
age) in the south of their range (Klemetsen et al. 2003) and
3–5 years in the north (Niemelä et al. 2006). This is fol-
lowed by a marine feeding migration where the majority of
growth occurs, followed by return migration to fresh water,
often homing back to their birth river, mostly one to three
years later to spawn (1-3SW; age at maturity, sea age, or
number of sea winters) (Klemetsen et al. 2003). Moreover,
further life-history strategy diversity is added via iteropar-
ity, i.e., individuals that survive to spawn in several years
(Niemelä et al. 2006; Persson et al. 2023).

Globally, Atlantic salmon populations have faced decline
or already been extirpated over the past two centuries

(Parrish et al. 1998; Dadswell et al. 2022; ICES 2025). This
decline is mainly due to human-related activities, includ-
ing overfishing, hydropower dams, diseases, pollution, and
climate change (Parrish et al. 1998). Additionally, studies
have shown shifts in phenotypic traits as age at maturity and
growth patterns across their European distribution (Otero et
al. 2012; Erkinaro et al. 2019; Harvey et al. 2021; Vollset
et al. 2022). Along the northern edge of the species distri-
bution, the salmon stock in the sub-Artic Näätämö River
in Fennoscandian North remains one of the relatively large
and viable wild populations (Niemelä et al. 2004). While
earlier studies reported no consistent decline in their overall
abundance from 1975 to 2003 (Niemelä et al. 2004), more
recent data show a significant decreasing trend in salmon
stocks of the Näätämö River (Kytökorpi et al. 2024), con-
sistent with trends observed in other northern rivers (VRL
2024). Specifically, from 2019 to 2023, the average number
of salmon observed has halved compared to previous years
(Kytökorpi et al. 2024). Several earlier population genetic
studies have included the Näätämö River for comparison to
other wild populations (Elo 1993; Elo et al. 1994; Skaala et
al. 1998; Asplund et al. 2004; Vähä et al. 2008; Ozerov et
al. 2023) and aquaculture origin (Skaala et al. 2004, 2005;
Andreassen et al. 2008; Besnier et al. 2011; Glover et al.
2013; Pritchard et al. 2015). However, no study has been
conducted on within-river genetic variation and the genetic
structure of the salmon in this river.

To address this knowledge gap, we explore fine-scale
population genetic structure across the Näätämö River using
contemporary genomic data. Juveniles from eight locations
were genotyped using a 60 K single nucleotide polymor-
phism (SNP) panel to assess the genetic diversity and struc-
ture. To place our findings in a broader regional context,
we compared them with the genetic structure of the nearby
Teno River, a well-studied system known for highly struc-
tured population complex and life-history variation (Vähä
et al. 2007, 2008, 2017; Aykanat et al. 2015; Erkinaro et
al. 2019). To evaluate the utility of existing tools for con-
servation management, we tested the assignment accuracy
of a previously designed 180 SNP panel. Additionally, we
describe the life-history variation (i.e., quantified the com-
bination of number of years spent in the river as a juvenile,
number of years spent feeding at sea, and number of spawn-
ing attempts) using a long-term scale archive of c. 22,000
scales collected over 48 years. This study aims to inform
evidence-based conservation and bilateral management
in a relatively pristine but declining northern salmon sys-
tem. Specifically, we tested whether (i) fine-scale popula-
tion genetic structure exists despite minimal anthropogenic
impact; (ii) a predesigned 180-SNP panel can accurately
assign individuals to origin locations within the river and
(iii) the Näätämö River supports high life-history diversity
comparable with previous studies in the rivers on the north-
ern coast of Norway (Erkinaro et al. 2019; Persson et al.
2023).

**Methods**

Study area

The Näätämöjoki River (Neidenelva in Norwegian; Njaud-
dâm in Skolt Sámi) is a transboundary catchment between
northern Finland and Norway (69°N, 29°E; Fig. 1) covers a
total area of 2962 km² and is characterized by little human
influence and good water quality (Niemelä et al. 2015;
Kytökorpi et al. 2024). Stocking juvenile salmon or eggs
reared outside the catchment area is currently prohibited by
law, and the last stocking of salmon fry took place in the mid-
1980s (Niemelä et al. 2015). Salmon have been detected up
to 220 km upstream of the river mouth, and adult salmon
are regularly caught along ca. 110 km of the mainstem and

one main tributary, Silisjoki (Vähä et al. 2008; Niemelä et
al. 2015).

