Issue
Knowl. Manag. Aquat. Ecosyst.
Number 421, 2020
Topical Issue on Fish Ecology
Article Number 18
Number of page(s) 16
DOI https://doi.org/10.1051/kmae/2020004
Published online 17 April 2020

© P. Berrebi et al., Published by EDP Sciences 2020

Licence Creative Commons
This is an Open Access article distributed under the terms of the Creative Commons Attribution License CC-BY-ND (https://creativecommons.org/licenses/by-nd/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. If you remix, transform, or build upon the material, you may not distribute the modified material.

1 Introduction

Brown trout, Salmo trutta Linnaeus, 1758, is one of the most man-handled fish species in the world (Laikre et al., 1999). Its natural range covers Europe, Western Asia and North Africa (Behnke, 1986; Elliott, 1994). According to the control region (CR) marker of mtDNA, five main geographic lineages have been described: Atlantic (AT), Mediterranean (ME), Adriatic (AD), marmoratus (MA) and Danubian (DA) (Bernatchez et al., 1992; Bernatchez, 2001). Several secondary lineages, placed at the basis of the main ones, have also been described, e.g. DU for Duero, TI for Tigris, the Balkan cluster, the Dades lineage in Morocco, NA for North Africa (respectively: Suarez et al., 2001; Bardakci et al., 2006; Snoj et al., 2009, 2011; Tougard et al., 2018).

From 1748 onwards, brown trout, generally of the AT lineage has been domesticated in the Eastern Atlantic slopes for stocking in Westphalia, Germany (Leitritz and Lewis, 1980). Then, in the middle of the nineteenth century, it was massively produced in hatcheries in Alsace, France and Baden Wurttemberg, Germany for aquaculture and domestic forms were introduced into the wild as eggs in boxes, fry or sub-adults (Bohling et al., 2016). European hatchery strains are composed of two lineages: (i) a more or less common one composed of admixed Atlantic original populations sampled in several north European countries and used currently as a commercial product for stocking (ComATL = common global Atlantic-based strain, according to Bohling et al., 2016) and (ii) a very heterogeneous set of strains originating from local rivers and devoted to local stocking (LocATL = strains derived from local Atlantic watersheds, LocMED = strains derived from local Mediterranean watersheds sensu Bohling et al., 2016). ComATL strains are mainly tagged by four D-loop haplotypes: haplotypes 1, 2, 3 and 4 (Cortey and García-Marín, 2000) that are widespread in Western Europe, from southern France to Norway, and northern Russia (Appendix 1). These haplotypes were then named At1a to d (Duftner et al., 2003) or AT-s1 to 4 (Cortey and García-Marín, 2002; Cortey et al., 2004) or Atcs1 to 4 (Cortey et al., 2009). This last denomination is used in this study.

Brown trout is well known for its adaptive abilities to various ecological conditions, provided that there is plenty of running, clear, oxygenated, fresh water not exceeding 20 °C. These conditions are commonly met in mountain streams all over the world, which has resulted in brown trout transfers being successful outside the species natural range, including the southern hemisphere, where salmonids are not native (Bailey, 1966, MacCrimmon and Marsall, 1968; Elliott, 1989). Therefore, when introduced worldwide, brown trout frequently constitute naturalized (self-sustaining) populations (MacCrimmon and Marshall, 1968; MacCrimmon et al., 1970). It has been reported that for self-sustainability, non-native brown trout require hydrologically stable streams with small snowmelt floods, low summer water temperatures and spawning ground availability (MacCrimmon and Marsall, 1968; Kawai et al., 2013). The capacity of non-native brown trout to settle self-sustainable populations has been largely exploited to provide sport fishing for this species outside its native range.

On the basis of ecological impacts (Townsend, 1996; Jonsson and Jonsson, 2011; Budy et al., 2013), brown trout are now considered one of the most pervasive and successful invaders often negatively affecting fishes and ecosystems, not only through predation and competition (Fausch and White, 1981; Ortiz-Sandoval et al., 2017) but also by acting as a vector of exotic parasites (for details on brown trout invasive abilities, see Budy and Gaeta, 2018).

At the world scale, ancient introductions were probably made with wild eggs or fry. The first brown trout translocation outside the species natural range goes back to the mid-19th century, when it was introduced into Tasmania, Australia and New Zealand (Jones and Closs, 2018). At about the turn of the century it was also transferred to South Africa (Weyl et al., 2018), USA and Patagonia (Budy and Gaeta 2018; Casalinuovo et al., 2018). After that, brown trout spread across all the continents − even into Antarctica to the Kerguelen Islands (Labonne et al., 2013). For details about brown trout translocations and distribution outside Europe, see Elliott, 1989 and Section 5 in Lobón-Cerviá and Sanz (2018).

Several studies have been made on introduced self-sustaining brown trout, including their reproduction, population density, growth, invasion dynamics etc., mainly in the Kerguelen Islands (Davaine and Beall, 1992; Labonne et al., 2013; Jarry et al., 2018). However, little has been done on genetic characterization of the translocated self-sustaining populations, which would reveal their origin, genetic structure, population genetic dynamics, and adaptive capacity as inferred from basic population parameters such as genetic diversity, genetic drift, gene flow etc.

In Japan, records on brown trout import are very rare and few data on the first brown trout introduction exist. Introductions were performed exclusively in the Islands of Honshu and Hokkaido. The population of Lake Chuzenji (Tochigi Prefecture, Honshu) is presumed to be the oldest one, supposedly originating in the early 1900's (Maruyama et al., 1987) or even earlier, around the end of the 19th century (in 1892 according to Elliott, 1989). According to Kawanabe and Mizuno (1989) it arrived via trout hatcheries in the USA, due to the erroneous presence of brown trout eggs among those of rainbow trout (Oncorhynchus mykiss Walbaum, 1792) or brook trout (Salvelinus fontinalis Mitchill, 1814).

The Chuzenji brown trout hatchery (now the Nikko Station, National Research Institute of Fisheries Science) was established at the lake to propagate local material, which was afterwards mostly used for stocking in Japan. Later on, stocking with Chuzenji hatchery brown trout was discontinued and the fish were cultivated for experimental purposes without extra input from outside (personal communication from Nikko station scientists).

In Honshu, brown trout inhabit the upper part of the Azusa river in the remote Kamikouchi high mountain valley (Nagano Prefecture). This is one of the main trout habitats in this prefecture (e.g. Sakata, 1974; Kitano et al., 2013). The prime source was eyed eggs from an American fish hatchery imported in the early 1930's and stocked by the Nagano prefectural government (Sakata, 1974).There is evidence that some Azusa trout were obtained directly from Europe in 1973 from Mepp Co. (a no longer operating French company; Maruyama et al., 1987). Brown trout have also appeared in the lower part of the river (close to the city of Matsumoto), where they apparently spread via downstream migration or due to anthropogenic transfers (Yagyu et al., 2016). Fishermen in Hokkaido introduced brown trout in the 1980s, which then appeared in numerous rivers in the southwestern part of the island.

