Beringian sub-refugia revealed in blackfish (Dallia): implications for understanding the effects of Pleistocene glaciations on Beringian taxa and other Arctic aquatic fauna
Notice bibliographique
Résumé
BACKGROUND: Pleistocene climatic instability had profound and diverse effects on the distribution and abundance of Arctic organisms revealed by variation in phylogeographic patterns documented in extant Arctic populations. To better understand the effects of geography and paleoclimate on Beringian freshwater fishes, we examined genetic variability in the genus Dallia (blackfish: Esociformes: Esocidae). The genus Dallia groups between one and three nominal species of small, cold- and hypoxia-tolerant freshwater fishes restricted entirely in distribution to Beringia from the Yukon River basin near Fairbanks, Alaska westward including the Kuskokwim River basin and low-lying areas of Western Alaska to the Amguema River on the north side of the Chukotka Peninsula and Mechigmen Bay on the south side of the Chukotka Peninsula. The genus has a non-continuous distribution divided by the Bering Strait and the Brooks Range. We examined the distribution of genetic variation across this range to determine the number and location of potential sub-refugia within the greater Beringian refugium as well as the roles of the Bering land bridge, Brooks Range, and large rivers within Beringia in shaping the current distribution of populations of Dallia. Our analyses were based on DNA sequence data from two nuclear gene introns (S7 and RAG1) and two mitochondrial genome fragments from nineteen sampling locations. These data were examined under genetic clustering and coalescent frameworks to identify sub-refugia within the greater Beringia refugium and to infer the demographic history of different populations of Dallia. RESULTS: We identified up to five distinct genetic clusters of Dallia. Four of these genetic clusters are present in Alaska: (1) Arctic Coastal Plain genetic cluster found north of the Brooks Range, (2) interior Alaska genetic cluster placed in upstream locations in the Kuskokwim and Yukon river basins, (3) a genetic cluster found on the Seward Peninsula, and (4) a coastal Alaska genetic cluster encompassing downstream Kuskokwim River and Yukon River basin sample locations and samples from Southwest Alaska not in either of these drainages. The Chukotka samples are assigned to their own genetic cluster (5) similar to the coastal Alaska genetic cluster. The clustering and ordination analyses implemented in Discriminant Analysis of Principal Components (DAPC) and STRUCTURE showed mostly concordant groupings and a high degree of differentiation among groups. The groups of sampling locations identified as genetic clusters correspond to geographic areas divided by likely biogeographic barriers including the Brooks Range and the Bering Strait. Estimates of sequence diversity (θ) are highest in the Yukon River and Kuskokwim River drainages near the Bering Sea. We also infer asymmetric migration rates between genetic clusters. The isolation of Dallia on the Arctic Coastal Plain of Alaska is associated with very low estimated migration rates between the coastal Alaska genetic cluster and the Arctic Coastal Plain genetic cluster. CONCLUSIONS: Our results support a scenario with multiple aquatic sub-refugia in Beringia during the Pleistocene and the preservation of that structure in extant populations of Dallia. An inferred historical presence of Dallia across the Bering land bridge explains the similarities in the genetic composition of Dallia in West Beringia and western coastal Alaska. In contrast, historic and contemporary isolation across the Brooks Range shaped the distinctiveness of present day Arctic Coastal Plain Dallia. Overall this study uncovered a high degree of genetic structuring among populations of Dallia supporting the idea of multiple Beringian sub-refugia during the Pleistocene and which appears to be maintained to the present due to the strictly freshwater nature and low dispersal ability of this genus.
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Comment cette classification a été obtenuedéplier
Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,000 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».