Samples and genotypes

We sampled 283 juveniles of 0–4 years of age by electro-
fishing from eight different areas across the Näätämö River
for population genetic analysis (Table 1, Fig. 1). DNA from
all individuals was purified from fin-clip samples using a
salt extraction protocol (Aljanabi and Martinez 1997). Each
sample was genotyped using a 60 K Atlantic salmon SNP
array developed by the Centre for Integrative Genetics
(CIGENE), Ås, Norway. To further explore the population
structure of the region, we also included 1519 individuals
genotyped using the same SNP array sampled from 42 loca-
tions in the Teno River (68–70° N, 25–27° E; total area of
16 386 km²) (Johansson et al. (2024); Online Resource 1,
Table S1), which is separated from the Näätämö River by
ca. 250 km (Vähä et al. 2008). The SNP coordinates were
based on genome version GCA_905237065.2, Ssal_v3.1,

<figure><img src="image_3.png" /><figcaption>Fig. 1 a) Map of northern Europe and b) sampling locations along the Näätämö River. Minimum (triangle) and maximum (circle) X, Y coordinates for the electrofishing per sampling area</figcaption></figure>
<table><caption>**Table 1** Sampling location, number of juvenile Atlantic salmon collected and genotyped, minimum and maximum X, Y coordinates of the sampling area, year of sampling and genetic diversity ($H_E$, expected heterozygosity; $H_O$, observed heterozygosity), country and abbreviated location names for each sampling location in the Näätämö River</caption><thead><tr><th>Name</th><th>Country</th><th>Abbre. name</th><th>#samples</th><th>X (min., max.)</th><th>Y (min., max.)</th><th>Sampling year</th><th>H<sub>E</sub></th><th>H<sub>O</sub></th></tr></thead><tbody><tr><td>Gisttahbealjohka</td><td>Finland</td><td>-</td><td>17</td><td>552659, 552786</td><td>7738130, 7740647</td><td>2019</td><td>0.314</td><td>0.347</td></tr><tr><td>Silisjoki</td><td>Finland</td><td>-</td><td>49</td><td>549798, 552375</td><td>7724753, 7740517</td><td>2019</td><td>0.341</td><td>0.345</td></tr><tr><td>Harrijoki</td><td>Finland</td><td>-</td><td>42</td><td>567807, 571353</td><td>7731008, 7735622</td><td>2019</td><td>0.335</td><td>0.335</td></tr><tr><td>Kuosnijoki</td><td>Finland</td><td>-</td><td>23</td><td>569219, 570793</td><td>7727066, 7727917</td><td>2020</td><td>0.337</td><td>0.346</td></tr><tr><td>Mainstem 1</td><td>Norway</td><td>MS1</td><td>30</td><td>587414, 592295</td><td>7733105, 7734478</td><td>2020</td><td>0.345</td><td>0.353</td></tr><tr><td>Mainstem 2</td><td>Norway</td><td>MS2</td><td>22</td><td>581689, 585223</td><td>7734288, 7735284</td><td>2019</td><td>0.34</td><td>0.35</td></tr><tr><td>Mainstem 3</td><td>Finland</td><td>MS3</td><td>41</td><td>558402, 575713</td><td>7722909, 7734959</td><td>2019</td><td>0.343</td><td>0.35</td></tr><tr><td>Mainstem 4</td><td>Finland</td><td>MS4</td><td>54</td><td>544111, 555066</td><td>7713225, 7721004</td><td>2019</td><td>0.344</td><td>0.350</td></tr></tbody></table>

by aligning SNP probes to the genome using BWA v.0.7.17 (Li and Durbin 2009) using an in-house script.

## Data filtering

Data were filtered using vcftools v0.1.16 (Danecek et al. 2011) and PLINK v1.09 (Purcell et al. 2007) according to the following criteria: (1) removing loci with more than 10% missing data (--geno 0.1), (2) removing individuals with more than 10% missing data (--mind 0.1), (3) removing loci deviating from Hardy-Weinberg equilibrium (--hwe 0.01), (4) removing loci with minor allele frequencies below 0.01 (--maf 0.01). For the population structure analysis, we (5) filtered SNPs in strong linkage disequilibrium (LD; --indep 50 5 1.5) and (6) removed potential close relatives with a PI_HAT (score of the proportion of sites in identity-by-descent IBD)>0.5, i.e., we retained only one individual per group. Steps 3, 4, 5, and 6 were applied separately to each river (Näätämö and Teno) (details per system in Online Resource 2, Table S2). We based the HWE calculation only on individuals from the Teno mainstem (n=146 samples, Group A=Piltamo, Sirma, Garnjarga, and Kortsham, Online Resource 1, Table S1), which do not exhibit significant population structuring ($F_{ST}$=0.01–0.02, Online Resource 4, Table S3).