Although there is plenty of indirect evidence pointing to self-sustainability of brown trout in Japan, some examples clearly show natural reproduction. Namely, the brown trout population from the Chitose river, a tributary to the Ishikari river in Hokkaido, has naturally propagated at least since 1984 when this species was first observed there (Urawa, 1989). In addition, some brown trout escaped into the Odori river (a tributary to the river Miya, central Honshu) from a small old hatchery in September 2004. Subsequently in 2008 and 2009, both juvenile and adult brown trout, including mature individuals, were observed and captured there (Ishizaki et al., 2012).

In Japan, introduction of non-native salmonids (rainbow trout, brown trout and brook trout) seriously affected native species such as masu salmon (Oncorhynchus masou Brevoort, 1856), white-spotted charr (Salvelinus leucomaenis Pallas, 1814), Dolly Varden (Salvelinus malma Walbaum, 1792) and Sakhalin taimen (Parahucho perryi Brevoort, 1856) (Kitano et al., 2013). Brown trout represent a major threat to the white-spotted charr, mainly through competition and genetic mixing. For example, earlier studies in Hokkaido populations have demonstrated replacement of white-spotted charr by brown trout (Takami et al., 2002; Morita et al., 2004; Hasegawa and Maekawa, 2009). Furthermore, brown trout that were released by local anglers into the Kane river (Fuji river system, Yamanashi Prefecture, Honshu) in 2004 began to hybridize with native white-spotted charr. This has become a problem of deep concern for conservation of the latter (Tanizawa et al., 2016). Moreover, after an invasion that occurred above a collapsed dam in the Monbetsu stream in Hokkaido, brown trout replaced white-spotted charr through competition and possibly hybridization (Hasegawa, 2017). Brown trout have also reduced the number of species of fish fauna in other streams in Hokkaido (Shimoda 2012; Hasegawa et al., 2017).

In response to its invasiveness in Hokkaido, brown trout stocking has been banned since 2003, while in Honshu, the Japanese government is now hurrying to prepare a law for brown trout management, mostly in terms of prohibiting stocking in natural streams.

The purpose of this article is twofold. Firstly in the introduction we have reviewed existing information on transfers of brown trout to Japan and the distribution of self-sustaining populations there. Then we have surveyed the evolution of the introduced populations by (i) testing present knowledge on the history of brown trout introduction in Japan through its genetic structure in the whole country, (ii) deducing the possible origins of self-sustaining populations in Japan by genetic comparison with Atlantic brown trout from Europe hatcheries and (iii) estimating the adaptive capacities of self-sustaining populations through genetic diversity.

The Japanese brown trout samples, which were collected between 2008 and 2017, were analyzed using mitochondrial DNA control region sequences and twelve microsatellite markers.

2 Materials and methods

2.1 Sampling

Specimens were caught between 2008 and 2017, mainly using electrofishing, but also nets and angling. Fin clips (adipose or ray fin) were taken and stored in 96% ethanol. Each sample was given a map number from 1 to 19 (Fig. 1). A description of the nineteen sampling localities, number of individuals analyzed (N) and the year of sampling are given in Table 1.

Sampling sites in the Mamachi and Monbetsu streams are described in detail, in Kitano et al. (2009) and Kawai et al. (2013) respectively (Tab. 1). Although both streams are tributaries to the Chitose river, they are physically isolated and are assumed to represent independent populations. Brown trout appeared in and invaded these streams from the 1980s onwards (Kawai et al., 2013). As the Jigoku stream is the inlet of Lake Chuzenji, there is no migration barrier between them, so the Jigoku sample should represent the lake population. Brown trout have been reared in the Chuzenji hatchery for experimental purposes since 2000 without any input or output activities. In the Azusa river system specimens were collected in the upper part in the Kamikouchi valley (samples 7 to 11) and also downstream (samples 12 to 14) close to Matsumoto city (Fig. 1). They formed nine samples of 1 to 24 trout, due to capture difficulties in a low density situation. Between the upper (samples 7 to 11) and lower (samples 12 to 14) areas, there are at least four apparently insurmountable dams (Fig. 1), potentially preventing gene flow between the two. Statistical controls are necessary before the upper and lower samples are each considered as a true population. Brown trout from three French hatcheries (Lées Athas, Cauterets and Isère; Tab. 1) were included as reference material.

thumbnail Fig. 1

Geographic position of the 19 Japanese samples analyzed.

Table 1

Description of the nineteen sampling stations and three reference hatcheries, number of individuals analyzed (N) and the date of sampling. E = flowing to eastern Japan: Pacific Ocean; W = to the west: Sea of Japan. All the sampling stations are in Honshu Island unless indicated as “Hokk.” = Hokkaido Island.

2.2 DNA extraction, sequencing and alignment

DNA was isolated from fin tissue following the improved Chelex extraction procedure as described in Estoup et al. (1996).

The complete mitochondrial control region (mtDNA CR) was PCR-amplified using primers LRBT-25 and LRBT-1195 (Uiblein et al., 2001), following the conditions in Marić et al. (2012). Both-directions sequencing was carried out on an ABI Prism 3130xl DNA sequencer (Applied Biosystems) according to the manufacturer's recommendations using the same primers. Sequences were aligned using the Clustal X computer program (Thompson et al., 1997) implemented in MEGA version 6 (Tamura et al., 2013). Haplotype nomenclature follows Cortey et al. (2009) for new haplotypes. The relationships among haplotypes detected in this study and reference haplotypes of the Atlantic lineage from Europe (Cortey et al., 2009; Appendix 2) are presented as a 95% statistical parsimony network constructed using TCS 1.21 (Clement et al., 2000).

2.3 Microsatellite genotyping and statistical analyses

Twelve microsatellite loci (Mst543, MST85, Omm1105, Omy21Dias, Oneμ9, Sfo1, Ssa197, SsoSL311, SsoSL438, SsoSL417, Str591 and StrBS131; Tab. 2) were amplified in three multiplex PCR (Tab. 2). PCR amplifications were carried out using the Qiagen multiplex PCR kit (Qiagen) and a set of forward primers with various concentrations (Tab. 2 and references herein). Amplifications were conducted in a GeneAmp PCR System 2700 thermal cycler (Applied Biosystems), according to the supplier's instructions (Qiagen multiplex PCR kit) with an initial denaturation step at 95 °C for 15 min; followed by 35 cycles of denaturation at 94 °C (30 s), annealing (59 °C, 90 s) and extension (72 °C, 59 s); with a final extension step at 59 °C for 30 min. Amplicons were separated on a capillary ABIPRISM 3130xl sequencer using GeneScan500Rox dye as the standard size. Fragment lengths were assessed using a GeneMapper v4.1 software system (Life TechnologiesTM).

In order to draw the overall genetic structure of the analyzed samples in a unique diagram, Factorial Correspondence Analysis (FCA; Benzécri, 1973), implemented in GENETIX 4.04 (Belkhir et al., 2004), was first performed.