## Basic statistics, genetic diversity, population structure and migration pattern analysis

Observed and expected heterozygosities ($H_O$ and $H_E$, respectively) were calculated for all sample locations using PLINK software. We calculated pairwise $F_{ST}$ (Weir and Cockerham 1984) among sampling locations using the R package stamppFst (Pembleton et al. 2013): (i) within Näätämö, and (ii) among Näätämö and Teno rivers. To examine the genetic structure between different locations of the Näätämö and Teno Rivers, we first performed a principal component analysis (PCA) implemented in PLINK. Second, to infer the number of populations on different combinations of datasets, we used the maximum likelihood analysis of individual

ancestries by Admixture software (Alexander et al. 2009): (i) for the eight Näätämö locations, the number of ancestral populations (K) was set from 1 to 10; (ii) for locations from the mainstem (i.e., MS1, MS2, MS3, MS4) and Kuosnijoki tributary, K was set from 1 to 10; and (iii) for Näätämö and Teno datasets, K was set from 1 to 51. The optimal K value was selected based on the lowest cross-validation error and visual inspection of co-ancestry values.

We estimated the direction and magnitude of gene flow among the sampling sites between the Näätämö and Teno systems using the divMigrate() algorithm (Sundqvist et al. 2016). This method was selected due to its computational efficiency and independence on prior assumptions, although it may be sensitive to lower sample size (Sundqvist et al. 2016). We calculated the number of migrants (Nm) per generation moving between pairs of sampling areas, derived from measures of genetic diversity between populations (Sundqvist et al. 2016). Drawing on these earlier analyses of genetic structure (Online Resource 3, Fig. S2; Online Resource 4, Table S3) which depict low level of differentiation between locations of the mainstem Teno River and the Näätämö River, we focused on comparing gene flow between all Näätämö locations and the following Teno location groups: i) Upper Group A; ii) Lower Group B and iii) Middle Group C (Online Resource 1, Table S1).

## Assignment analysis

We evaluated the assignment accuracy of a 180 SNP panel designed for genome stock identification (GSI) of Teno River populations (Johansson et al. 2024) for population assignment within the Näätämö River. These analyses were conducted using the R package Rubias v0.3.3 (Moran and Anderson 2018). Our evaluation involved two distinct approaches: (i) the leave-one-out approach, in which we assigned individuals from a reference dataset back to their collection site, and (ii) 100% simulations, where mixtures with 100% of the individuals from one collection site were simulated. Moreover, incorporating the findings previously obtained by Admixture analysis to improve accuracy, we
redefined the reporting groups: the sites from the mainstem (i.e., MS1, MS2, MS3, MS4) and Kuosnijoki tributary, Gisttahbealjohka—Silisjoki complex and eight differentiated individuals from the Gisttahbealjohka and Silisjoki locations (details on Online Resource 2, Table S2).

## Life-history strategies within the Näätämö River salmon population

A fish-scale collection comprising c. 22,000 Atlantic salmon was collected over 48 years (1975–2022) from the mainstem sections (i.e., MS1, MS2 and MS3) of the Näätämö River (Niemelä et al. 2004). Recreational tourist anglers and local fishers collected scale samples using diverse catching methods (i.e., rod 46.3%, gill nets 22.7% and cast nets 31%) during the fishing season (June–August). The life-history strategy for each sample was determined by examining the scale growth rings, which provided information about the age at which the fish left freshwater and migrated to the sea (river, RA), the number of winters they spent at sea before returning to spawn for the first time (sea age at maturity or sea winters, SW), and the possible earlier spawning events (repeat spawners). Trained experts evaluated scale growth rings following the international guidelines for Atlantic salmon scale reading (ICES 2011). Individuals without sex information, kelts i.e., adults that have recently spawned, often in poor condition (Mobley et al. 2021), and escaped salmon of aquaculture origin were excluded.

The assessment of diversity in life-history strategies was accomplished by identifying unique combinations of the number of years salmon spend in freshwater and in the ocean and the number of reproductive events. To determine the relationship between the number of documented strategies and the number of individuals sampled over 48 years, we implemented an individual-based rarefaction curve with a 95% unconditional confidence interval. Life-history strategies were ranked by frequency and the proportion of each strategy was plotted against its rank.

## Results

### Genetic diversity, population structure analysis

After quality control, we retained 17227 SNPs and 252 individuals for the Näätämö River (Online Resource 1, Table S1 and Online Resource 2, Table S2). Observed and expected heterozygosity per sampling location ranged from 0.314–0.353 (Table 1). Little or no clustering was observed among the four mainstem sampling locations of the system (MS1, MS2, MS3 and MS4) and Kuosnijoki tributary, which formed a single cluster based on principal component

analysis (PCA; Fig. 2a). The other tributaries formed two distinct clusters: i) Gisttahbealjohka – Silisjoki complex (largest tributary of the river) and ii) Harrijoki (Fig. 2a). In agreement with the PCA results, Admixture analysis revealed that the expected number of ancestral populations (K value) was three (Fig. 2c; Online Resource 3, Fig. S1). Eight individuals with over 99% ancestry in common with each other were found in the Gisttahbealjohka (4 individuals) and Silisjoki (4 individuals) from an unknown population (Details in Online Resource 2, Table S2). For the four locations belonging to the mainstem and Kuosnijoki tributary, K=1 was found, further supporting the lack of a structure between these locations. Genetic differentiation ($F_{ST}$, Weir and Cockerham mean) for the Näätämö River was low among the four mainstem sites and Kuosnijoki tributary ($F_{ST}$ ranging from 0.001–0.009), while the three remaining tributaries had slightly higher levels of differentiation ($F_{ST}$ ranging from 0.016–0.035) (Fig. 2b).

We incorporated SNP data from the neighbouring Teno River to delve deeper into the population structure of the region. Following quality control, we kept we keep 12912 SNPs in common and 1651 samples (1399 Teno; 252 Näätämö) (details per system are provided in Online Resource 2, Table S2). Observed and expected heterozygosity per sampling location ranged from 0.257–0.349 (details per system are provided in Online Resource 1, Table S1). PCA of the combined samples revealed that the Näätämö salmon clustered within Teno River samples (Details in Online Resource 3, Fig. S2). Admixture predicted that the best number of clusters was 23 (Online Resource 3, Fig. S3). $F_{ST}$ values between the Näätämö and Teno River samples ranged from 0.013 to 0.132. The higher $F_{ST}$ values were observed between the Näätämö River and Teno tributaries ($F_{ST}$=0.045–0.132), while the sections with lower differentiation were mainly located between Näätämö River and Teno mainstem locations ($F_{ST}$=0.013–0.034) (see Online Resource 4, Table S3). Gene flow was observed among samples collected from all analyzed locations in both the Näätämö and Teno Rivers (Online Resource 5, Table S4). The gene flow was higher within the Näätämö (Nm=0.37–1) and Teno River (Nm=0.82–1) than between the rivers (Nm=0.30–0.59), excluding Gisttahbealjohka tributary. No significant asymmetric gene flow ($P$>0.05) was detected between any pair of sampling locations. Notably, Gisttahbealjohka showed lower Nm values, which could be attributed to the smaller sample size.

### Assignment analysis

From the panel of Johansson et al. (In press) designed for Teno River, we found 179 out of 180 SNPs. The mean assignment accuracy to sampling locations for the SNP panel were 44% and 47% for the leave-one-out and 100%
<figure><img src="image_4.png" /><figcaption>**Fig. 2 a)** Principal Component Analysis (PCA) of 252 juvenile Atlantic salmon samples from the Näätämö River. Each sampling location had a unique color. The X- and Y-axis titles show the percentage variation explained by PC1 and PC2, respectively; **b)** Pairwise FST values between the eight sampling locations from the Näätämö River; **c)**</figcaption></figure>

Admixture results for three genetic clusters (K=3) for the Näätämö dataset. Each column represents an individual ordered by sampling location, indicated on the x-axis. The sampling location abbreviations are listed in Table 1

simulation approaches, respectively (Table 2). In contrast, when defining the three reporting groups based on the Admixture results, notably higher accuracy was observed, with mean accuracies of 85% and 82% for the leave-one-out and 100% simulation approaches, respectively.

## Life-history strategies

Based on almost 18000 samples, we identified 59 distinct life-history strategies, including various combinations of river ages from 1 to 7 years, sea ages prior to first spawning from 1 to 4 years, and numerous types of repeat spawners (Fig. 3a). Of these, 23 strategies correspond to salmon caught during their first return to spawn, representing most individuals (95.6%). The remaining 4.4% were repeat spawners, but they displayed 36 different life-history strategies, accounting for 61% of the total life-history variation

(54.6% in males; 45.4% in females). Among those individuals categorized as repeat spawners, 98.4% were alternate spawners (i.e., salmon returning to spawn two or more years after their first spawning event), spanning 28 distinct strategies. In contrast, only 1.6% were consecutive spawners (i.e., salmon returning to spawn the year following their first spawning event), spanning eight strategies.