Assignment tests, using the Bayesian STRUCTURE 2.1 program (Pritchard et al., 2000), were used to subdivide the whole sample-set into K subgroups characterized by the best genetic equilibrium in terms of best panmixia and lower linkage. The admixture ancestry model and correlated allele frequencies options were chosen. A burn-in of 100,000 iterations followed by 200,000 additional Markov Chain Monte Carlo iterations was run for all tests except from step four of the hierarchical analysis (see below) for which they were 50,000/100,000 respectively. For each K value, five runs were repeated in order to check the stability of the assignment. Estimation of the best K value (number of biological subgroups in the entire sample) was approached using the “Delta K method” of Evanno et al. (2005) through STRUCTURE HARVESTER (Earl and von Holdt, 2012).

Two methods were used to explore the assignment deeply:

The entire sampling was first analyzed as a whole from K = 1 to K = 10. While using the method of Evanno et al. (2005), all runs that made sense were represented in a tree describing all subdivisions. This precaution was taken because, as explained by Gilbert et al. (2012), “selecting the optimal K can be quite a subjective procedure and is best inferred when the biology and history of the organism are taken into account”. Therefore, levels of K higher than that suggested by the Delta K method were also explored.

The hierarchical STRUCTURE assignment analysis was then performed (Pritchard et al., 2000; Vähä et al., 2007; Marić et al., 2017). After the first analysis and determination of the most probable K using the method of Evanno et al. (2005), which set the first hierarchical level, each subgroup of the first level was analyzed separately, allowing for more precise clustering of individuals without eliminating admixed individuals. This hierarchical method was applied until no further substructure was observed.

Genetic diversity is also an important parameter for introduced populations, because it can provide information on the evolutionary potential of the strain used and on the number of individuals introduced. In addition, it can be the mark of small population size or of bottlenecks. This was estimated through three parameters using GENETIX software, i.e. observed heterozygosity (Ho), the non-biased estimated heterozygosity (Hnb), which is the calculated proportion of heterozygote genotypes according to the allele frequencies pondered by the sample size (Nei, 1978) and the mean number of alleles per locus (A).

Panmixia was determined using the Fis parameter. For this, the estimator F of Weir and Cockerham (1984) was calculated, together with its significance after 5,000 permutations of alleles within each sample, using GENETIX software. This test was applied sample by sample, then to the upper and lower groups of Azusa river sampling sites in order to collapse them into only two populations.

Differentiation between samples (populations) was evaluated using the Fst parameter (Weir and Cockerham, 1984) using GENETIX software. In addition, 5000 permutations of individuals among samples allowed the significance of the differentiations to be calculated.

Sequential Bonferroni correction (Rice, 1989) was performed for repeated tests.

When calculating Fst and Fis values, the upper and lower samples of the Azusa river system were joined in two independent, upper and lower assemblages. Fis value was calculated also for the entire Azusa sample-set. Genetic diversity was estimated for each sample and also for the upper and lower assemblages, respectively.

Table 2

Twelve microsatellite loci characteristics. The second column indicates the three multiplexes developed.

3 Results

3.1 Mitochondrial diversity

About 1100 bp, representing the complete CR mtDNA, were resolved with sequence analysis in 92 individuals (six to eight per wild population and altogether sixteen from hatcheries; Tab. 3).

After alignment, the CR sequences of Japanese samples collapsed into six haplotypes. They corresponded to previously described haplotypes ATcs1 to 4 and A17 (aka At1f). We also detected one previously undescribed haplotype that we named ATcs53 (GenBank Accession No MK330940).

The geographic distribution of the haplotypes observed in Japan is given in Table 3. The most common haplotypes were ATcs3 and ATcs4 (26% each) followed by Atcs2 (25%), ATcs1 and ATcs53 (10% each) and A17 (3%). The number of haplotypes varied from one to four per sample being the highest in Jigoku stream (Lake Chuzenji) and the lowest in the Chuzenji hatchery, the Kane stream and Azusa river (Shimauchi). Haplotypes ATcs3 and 4 were observed in the Cauterets hatchery, but only haplotype ATcs4 was found in the Isère hatchery.

Data on the distribution of haplotypes ATcs 1 to 4 outside Japan are collected in Appendix 1. When the haplotypes found in Japan were plotted onto the maximum parsimony network (Fig. 2) consisting of various haplotypes of the Atlantic brown trout (Appendix 2), they fell into clades 3-1 and 3-2 (sensu Cortey et al., 2009).

Table 3

Control region haplotype distribution. Numbers in parentheses indicate the number of haplotypes in a sample. For Map numbers, see Figure 1.

thumbnail Fig. 2

Haplotype network relating the haplotypes of the Atlantic clade found in brown trout in Japan (in red) with previously published data (Appendix 2). Lines, regardless of length, represent single mutational events and link the haplotypes; black dots represent a missing or theoretical haplotype. 3-1, 3-2 and 3-3 designate the clades as in Cortey et al. (2009) and are included in grey, pink and green envelopes.

3.2 Multidimensional analysis

In the multidimensional FCA graph, in which both samples from Japan and specimens from three hatcheries in France were included (Fig. 3A), three distinct clouds emerged. The cloud encircled by a black ellipse comprises individuals from French hatcheries along with most of the individuals from the Japanese rivers, while the cloud encircled by red includes samples from the Azusa river and that encircled by green represent the Chuzenji hatchery.

In the second step (Fig. 3B) the individuals from the Chuzenji hatchery and the Azusa samples were removed in order to obtain clearer resolution of the remaining samples (black circled in Fig. 3A). Thus, individuals from French hatcheries clustered on the left side of the graph, while Japanese samples grouped on the right with a possible cline of the Kane sample to the top and the Shiriuchi sample to the bottom.

thumbnail Fig. 3

Graphical presentation of the results inferred from multidimensional factorial correspondence analysis. Clouds detected on the diagram correspond to genetically similar individuals constituting lineages. A: the entire sample-set from Japan and specimens from three hatcheries in France; B: only the samples encircled with black in A.

3.3 Assignment structure

Classical assignment and the Delta K test recognized K = 2 as the most probable, followed by K = 5 and K = 7. The complete analysis, synthesized as a tree in Figure 4, described the steps from K = 2 to K = 7. The “Structure tree” recognized Azusa river brown trout as the most differentiated group followed by the samples from French hatcheries and the Chuzenji hatchery.

Hierarchical assignment analysis is presented in Figure 5. Azusa river was the first cluster excluded (cluster 2), which, in further steps, collapsed into clusters 2.1 and 2.2 corresponding to the upper and the lower sampling areas. Stemming from cluster 1, three genetically homogenous sub-clusters emerged: French hatcheries (1.3), Kane stream and Chuzenji hatchery (1.2) and the remaining samples (1.1) constituting a rather heterogeneous, weakly structured genetic group (Steps 3 to 6).

thumbnail Fig. 4

Successive assignment analyses, performed with STRUCTURE 2.3.4 software, from K = 2 to K = 7 represented as a tree. Numbers represent samples as in the first column of Table 1. Numbers in bold correspond to samples that “jumped" from one branch to another (= statistical artifacts).

thumbnail Fig. 5

Hierarchical assignment analysis. At each step and each cluster, STRUCTURE assignment is processed until absence of structure is reached (red crosses), i.e. multiple samples without subgroups (below 1.3.1, 2.1 and 2.2 where assignment cuts each individual and not between individuals) or a single sample with low diversity (all other cases).

3.4 Population parameters

Very low levels of genetic diversity were observed in the Chuzenji hatchery (Hnb = 0.39, A = 2.25) and in most Azusa river samples (0.44 < Hnb < 0.50, 2.83 < A < 4.5; Tab. 4). High values were obtained for the Jigoku stream with Hnb = 0.71 and A = 6.33. Note that most other samples showed Hnb values over 0.6, which is also considered as high (Bohling et al., 2016). The Hnb descriptors were generally in congruence with the level of genetic diversity of the populations described as measured by an average number of alleles per locus (A).

The Fis parameter, measuring deviation from the Hardy-Weinberg (H-W) expectation (Wright, 1951), indicated only a few panmixia deviations. Table 4 shows that five samples were significantly in H-W disequilibrium, but this was lost in three after Bonferroni correction and one was too small. As a result, only samples 15 and 17 were significantly out of panmixia at p < 0.05, demonstrating that globally, brown trout populations in Japan are in panmixia. The Fis value of the upper and the lower Azusa assemblage was estimated with high statistical support not to be different from zero, while for the entire Azusa sample-set, Fis reached 0.029 and the probability to differ from zero was 92.5%.

Inter-population differentiation based on pairwise Fst values is shown in Table 5. The nine Azusa samples were expected to constitute two populations isolated by impassable dams, so they were computed as two samples. The highest values were observed in comparison with the Chuzenji hatchery sample (0.31 < Fst < 0.44). The Azusa upper and lower assemblages were the next most differentiated units (0.22 < Fst < 0.33 excluding comparison with the Chuzenji hatchery sample). Genetic differentiation between the upper and lower assemblage was relatively low (Fst = 0.05) but highly significant. Within the upper and within the lower Azusa assemblages, there was genetic identity (non-significant Fst) between the samples (data not shown).

Table 4

The population parameters describe the samples' genetic diversity and their panmictic equilibrium. Panmixia is globally respected with some exceptions: * = significant at p < 0.95 (no longer significant after Bonferroni correction when between parentheses); na = not analyzed because the sample size was too small; ns = not significant (respects the panmixia). Azusa river trout were analyzed sample by sample, and then grouped into upstream and downstream assemblages (see text).

Table 5

Fst estimation between sample pairs, considering Azusa samples as separate, upper and lower assemblages. All comparisons are highly significantly different from zero (p < 0.0002, ***). Colored cells designate low values below 0.1 (red, orange, yellow and beige show increasing values). The framed cell shows the significant Fst between the upper and lower Azusa river.

4 Discussion

4.1 Origin of Japanese brown trout

The distribution of most of the haplotypes found in Japan is highly dispersed throughout Europe. In addition, the haplogroup ATcs1 to 4, which is characteristic of European domestic strains, also appears in most natural Atlantic populations along the whole Atlantic side of Europe from Norway to Spain, including Iceland, and also in Central and Eastern Europe up to Russia (Appendix 1). This is the main reason why, in most cases, it was not possible to recognize the origin of Japanese populations, despite some differences in the distribution of the four haplotypes. The presence of rare haplotype A17 was not supportive either. This haplotype was previously observed in the river Libĕchovka in the Elbe river system, Czech Republic, with the status of wild origin (Kohout et al., 2012), and in the Eisack/Isarco river, South Tyrol (named as At1f; Meraner et al., 2007). However, it cannot be ascertained that this haplotype is endemic to the Elbe river system nor are there any reports indicating brown trout transportation from the Czech Republic to Asia. Moreover, Meraner et al., (2007) suggested that this haplotype was also distributed from hatchery strains. The appearance of haplotype ATcs53, which has not been found previously, requires a more detailed explanation. We observed it only in the Chuzenji hatchery, in the Jigoku stream apparently reflecting the population of Lake Chuzenji, and in the upper part of the Azusa river. The introduction of brown trout into the lake goes back to the beginning of the 20th century (Maruyama et al., 1987). Nowadays, this haplotype is not part of commercial domestic lines, but it is possible that it might have been in the past and that it was later lost due to founder effects and/or random genetic drift. The first introductions of brown trout into the U.S., from where this species was assumed to be transferred to Lake Chuzenji (see Introduction), started from Germany in 1883 and 1884, but over the next few years, brown trout eggs arrived in U.S. hatcheries from Scotland and England as well (Behnke and Williams, 2007). Haplotype ATcs53 differs from haplotype ATcs49 by a single base mutation. It was detected in the Coquet river, British Isles (Cortey et al., 2009) and also does not belong to the contemporary hatchery haplogroup. Thus, these pieces of evidence suggest that haplotype ATcs53 found in Lake Chuzenji could have an origin in the British Isles. The lake population had served as a source for brown trout cultivation in Chuzenji hatchery (Fukuda, 1999), which explains how this haplotype came there. However, because the hatchery population has been maintained without extra input from outside since 2000 (e.g. from the lake), haplotype ATcs53 could have become fixed, due to random genetic drift and founder effects. Given that the material from the hatchery before 2000 was used to supply the remaining Japanese rivers, it is possible that this haplotype was also transferred into the Azusa river. Alternatively, it is also possible that it came there directly in eggs imported from America in the early 1930's when the upper Azusa river was supposedly first stocked (Sakata, 1974). This latter assumption is supported by inter-population differentiation tests (FCA, STRUCTURE and F indexes) all suggesting genetic distinction of the Azusa sample from all the other Japanese samples and the possibility of an independent import source of brown trout in the Azusa river system.

The Azusa river system was sampled at two sub-regions known as Kamikouchi (upstream) and Matsumoto (downstream) areas that are isolated from each other by four dams (Taishoh, Nagawado, Midono and Inekoki). While we do not know if construction of the dams occurred before or after trout introduction and are not sure if these dams are insurmountable to trout, our assignment and Fst tests clearly pointed out genetic interregional differentiation of the divided populations. Moreover, H-W disequilibrium was detected, if the entire Azusa sample-set was considered as one population, suggesting the presence of genetic substructures. Nevertheless, when calculated for the upper and the lower Azusa assemblage separately, both were found in panmixia. The coherence observed above and below the dams indicates gene flow between the samples within each area and limited or no transfer of genetic variation between the upstream and downstream samples. This suggests that each can be considered a single population.

The Chitose river was also examined at two stations (Mamachi and Montbetsu streams: samples 1 and 2). The Fst value between them is significant (Tab. 5), while they were not assigned to different lineages before K=7 in the one run assignment analysis (Fig. 4) nor before step 5 in the hierarchical assignment analysis (Fig. 5). All these results indicate slight genetic differences and the two stations, physically isolated by damming, cannot be considered as hosting the same population.