One-quarter of all the analyzed life-history strategies (16 out of 59) were observed only once, whereas the five most common strategies comprised 77.3% of all individuals. The predominant strategies in the system were: 4–1 (river age – sea age; 30.9% of individuals), 4–2 (15.0%), 3–1 (12.8%), 5–1 (10.4%) and 4–3 (8.4%). The sea ages composition of the river corresponded to 54.9% of individuals classified as 1SW, 25.4% as 2SW, 14.9% as 3SW, 0.3% as 4SW and 4.4% as repeat spawners. The most common river ages in the Näätämö system were 4 (57%) and 3 (23.4%) individuals.
<caption>**Table 2** Assignment accuracy for eight populations from the Näätämö River using 179 SNP panel described in Johansson et al. (In press)</caption>

"NAPOP" indicates samples clustered by admixture from an unknown population

<table><thead><tr><th rowspan="2">Reporting group</th><th colspan="2">Sampling location</th><th colspan="2">Sampling location + genetic inf</th></tr><tr><th>Leave-one-out</th><th>100% simulations</th><th>Leave-one-out</th><th>100% simulations</th></tr></thead><tbody><tr><td>Gisttahbealjohka</td><td>25%</td><td>37%</td><td>79%</td><td>71%</td></tr><tr><td>Silisjoki</td><td>67%</td><td>83%</td><td></td><td></td></tr><tr><td>Harrijoki</td><td>68%</td><td>71%</td><td>70%</td><td>86%</td></tr><tr><td>Kuosnijoki</td><td>14%</td><td>7%</td><td>91%</td><td>97%</td></tr><tr><td>MS1</td><td>37%</td><td>53%</td><td></td><td></td></tr><tr><td>MS2</td><td>9%</td><td>6%</td><td></td><td></td></tr><tr><td>MS3</td><td>42%</td><td>56%</td><td></td><td></td></tr><tr><td>MS4</td><td>47%</td><td>66%</td><td></td><td></td></tr><tr><td>NAPOP</td><td>-</td><td>-</td><td>75%</td><td>74%</td></tr><tr><td></td><td>44%</td><td>47%</td><td>85%</td><td>82%</td></tr></tbody></table>

Return timing also varied by life-history strategy and sex. Between 1975 and 2022, most fish caught early in the fishing season (weeks 22–24) were 2SW and 3SW females, whereas 1SW males predominantly returned later (weeks 25–35; Online Resource 6, Fig. S4). The oldest salmon recorded in the system was a 13 years old female (106 cm, 12 kg) caught in July 1975. Its life history was 5-2S3S1:

5 years in the river, 2 years at sea, a first spawning migration, followed by 3 full years at sea, and a second spawning migration before being caught during its third spawning migration in the river.

The rarefaction curve did not reach an asymptote (Fig. 3b), suggesting the possibility of discovering additional rare strategies with an increased sample size. Our analyses

<figure><img src="image_5.png" /><figcaption>**Fig. 3** Life-history strategies composition along the Näätämö River. a) The proportion of each life-history strategy observed in the Näätämö River ranked by their frequency in catches (triangles: first time spawning strategies; circles: repeat spawning strategies). b) Rarefaction</figcaption></figure>

curve of the number of life-history strategies for the 48-year sampling period with the shaded area representing 95% unconditional confidence interval
of rarefaction and extrapolation curves did not reveal any
changes in the richness of life-history strategies throughout
the analyzed period (Online Resource 6, Fig. S5).

Discussion

Low genetic differentiation within the Näätämö river

Along the mainstem locations (M1, M2, M3 and M4) and the Kuosnijoki tributary, weak or no genetic structure, minimal levels of differentiation and high gene flow were observed, consistent with natural straying within the system. Whereas the other tributaries formed two distinct clusters, i.e., Gisttahbealjohka—Silisjoki complex and Harrijoki tributary, characterized by low levels of differentiation. Similar patterns of low genetic divergence within rivers have been found in other systems that hold wild Atlantic salmon populations (Primmer et al. 2006; Dionne et al. 2009; Wellband et al. 2019; Östergren et al. 2021). Some previous studies have suggested the implications of straying from hatchery or aquaculture-reared salmon. However, in the case of the Näätämö River, the introduction of salmon from outside of the catchment area was prohibited by law in the late 1980s (Niemelä et al. 2001). Prior to this prohibition, only small numbers of mostly locally derived hatchery-reared fry had been stocked in several locations (Niemelä et al. 2001, 2015). While it seems unlikely that this would have had a major effect on the current genetic structuring within the system, its effect cannot be completely ruled out. We were unable to address this question directly, as no hatchery-origin salmon samples were available for comparison. Therefore, further investigation is warranted. Another potential factor affecting genetic structuring is the reproduction of escaped farmed Atlantic salmon over the system. However, an extensive monitoring program of 25 years from 1989 to 2013 showed a low incidence of escaped farmed salmon (mean incidence 0–0.04) (Diserud et al. 2019), which indicates a low risk of genetic change (<4% incidence) (Taranger et al. 2015).