The remaining Japanese populations sampled (i.e. without Azusa) seem to stem from a single or several introductions based on genetically very similar material (Figs. 35; Tab. 5), which, taking into account the assumed history of brown trout stocking, is very likely for brown trout from Lake Chuzenji. An exception was the Chuzenji hatchery sample, which differed considerably from the Jigoku sample in fixation index (Fst = 0.29) on the basis of microsatellites, although sharing its only haplotype with the Jigoku (lake) sample. This differentiation can be explained by the fact that the hatchery population has been kept in captivity for more than 30 years without input of any fish from other populations. Its genetic diversity was found to be the lowest in the entire sample-set, even lower than for the fragmented Azusa river samples. This is probably a consequence of a small effective population size (small number of genitors) and selection pressure due to the hatchery environment accompanied by founder effects and random genetic drift. Altogether, this could have specifically shaped the distinct genetic profile of the Chuzenji hatchery population and impoverished its genetic diversity. Interestingly, the pair-wise Fst-values between the hatchery sample and each of the remaining Japanese samples is nevertheless much higher (0.30 < Fst < 0.44) than between the hatchery and lake samples (Fst = 0.29), which implies that the hatchery population originating from the lake has differentiated due to its long-lasting separation.

According to the records on brown trout imports to Japan, a French source was assumed to be involved. Using FCA, genetic proximity of most Japanese populations with individuals from French hatcheries was implied at first sight (Fig. 3A). This was supported with the very low Fst value 0.09 between the Lake Chuzenji and Cauterets hatchery samples suggesting a French origin of part of current Japanese populations as possible. However, more in-depth multidimensional analysis (Fig. 3B) revealed that the individuals from French hatcheries are actually rather separated from all the Japanese samples, suggesting that these Japanese populations are not of French origin, even though the Nagano Prefecture (mid part of Honshu island) is known to have been stocked with a brown trout strain shipped over by a French private company (Maruyama et al., 1987). Moreover, the Azusa river and Chuzenji hatchery samples appeared to be genetically very distinct from modern hatchery individuals, and so, as argued above, their source should probably be connected with early introduction from the USA. However, it should not be neglected that the genetic profile of microsatellites characterizing trout in modern French hatcheries could differ from that of fish imported from France, whose origin is not known. For more precise identification of the origin of Japanese trout, additional out-groups from the Atlantic basin should be included.

4.2 Adaptation of introduced populations

One of the known difficulties of adaptation of introduced species is their lack of genetic diversity. An introduced species is often characterized by a small founder population inducing strong founder effect and genetic drift (Sakai et al., 2001; Allendorf and Lundquist, 2003; Yonekura et al., 2007). The consequences can be inbreeding depression, poor ability to adapt and low success in a new environment (Reed and Frankham, 2003; Spielman et al., 2004).

In Japan, most introduced brown trout populations in rivers exhibited genetic diversity similar to that in domestic French hatcheries, which are considered as highly polymorphic (Hnb around 0.65, Bohling et al., 2016). The main exception is Azusa river samples, which always showed Hnb < 0.5, suggesting distinct origins and/or methods of management. The high genetic diversity of most Japanese brown trout populations results from the introduction of highly polymorphic strains and the suitability of Japanese rivers as a habitat for brown trout, since no founder effect was detectable except in the Azusa basin. Maintenance of wide diversity and genetic similarity all around Japan, except for the Azusa river, may also be due to a relatively recent common introduction and/or the presence of large populations able to avoid genetic drift.

5 Conclusions

Several useful observations can be inferred from this study on self-sustaining brown trout populations in Japan:

  • The introduced brown trout are genetically not homogeneous, which is probably a consequence of several introductions, at least two.

  • In most cases, there is no real geographic logic in the clustering of samples, which is expected for introduced populations depending mostly on human decisions; the exception is Azusa brown trout, which are differentiated from other populations and probably represent an independent import from the USA in the 1930's.

  • The Chuzenji hatchery (sample 4) houses a genetically very distinct strain. This is likely due to intense manipulation under isolated experimental conditions over 30 years.

  • Most populations showed high genetic diversity (Mamachi, Kane and Odori streams, Lake Chuzenji) and so favorable capacities for adaptation, with the exception of the Azusa river samples, where a certain lack of genetic polymorphism was detected.

  • While of limited efficiency, the genetic comparison between some European domestic samples and the Japanese ones suggested that brown trout in Japan stem from hatchery raised strains originating from north Atlantic European Rivers.

Acknowledgments

We thank Shoichiro Yamamoto, Kouta Miyamoto, Hideki Oohama, Masayuki Yagyu, Satoshi Kitano, Daisuke Kishi and Jun-ichi Tsuboi for providing samples and historical information about brown trout in each region, Shunpei Sato for help in the field of molecular biology and population genetics in Sapporo and Judith Anne Nikolić for English revision. Sampling campaigns were financially supported by JSPS KAKENHI Grant Number JP16K07857. Genotyping were performed by David Schikorski (Genindexe-Labofarm laboratory, France). Saša Marić was supported by the Ministry of Education, Science and Technological Development of the Republic of Serbia (Grant No. 173045). Aleš Snoj acknowledges financial support from the Slovenian Research Agency (Research core funding No. P4-0220).

Appendix 1

Appendix 1

Distribution of haplotypes ATcs1, 2, 3 and 4.

Appendix 2

Appendix 2

List of mtDNA CR haplotypes used for genealogical analysis, with their GenBank accession numbers. The six haplotypes detected in Japanese populations are underlined.

References

  • Allendorf FW, Lundquist LL. 2003. Introduction: population biology, evolution, and control of invasive species. Conserv Biol 17: 24–30. [Google Scholar]
  • Angers B, Bernatchez L, Angers A, Desgroseillers L. 1995. Specific microsatellite loci for brook charr reveal strong population subdivision on a microgeographic scale. J Fish Biol 47: 177–185. [Google Scholar]
  • Bailey RG. 1966. The dam fisheries of Tanzania. E Afr Agr Forestry J 32: 1–15. [CrossRef] [Google Scholar]
  • Bardakci F, Degerli N, Ozdemir O, Basibuyuk HH. 2006. Phylogeography of the Turkish brown trout Salmo trutta L.: mitochondrial DNA PCR-RFLP variation. J Fish Biol 68: 36–55. [Google Scholar]
  • Behnke RJ. 1986. Brown trout. Trout 27: 42–47. [Google Scholar]
  • Behnke RJ, Williams T. 2007. “Brown Trout-Winter, 1986". About trout: the best of Robert J. Behnke from Trout Magazine. Guilford, CT: Globe Pequot. pp 45. [Google Scholar]
  • Belkhir K, Borsa P, Goudet J, Bonhomme F. 2004. GENETIX 4.05: logiciel sous Windows pour la génétique des populations. Montpellier, France: Laboratoire Génome et Population, CNRS-UPR, Université de Montpellier II. [Google Scholar]
  • Benzécri JP. 1973. L'analyse des données. Paris, France: Dunod. 615 and 619 p. [Google Scholar]
  • Bernatchez L. 2001. The evolutionary history of brown trout (Salmo trutta L.) inferred from phylogeographic, nested clade, and mismatch analyses of mitochondrial DNA variation. Evolution 55: 351–379. [CrossRef] [PubMed] [Google Scholar]
  • Bernatchez L, Guyomard R, Bonhomme F. 1992. DNA sequence variation of the mitochondrial control region among geographically and morphologically remote European brown trout Salmo trutta populations. Mol Ecol 1: 161–173. [CrossRef] [PubMed] [Google Scholar]
  • Bohling J, Haffray P, Berrebi P. 2016. Genetic diversity and population structure of domestic brown trout (Salmo trutta) in France. Aquaculture 462: 1–9. [Google Scholar]
  • Budy P, Gaeta JW. 2018. Brown trout as an invader. In: Lobon-Cervia J, Sanz N. Eds. Brown trout: biology, ecology and management . Hoboken, New Jersey, USA: John Wiley and Sons Ltd., pp 525–543. [Google Scholar]
  • Budy P, Thiede GP, Lobon-Cervia J, et al. 2013. Limitation and facilitation of one of the world's most invasive fish: an intercontinental comparison. Ecology 94: 356–367. [Google Scholar]
  • Casalinuovo A, Alonso MF, Macchi PJ, Kuroda JA. 2018. Brown trout in Argentina. History, interactions and perspectives. In: Lobon-Cervia J, Sanz N. Eds. Brown trout: biology, ecology and management . Hoboken, New Jersey, USA: John Wiley and Sons Ltd, pp 17–63. [Google Scholar]
  • Charles K, Guyomard R, Hoyheim B, Ombredane D, Baglinière J-L. 2005. Lack of genetic differentiation between anadromous and resident sympatric brown trout (Salmo trutta) in a Normandy population. Aquat Living Resour 18: 65–69. [CrossRef] [Google Scholar]
  • Clement M, Posada D, Crandall K. 2000. TCS: A computer program to estimate gene genealogies. Mol Ecol 9: 1657–1660. [CrossRef] [PubMed] [Google Scholar]
  • Cortey M, Pla C, García-Marín J-L. 2000. Mitochondrial control region sequence divergence among Atlantic brown trout populations. Evolutionary and management considerations. Genbank https://www.ncbi.nlm.nih.gov/nuccore/AF273086. [Google Scholar]
  • Cortey M, Pla C, García-Marín JL. 2004. Historical biogeography of Mediterranean Trout. The role of allopatry and dispersal events. Mol Phylogenet Evol : 831–844. [Google Scholar]
  • Cortey M, Vera M, Pla C, García-Marín J-L. 2009. Northern and Southern expansions of Atlantic brown trout (Salmo trutta) populations during the Pleistocene. Biol J Linn Soc 97: 904–917. [CrossRef] [Google Scholar]
  • Davaine P, Beall E. 1992. Relationships between temperature, population density, and growth in a sea trout population (S. trutta L.) of the Kerguelen Islands. ICES J Mar Sci 49: 445–451. [Google Scholar]
  • Duftner N, Weiss S, Medgyesy N, Sturmbauer C. 2003. Enhanced phylogeographic information about Austrian brown trout populations derived from complete mitochondrial control region sequences. J Fish Biol 62: 427–435. [Google Scholar]
  • Earl DA, von Holdt BM. 2012. STRUCTURE HARVESTER: a website and program for visualizing STRUCTURE output and implementing the Evanno method. Conserv Genet Resour 4: 359–361. [Google Scholar]
  • Elliott JM. 1989. Wild brown trout Salmo trutta: an important national and international resource. Freshwater Biol 21: 1–5. [CrossRef] [Google Scholar]
  • Elliott JM. 1994. Quantitative ecology and the brown trout. Oxford, Great Britain: Oxford Series in Ecology and Evolution, Oxford University Press, p 286. [Google Scholar]
  • Estoup A, Largiader CR, Perrot E, Chourrout D. 1996. Rapid one-tube DNA extraction for reliable PCR detection of fish polymorphic markers and transgenes. Mol Mar Biol Biotech 5: 295–298. [Google Scholar]
  • Evanno G, Regnaut S, Goudet J. 2005. Detecting the number of clusters of individuals using the software STRUCTURE: a simulation study. Mol Ecol 14: 2611–2620. [CrossRef] [PubMed] [Google Scholar]
  • Fausch KD, White RJ. 1981. Competition between brook trout (Salvelinus fontinalis) and brown trout (Salmo trutta) for positions in a Michigan Stream. Can J Fish Aquat Sci 38: 1220–1227. [Google Scholar]
  • Fukuda K. 1999. The story of old anglers in Nikko. Yamatokeikokusha, Tokyo, 255pp. (in Japanese) [Google Scholar]
  • Gilbert KJ, Andrew RL, Bock DG, et al. 2012. Recommendations for utilizing and reporting population genetic analyses: the reproducibility of genetic clustering using the program STRUCTURE. Mol Ecol 21: 4925–4930. [CrossRef] [PubMed] [Google Scholar]
  • Hasegawa K. 2017. Displacement of native white-spotted charr Salvelinus leucomaenis by non-native brown trout Salmo trutta after resolution of habitat fragmentation by a migration barrier. J Fish Biol 90: 2475–2479. [CrossRef] [PubMed] [Google Scholar]
  • Hasegawa K, Maekawa K. 2009. Role of visual barriers on mitigation of interspecific interference competition between native and nonnative salmonids. Can J Zool 87: 781–786. [Google Scholar]
  • Hasegawa K, Mori T, Yamazaki C. 2017. Density-dependent effects of non-native brown trout Salmo trutta on the species-area relationship in stream fish assemblages. J Fish Biol 90: 370–383. [CrossRef] [PubMed] [Google Scholar]
  • Holm LECB. 2000. Oncorhynchus mykiss clone TAA72-13, sequence tagged site. Accession number AF239038. 2000. [Google Scholar]
  • Ishizaki D, Taniguchi Y, Yodo T. 2012. Establishment of brown trout Salmo trutta in Odori Stream, Jinzu River system, Gifu Prefecture, Central Japan. Jpn J Ichthyol 59: 49–54. [Google Scholar]
  • Jarry M, Beall E, Davaine P, et al. 2018. Sea trout (Salmo trutta) growth patterns during early steps of invasion in the Kerguelen Islands. Polar Biol 41: 925–934. [Google Scholar]
  • Jones P, Closs G. 2018. The Introduction of Brown Trout to New Zealand and their Impact on Native Fish Communities. In: Lobon-Cervia J, Sanz N. Eds. Brown trout: biology, ecology and management . Hoboken, New Jersey, USA: John Wiley and Sons Ltd, 545–567. [Google Scholar]
  • Jonsson B, Jonsson N. 2011. Ecology of Atlantic salmon and brown trout: habitat as a template for life histories. New York, USA: Springer. 655 pp. [Google Scholar]
  • Kawai H, Ishiyama N, Hasegawa K, Nakamura F. 2013. The relationship between the snowmelt flood and the establishment of non-native brown trout (Salmo trutta) in streams of the Chitose River, Hokkaido, northern Japan. Ecol Freshw Fish 22: 645–653. [Google Scholar]
  • Kawanabe H, Mizuno N. 1989. Freshwater fishes of Japan . Tokyo, Japan: Yama-kei Publishers Co., Ltd. 719 pp. [Google Scholar]
  • Kitano S, Hasegawa K, Maekawa K. 2009. Evidence for interspecific hybridization between native white-spotted charr Salvelinus leucomaenis and non-native brown trout Salmo trutta on Hokkaido Island, Japan. J Fish Biol 74: 467–473. [CrossRef] [PubMed] [Google Scholar]
  • Kitano S, Itsumi Y, Yagyu M, Mima J. 2013. Brown trout (Salmo trutta) invasion in irrigation canals along the Azusa River, central Nagano Prefecture. B. Nagano Env Conserv Res Inst 9: 67–70. [Google Scholar]
  • Kohout J, Jaskova I, Papousek I, Sediva A, Slechta V. 2012. Effects of stocking on the genetic structure of brown trout, Salmo trutta, in Central Europe inferred from mitochondrial and nuclear DNA markers. Fisheries Manag Ecol 19: 252–263. [CrossRef] [Google Scholar]