Between-river migration or straying can also contribute
to the weak genetic structure within the Näätämö River. The
nearest river with comparable genomic data was the well-
studied Teno River (Niemelä et al. 2004; Erkinoar et al. 2019).
Salmon originating from the Teno River have been caught
over 600 km along the North-Norwegian coast (Svenning
et al. 2019), depicting their wide dispersal potential. Upon
comparing both systems, we found low levels of genetic
differentiation mainly between the mainstem sections, with
high bidirectional gene flow. Notably, we observed that the
levels of genetic differentiation between the Näätämö River
and Teno mainstem were lower (F<sub>ST</sub>=0.013–0.019) than

some within-Teno comparison, where F<sub>ST</sub> values ranged
from 0.013 to 0.119 (see Online Resource 4, Table S3).
Earlier studies using microsatellite loci have shown that the
Näätämö system has clustered with other rivers of the Bar-
ents Sea area, including the Teno River (Zueva et al. 2018,
2020). In contrast, Vähä et al. (2008) reported Näätämö as a
distinct genetic group using 29 microsatellites, and although
the mean gene flow between Näätämö and Teno was low,
it remained comparable to within-Teno estimates. The dis-
crepancy with our results potentially arises from the higher-
resolution markers and larger sample size from our study,
allowing us to detect subtle patterns of gene flow and previ-
ously unresolved structure. Taken together, we suggest that
the low genetic differentiation and weak structure observed
within Näätämö River may have been influenced by natu-
ral straying within the system and potentially other adjacent
systems to the Näätämö River, such as the Teno River.

Performance of a 180 SNP panel for genetic stock
identification

Genetic stock identification (GSI) has been widely used
for Atlantic salmon across a broad geographical coverage
(Gilbey et al. 2018; O'Sullivan et al. 2022; Ozerov et al.
2017; Svenning et al. 2019), whereas the use of the GSI in
within-river analyses is limited (Primmer et al. 2016; Vähä
et al. 2007; 2017). By exclusively considering the sampling
locations, we observed that sites exhibiting slightly higher
genetic divergence, specifically tributary locations (Silisjoki
and Harrijoki), showed better accuracy (67–68%) than those
locations from the mainstem system (9–47%). One factor
that can influence GSI success is the level of genetic dif-
ferentiation between stocks (Vähä et al. 2017). For example,
the Snake River basin's steelhead trout (O. mykiss) exhib-
ited reduced GSI accuracy due to low genetic differentiation
among populations resulting from translocation (Powell and
Campbell 2020). Incorporating the genetic structure infor-
mation, i.e., grouping as three independent reporting groups
those sites from i) the mainstem and Kuosnijoki tributary, ii)
Gisttahbealjohka and Silisjoki complex and iii) eight sam-
ples from unknown origin the from the Gisttahbealjohka and
Silisjoki rivers, significantly enhanced assignment success.
Particularly for the group composed by the mainstem and
Kuosnijoki tributary (91%) and Gisttahbealjohka—Silis-
joki complex (76%). Vähä et al. (2017) also noted that less
distinct populations required larger sample sizes than more
diverged populations (optimal: n>100, F<sub>ST</sub>>0.07; n>200,
less diverged). The mean sample size per location in our
system was 32, with a range of 8–49, which probably influ-
enced the mean assignment accuracy to sampling locations
results that improved by incorporating the genetic informa-
tion. Therefore, the SNP panel initially developed for the
Teno River (Johansson et al. 2024) performed reasonably well for GSI in the Näätämö River. Nevertheless, selecting more informative SNPs specifically for this system, increasing the sample size (e.g., Gisttahbealjohka) or including additional populations (Kallojoki and Vuosttamušjohka tributaries) may substantially enhance the GSI accuracy.

## Exploring life-history strategies along the system

Previous studies have shown that Atlantic salmon display a broad range of life-history strategies. For example, research on the Pechora River in Russia revealed 24 distinct strategies (Studenov et al. 2008), whereas studies on 179 Norwegian rivers identified 141 distinct strategies, ranging from 1 to 51 per river (Persson et al. 2023). Exceptional cases include the Teno River, in which 121 strategies have been documented for a single system (Erkinaro et al. 2019). The Näätämö salmon population displayed a wide spectrum of life-history strategies, represented by 59 unique strategies, the second highest reported for a single river. The five most frequent strategies identified in our system (i.e., river age—sea age; 4–1, 4–2, 3–1, 5–1, and 4–3) align with the same categories observed in the Teno system, encompassing 79% of the individuals sampled in Erkinaro et al. (2019), and 77% for the Näätämö system.