  • Laikre L, Antunes A, Apostolidis A, et al. 1999. Conservation genetic management of brown trout (Salmo trutta) in Europe. Report by the concerted action on identification, management and exploitation of genetic resources in the brown trout (Salmo trutta). “TROUTCONCERT”; EU FAIR CT97-3882. Silkeborg, Danmarks fiskeriundersrgelser. 91 pp. [Google Scholar]
  • Labonne J, Vignon M, Prévost E, et al. 2013. Invasion dynamics of a fish-free landscape by brown trout (Salmo trutta). PLoS ONE 8: 1–7. [Google Scholar]
  • Leitritz E, Lewis RC. 1980. Trout and salmon culture (hatchery methods). California Department of Fish and Game, Fish Bulletin 164. 197p. [Google Scholar]
  • Lobón-Cerviá J, Sanz N. 2018. Brown trout − biology, ecology and management. Chichester, UK: John Wiley and Sons Ltd. 790 pp. [Google Scholar]
  • Marić S, Kalamujić B, Snoj A, et al. 2012. Genetic variation of European grayling (Thymallus thymallus) populations in the Western Balkans. Hydrobiologia 691: 225–237. [Google Scholar]
  • Marić S, Sušnik Bajec S, Schöffmann J, Kostov V, Snoj A. 2017. Phylogeography of stream-dwelling trout in the Republic of Macedonia and a molecular genetic basis for revision of the taxonomy proposed by S. Karaman. Hydrobiologia 785: 249–260. [Google Scholar]
  • Maruyama T, Fujii K, Kijima T, Maeda H. 1987. Introduction of foreign fish species into Japan. Fisheries Agency Japan. [Google Scholar]
  • MacCrimmon HR, Marshall TL. 1968. World distribution of brown trout, Salmo trutta . J Fish Res Board Can 25: 2527–2548. [CrossRef] [Google Scholar]
  • MacCrimmon HR, Marshall TL, Gots BL. 1970. World distribution of brown trout, Salmo trutta: further observations. J Fish Res Board Can 27: 811–818. [CrossRef] [Google Scholar]
  • Meraner A, Baric S, Pelster B, Dalla Via J. 2007. Trout (Salmo trutta) mitochondrial DNA polymorphism in the centre of the marble trout distribution area. Hydrobiologia 579: 337–349. [Google Scholar]
  • Morita K, Tsuboi J, Matsuda H. 2004. The impact of exotic trout on native charr in a Japanese stream. J Appl Ecol 41: 962–972. [Google Scholar]
  • Nei M. 1978. Estimation of average heterozygosity and genetic distance from a small number of individuals. Genetics 89: 583–590. [PubMed] [Google Scholar]
  • O'Reilly PT, Hamilton LC, McConnell SK, Wright JM. 1996. Rapid analysis of genetic variation in Atlantic salmon (Salmo salar) by PCR multiplexing of dinucleotids and tetranucleotids microsatellites. Can J Fish Aquatic Sci 53: 2292–2298. [Google Scholar]
  • Ortiz-Sandoval J, Gorski K, Sobenes C, et al. 2017. Invasive trout affect trophic ecology of Galaxias platei in Patagonian lakes. Hydrobiologia 790: 201–212. [Google Scholar]
  • Presa P, Krieg F, Estoup A, Guyomard R. 1994. Diversité et gestion génétique de la truite commune: apport de l'étude du polymorphisme des locus protéiques et microsatellites. Genet Select Evolut 26: 183–202. [CrossRef] [Google Scholar]
  • Presa P, Guyomard R. 1996. Conservation of microsatellites in three species of salmonids. J Fish Biol 49: 1326–1329. [Google Scholar]
  • Pritchard JK, Stephens M, Donnelly P. 2000. Inference of population structure using multilocus genotype data. Genetics 155: 945–959. [Google Scholar]
  • Reed DH, Frankham R. 2003. Correlation between fitness and genetic diversity. Conserv Biol 17: 230–237. [Google Scholar]
  • Rexroad CE, Coleman RL, Hershberger WK, Killefer J. 2002. Rapid communication: thirty-eight polymorphic microsatellite markers for mapping in rainbow trout. J Animal Sci 80: 541–542. [CrossRef] [Google Scholar]
  • Rice WR. 1989. Analyzing tables of statistical tests. Evolution 43: 223–225. [CrossRef] [PubMed] [Google Scholar]
  • Sakai AK, Allendolf FW, Holt JS, et al. 2001. The population biology of invasive species. Annu Rev Ecol Syst 32: 305–332. [Google Scholar]
  • Sakata H. 1974. White-spotted charr in Kamikouchi. The natural history of northern Japanese Alps. Oomachi Museum of Mountain. Nagano, Japan: Shinanoji co, pp 178–182. [Google Scholar]
  • Scribner KT, Gust JR, Fields RL. 1996. Isolation and characterization of novel salmon microsatellite loci: cross-species amplification and population genetic applications. Can J Fish Aquat Sci 53: 833–841. [Google Scholar]
  • Shimoda K. 2012. Alien fish problems in Hokkaido (introduced salmonidae fishes). Nippon Suisan Gakkaishi , 78: 754–757. (in Japanese) [CrossRef] [Google Scholar]
  • Slettan A, Olsaker I, Lie Ø. 1995. Atlantic salmon, Salmo salar, microsatellites at the SSOSL25, SSOSL85, SSOSL311, SSOSL417 loci. Animal Genet 26: 277–285. [Google Scholar]
  • Slettan A, Olsaker I, Lie Ø. 1996. Polymorphic Atlantic salmon, Salmo salar L., microsatellites at the SSOSL438, SSOSL439 and SSOSL444 loci. Animal Genet 27: 57–64. [CrossRef] [Google Scholar]
  • Snoj A, Marić S, Berrebi P, Crivelli AJ, Shumka S, Sušnik S. 2009. Genetic architecture of trout from Albania as revealed by mtDNA control region variation. Genet Sel Evol 41: 22. [Google Scholar]
  • Snoj A, Marić S, Sušnik Bajec S, Berrebi P, Janjani S, Schőffmann J. 2011. Phylogeographic structure and demographic patterns of brown trout in North-West Africa. Mol Phylogenet Evol 61: 203–211. [Google Scholar]
  • Spielman D, Brook BW, Frankham R. 2004. Most species are not driven to extinction before genetic factors impact them. P Natl Acad Sci USA 101: 15261–15264. [CrossRef] [Google Scholar]
  • Suarez J, Bautista JM, Almodovar A, Machordom A. 2001. Evolution of the mitochondrial control region in Palaearctic brown trout (Salmo trutta) populations: the biogeographical role of the Iberian Peninsula. Heredity 87: 198–206. [CrossRef] [PubMed] [Google Scholar]
  • Takami T, Yoshihara T, Miyakoshi Y, Kuwabara R. 2002. Replacement of white-spotted charr Salvelinus leucomaenis by brown trout Salmo trutta in a branch of the Chitose River, Hokkaido. Nippon Suisan Gakkaishi 68: 24–28. [CrossRef] [Google Scholar]
  • Tamura K, Stecher G, Peterson D, Filipski A, Kumar S. 2013. MEGA6: Molecular Evolutionary Genetics Analysis version 6.0. Mol Biol Evol. 30: 2725–2729. [CrossRef] [PubMed] [Google Scholar]
  • Tanizawa K, Oohama H, Ozawa R, Tsuboi J, Hasegaw, K. 2016. The effect of brown trout eradication in Kane River, a tributary of Fuji River. Report of Yamanashi Prefectural Fisheries Technology Center , 43, 8–16. [Google Scholar]
  • Thompson JD, Gibson TJ, Plewniak F, Jeanmougin F, Higgins DG. 1997. The CLUSTAL_X windows interface: flexible strategies for multiple sequence alignment aided by quality analysis tools. Nucleic Acids Res 25: 4876–4882. [CrossRef] [PubMed] [Google Scholar]
  • Tougard C, Justy F, Guinand B, Douzery EJP, Berrebi P. 2018. Salmo macrostigma (Teleostei, Salmonidae): nothing more than a brown trout (S. trutta) lineage? J Fish Biol 93: 302–310. [CrossRef] [PubMed] [Google Scholar]
  • Townsend CR. 1996. Invasion biology and ecological impact of brown trout Salmo trutta in New Zealand. Biol Cons 78: 13–22. [CrossRef] [Google Scholar]
  • Uiblein F, Jagsch A, Honsig-Erlenburg W, Weiss S. 2001. Status, habitat use, and vulnerability of the European grayling in Austrian waters. J Fish Biol 59: 223–247. [Google Scholar]
  • Urawa S. 1989. Seasonal occurrence of Microsporidium takedai (Microsporida) infection in masu salmon, Oncorhynchus masou, from the Chitose River. Physiol Ecol Jpn 1: 587–598. [Google Scholar]
  • Vähä JP, Erkinaro J, Niemelä E, Primmer CR. 2007. Life-history and habitat features influence the within-river genetic structure of Atlantic salmon. Mol Ecol 16: 2638–2654. [CrossRef] [PubMed] [Google Scholar]
  • Weir BS, Cockerham CC. 1984. Estimating F-statistics for the analysis of population structure. Evolution 38: 1358–1370. [CrossRef] [PubMed] [Google Scholar]
  • Weyl OLF, Ellender BR, Ivey P, et al. 2018. Africa: Brown trout introductions, establishment, current status, impacts and conflicts. In: Lobon-Cervia J, Sanz N. Eds. Brown trout: biology, ecology and management. Hoboken, New Jersey, USA: John Wiley and Sons Ltd., 623–639. [Google Scholar]
  • Wright S. 1951. The genetical structure of populations. Ann Eugen 15: 323–354. [CrossRef] [PubMed] [Google Scholar]
  • Yagyu M, Kitano S, Otsuki K, Mima J. 2016. Expanding distribution and establishment of the invasive alien fish brown trout Salmo trutta in Matsumoto basin. The Bulletin of Shiojiri City Museum of Natural History , 16, 1–8. [Google Scholar]
  • Yonekura R, Kawamura K, Uchii K. 2007. A peculiar relationship between genetic diversity and adaptability in invasive exotic species: bluegill sunfish as a model species. Ecol Res 22: 911–919. [Google Scholar]