Our results also revealed slight changes to the sea-age composition of Näätämö River salmon previously reported for 1975 to 2003 (Niemelä et al. 2004) with reductions in 1SW and 3SW proportions (from 62 to 55% and 16% to 15%, respectively) and increases in 2SW and repeat spawners proportions (from 19 to 25% and 3.0% to 4.4%, respectively). A consistent large-scale phenomenon has been observed in some northern rivers, characterized by a transition in salmon populations from 1 to 2SW fish, which has been partly attributed to the ban of drift nets on the Norwegian coast in late 1989 and the increased sea surface temperature (Otero et al. 2012; Erkinaro et al. 2019; Harvey et al. 2021; Vollset et al. 2022). Further analysis is necessary to determine if the Näätämö River exhibits this same shift pattern. The general predominance of first-time spawning salmon compared to repeat spawners remained consistent, albeit with a slight decrease of approximately 1% in first-time spawners.

Repeat spawners play an important role in population dynamics (Persson et al. 2023). Research has shown that even small proportions of repeat spawners can act as a buffer during periods of low abundance of first-time spawners (Bordeleau et al. 2020). On average during these periods, repeat spawning females contributed approximately 18.2—35.3% of all the eggs produced by the populations (Bordeleau et al. 2020). Furthermore, repeat spawners may provide a valuable source of genetic diversity by simultaneously

increasing the number of cohorts spawning (Erkinaro et al. 2019; Mobley et al. 2024). By spawning multiple times, they can buffer against unpredictable events that threaten population recruitment (Reid and Chaput 2012; Harvey et al. 2021). In less predictable environments, repeat spawning individuals may introduce favorable traits into the gene pool, as those that successfully spawn more than once potentially possess beneficial traits for survival and reproduction, which they pass on to their progeny, thereby contributing to the sustainability and stability of the population over time (Aykanat et al. 2019; Birnie-Gauvin et al. 2019). In the Näätämö River, there was a high level of variation in the specific life-history strategy among the repeat spawners, comprising 61% (36 of 59) of the total life-history strategies observed in the river, despite constituting only 4% of the total frequency of individuals. Our findings are consistent with previous research on the Teno River, which revealed that 76% of the variation in life-history strategies (91 of 120 observed strategies) is attributable to repeat spawners (Erkinaro et al. 2019). Similarly, in 170 Norwegian rivers, 75% of the variation was attributable to repeat spawners (106 from 141 observed strategies) despite their low total frequency (4%) (Persson et al. 2023). Alternate spawners were the predominant strategy in our system, which is consistent with previous studies that have suggested that this strategy is most common in more northern latitudes, while consecutive spawning is more prevalent in southern latitudes (Niemelä et al. 2006; Chaput and Benoît 2012; Reid and Chaput 2012; Erkinaro et al. 2019; Kaland et al. 2023).

## Management implications and future work

Given the recent decline in the abundance of the Näätämö salmon stocks and the rapid pace of environmental change, it would be important to revise the current management framework. This framework, based on a bilateral fishing agreement between Finland and Norway dating back to the 1970s–1980s, is complex and slow, hampering swift conservation efforts (Kytökorpi et al. 2024). To address these challenges, an adaptive management approach, informed by up-to-date knowledge of stock status, should be implemented to enable quick and flexible reactions to changes in population status and structure of the Näätämö salmon, and the challenges posed by the rapid environmental changes, such as the ongoing dramatic increase in invasive pink salmon (*Oncorhynchus gorbuscha*) populations (Staveley et al. 2025).

As part of this adaptive framework, the effective conservation and management of the Näätämö salmon stocks requires systematically monitored genetic structure, genetic diversity, and life-history variation as part of cross-border
collaboration. Our findings indicate weak genetic differen-
tiation across the mainstem locations and Kuosnijoki tribu-
tary, supporting the idea of managing these areas as a single
unit. However, we have observed some level of genetic
differentiation in the Silisjoki-Gisttahbealjohka complex
and Harrijoki tributaries that should not be overlooked and
warrants special attention. Specifically, one of the main pur-
poses of the monitoring should be to enhance the resolu-
tion of the baseline genetic structure. For instance, it would
be advisable to include additional samples from the Gist-
tahbealjohka tributary, given the low sample size retained
after quality control (i.e., n=8, of which half belong to an
unknown population). Additionally, expanding the baseline
by incorporating previously unsampled areas, such as the
Kallojoki and Vuosttamušjohka tributaries, where consid-
erable juvenile production still occurs despite the reduced
discharge caused by water diversion to the Gandvik hydro-
power facility (Niemelä et al. 2015; Orell et al. 2024). This
would provide a more comprehensive understanding of
population structure across the Näätämö River. Moreover,
substantial gene flow within the Näätämö River may pro-
mote demographic and genetic rescue effects, maintaining
overall population persistence and resilience in the face
of environmental change (Garant et al. 2007; Whiteley et
al. 2015). Thus, maintaining connectivity across habitats
should therefore be a conservation priority.

Despite the weak genetic structure observed within the
Näätämö River, we cannot exclude the possibility of there
being life-history differentiation among the genetic clusters
(i.e., mainstem locations-Kuosnijoki tributary, the Gisttah-
bealjohka-Silisjoki tributaries and the Harrijoki tributary). In
this study, we were not able to address this question because
phenotypic data were only available for individuals from
the mainstem section, so further investigation is warranted.
Nonetheless, we observed an overall high diversity of life-
history strategies, exhibiting the second-highest number
reported for a single river. Intraspecific variation in traits such
as age at maturity and migration timing plays a crucial role
in enhancing resilience to environmental change, stabilizing
populations and supporting sustainable harvest (Hilborn et al.
2003; Östergren and Nilsson 2012; Gharrett et al. 2013; Miet-
tinen et al. 2021; Carvalho et al. 2023). Preserving this diver-
sity is therefore essential for the long-term viability of the
Näätämö salmon stocks. Including targeted measures, such
as regulating the early-season fishery, could help safeguard
specific salmon life histories (Erkinaro et al. 2019). The fish-
ing season on the Näätämö River begins in early June, mak-
ing it one of the earliest in Northern Norway (Kytökorpi et al.
2024). Early-season fisheries primarily target 2–4SW salmon
and repeat spawners (Online Resource 6, Fig. S4), which pri-
marily consist of large, high-fecundity females (Niemelä et
al. 2006) that play a crucial role in population productivity.

Finally, salmon life-history variation—particularly sea age—
has been associated with large-effect loci (Barson et al. 2015;
Sinclair-Waters et al. 2020), offering promising avenues
for population monitoring and conservation (Waples et al.
2022). We recommend assessing the adaptive potential of the
Näätämö stocks, and more broadly of Atlantic salmon popula-
tions, in response to human induced selection, by monitoring
changes of allele frequencies of large-effect loci associated
with key life-history traits over time.

**Supplementary Information** The online version contains supplementary material available at https://doi.org/10.1007/s10592-0 25-01717-y.

**Acknowledgements** We would like to thank sample providers (Natural Resources Institute Finland staff, traditional and recreational fishers), scale age readers and the genetics laboratory team (Helena Johansson, likki Donner and Annukka Ruokolainen) for their assistance. We acknowledge the DNA Sequencing and Genomics Laboratory at the University of Helsinki (Institute of Biotechnology; supported by HiLIFE and Biocenter Finland funding) and CIGENE (Centre of Integrative Genetics, Norwegian University of Life Sciences, Norway) for sequencing. CSC (IT Center for Sciences, Finland) for access to computational resources.

**Author contributions** Conceptualization: VLP, CRP, MIC, MSW; Sample collection: JE, PO; Data curation and analysis: MIC, MSW, JE, PO; Visualization: MIC, ASL, AL, MF; Funding acquisition: CRP, VLP; Writing-original draft: MIC; Writing-review & editing: All authors.

**Funding** Open Access funding provided by University of Helsinki (including Helsinki University Central Hospital). Funded by the University of Helsinki, Natural Resources Institute Finland (Luke) (until 2015: Finnish Game and Fisheries Research, Institute), Ministry of Agriculture and Forestry (Finland), Neiden Fiskefelleskap and Norwegian Environment Agency and the European Union (ERC, FishLEGs, 101054307). Views and opinions expressed are however those of the author(s) only and do not necessarily reflect those of the European Union or the European Research Council Executive Agency. Neither the European Union nor the granting authority can be held responsible for them.

**Data availability** Data for this study are available at Zenodo: https://d oi.org/10.5281/zenodo.16762360.

Declarations

**Ethical approval** This study used Atlantic salmon tissue samples collected from an fisheries monitoring programs. All sample collection complied with applicable national legislation of Finland and Norway.

**Consent for publication** All authors consent to submitting this article to *Conservation Genetics*.

**Conflicts of interest/Competing interests** The authors declare no com-
peting interests.

**Open Access** This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format,
as long as you give appropriate credit to the original author(s) and the
source, provide a link to the Creative Commons licence, and indicate
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