Cite this article as: Berrebi P, Marić S, Snoj A, Hasegawa K. 2020. Brown trout in Japan − introduction history, distribution and genetic structure. Knowl. Manag. Aquat. Ecosyst., 421, 18.

All Tables

Table 1

Description of the nineteen sampling stations and three reference hatcheries, number of individuals analyzed (N) and the date of sampling. E = flowing to eastern Japan: Pacific Ocean; W = to the west: Sea of Japan. All the sampling stations are in Honshu Island unless indicated as “Hokk.” = Hokkaido Island.

Table 2

Twelve microsatellite loci characteristics. The second column indicates the three multiplexes developed.

Table 3

Control region haplotype distribution. Numbers in parentheses indicate the number of haplotypes in a sample. For Map numbers, see Figure 1.

Table 4

The population parameters describe the samples' genetic diversity and their panmictic equilibrium. Panmixia is globally respected with some exceptions: * = significant at p < 0.95 (no longer significant after Bonferroni correction when between parentheses); na = not analyzed because the sample size was too small; ns = not significant (respects the panmixia). Azusa river trout were analyzed sample by sample, and then grouped into upstream and downstream assemblages (see text).

Table 5

Fst estimation between sample pairs, considering Azusa samples as separate, upper and lower assemblages. All comparisons are highly significantly different from zero (p < 0.0002, ***). Colored cells designate low values below 0.1 (red, orange, yellow and beige show increasing values). The framed cell shows the significant Fst between the upper and lower Azusa river.

Appendix 1

Distribution of haplotypes ATcs1, 2, 3 and 4.

Appendix 2

List of mtDNA CR haplotypes used for genealogical analysis, with their GenBank accession numbers. The six haplotypes detected in Japanese populations are underlined.

All Figures

thumbnail Fig. 1

Geographic position of the 19 Japanese samples analyzed.

In the text
thumbnail Fig. 2

Haplotype network relating the haplotypes of the Atlantic clade found in brown trout in Japan (in red) with previously published data (Appendix 2). Lines, regardless of length, represent single mutational events and link the haplotypes; black dots represent a missing or theoretical haplotype. 3-1, 3-2 and 3-3 designate the clades as in Cortey et al. (2009) and are included in grey, pink and green envelopes.

In the text
thumbnail Fig. 3

Graphical presentation of the results inferred from multidimensional factorial correspondence analysis. Clouds detected on the diagram correspond to genetically similar individuals constituting lineages. A: the entire sample-set from Japan and specimens from three hatcheries in France; B: only the samples encircled with black in A.

In the text
thumbnail Fig. 4

Successive assignment analyses, performed with STRUCTURE 2.3.4 software, from K = 2 to K = 7 represented as a tree. Numbers represent samples as in the first column of Table 1. Numbers in bold correspond to samples that “jumped" from one branch to another (= statistical artifacts).

In the text
thumbnail Fig. 5

Hierarchical assignment analysis. At each step and each cluster, STRUCTURE assignment is processed until absence of structure is reached (red crosses), i.e. multiple samples without subgroups (below 1.3.1, 2.1 and 2.2 where assignment cuts each individual and not between individuals) or a single sample with low diversity (all other cases).

In the text

Current usage metrics show cumulative count of Article Views (full-text article views including HTML views, PDF and ePub downloads, according to the available data) and Abstracts Views on Vision4Press platform.

Data correspond to usage on the plateform after 2015. The current usage metrics is available 48-96 hours after online publication and is updated daily on week days.

Initial download of the metrics may take a while.