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Enregistrement W4252015269 · doi:10.1111/jbi.12958

Phylogeography

2017· article· en· W4252015269 sur OpenAlexaboutno aff
H. P. Linder

Notice bibliographique

RevueJournal of Biogeography · 2017
Typearticle
Langueen
DomaineBiochemistry, Genetics and Molecular Biology
ThématiqueGenetic diversity and population structure
Établissements canadiensnon disponible
Organismes subventionnairesnon disponible
Mots-clésPhylogeographyEcologyBiogeographyVicarianceRange (aeronautics)BiologyGeographyPhylogenetics

Résumé

récupéré en direct d'OpenAlex

This issue is dedicated to phylogeography, a relatively young research field dating back to some 30 years. The field was sculpted by John Avise's book ‘Phylogeography’ (Avise, 2000), and the subsequent rapid developments in molecular techniques which facilitate a rapid, cheap and accurate documentation of genetic differentiation across many populations. This was paralleled by better analytical tools. In the last decade, phylogeography has become a major component of biogeography. Phylogeographical methods have been used to address many questions. The major achievements of Avise and colleagues were illustrating shared biogeographical patterns in freshwater, terrestrial and marine taxa in SE USA; meanwhile, on the other side of the Atlantic, phylogeographical studies made major progress in elucidating Late Quaternary range contractions and expansions in Europe. The remarkable success of phylogeographical methods in disentangling the Late Quaternary history of the European biota stimulated similar studies in many parts of the globe, terrestrial and marine. It also led to the asking of more complex questions, integrating ecology and history. The collection of papers in this issue does not cover the full breadth of issues addressed using phylogeographical methods, but it does give a taste of what is possible. The search for refuges (typically, where species survived periods when the climate was less suitable) and range expansion patterns is still a very common theme in phylogeographical studies, and is also the focus of many papers in this issue, covering Arctic to tropical as well as terrestrial and marine species. Gutiérrez-Rodríguez et al. (2017) demonstrate southern Iberian refuges with northern expansion for the spadefoot toad, matching species distribution models, with patterns of genetic diversity. Alexandri et al. (2017) continued their exploration of Balkan refuges for wild boar, showing differences between microsatellite and genome-wide SNP analyses. Hantemirova et al. (2017) showed that far northern Russian and Siberian populations of the common juniperus are largely descendent from populations which survived in cryptic northern refuges, and that the contribution of southern refuges to these populations was relatively small, thus upsetting the paradigm of southern refuges. The region(s) where the Aleutian flora survived the Last Glacial Maximum (LGM) is still unknown, and Hata et al. (2017) showed that Therorhodion camtschaticum (Ericaceae) probably survived the LGM on Kamchatka. The evolution of the enormous diversity of the Hengduanshan Biodiversity Centre is still enigmatic. Du et al. (2017) explored the phylogeography of one of the woody species in the region, Quercus aquilofioides, and showed that in Tibet, the species presence was dynamic, whereas in the Hengduan Mountains, populations were stable, and that this stability is associated with a high haplotype diversity. It has long been suggested that during the LGM, the African tropical rain forests were restricted to few refugia, and Ley et al. (2017) demonstrate that the genetic patterns in the liana Haumania are consistent with these refuges with subsequent range expansion and admixture. The climate in deserts also fluctuated during the Late Quaternary, and Scheinvar et al. (2017) showed that Agave lechuguilla survived the LGM in five refuges in the North American Chihauhuan desert from which it expanded during the Holocene. Spatial genetic structure in marine organisms is also consistent with fluctuating climates, illustrated for red mangroves in Florida (comparing the east and west coasts) by Kennedy et al. (2017), and for Atlantic starfish by Pérez-Portela et al. (2017). These case studies add to the detailed understanding, developed over the past decade, of the biotic responses to Quaternary climatic fluctuation. There are some interesting complications to these patterns. Fang et al. (2017) showed that the genetic patterns were strongly influenced by the reproductive biology in two aphid species which feed on the same species of Chinese willow, but which have different life cycles, with the sexual species with more genetic and population variation than the asexual species. Llorens et al. (2017) demonstrated that the wind pollinated Australian shrub, Allocasuarina humilis, showed no regional genetic structure, indicative of populations that have had their current distribution for a long time, but that genetic structure was influenced by the degree of population fragmentation. Another form of intrinsic genetic variation was shown by McMinn et al. (2017): marginal populations were genetically less variable than central populations in Cirsium canescens in North America. Simplistically, spatial genetic structure in a species is due to either adaptation or historical factors, but the two are rarely combined. Such a combination combines the fields of trait and historical biogeography. Polfus et al. (2017) explore this link between ecotypes and genetic lineages in Canadian caribou, and Díaz et al. (2017) between the distribution of cryptic coloration and genetic lineages in an Iberian lizard. Islands also generate phylogeographical pattern, for example, in the Indian Ocean geckos (Hawlitschek et al., 2017). Such patterns can persist long after the ocean has retreated, leaving previous islands as modern mountains, as illustrated for a Carpathian crustacean (Copilaş-Ciocianu & Petrusek, 2017), restricted to mountains that formed an archipelago in the European Miocene epicontinental sea. Phylogeographical patterns are often taxonomy informative (Avise, 2000). Habel et al. (2017) combined morphological data with genetically informed phylogeographical methods to evaluate the divergence in the marbled butterfly species group in the Western Palaearctic. The documentation of the phylogeographical patterns in starfish in the Atlantic and Mediterranean Pérez-Portela et al. (2017) also led to the recognition of two species separated by oceanic currents. Finally, a criticism of many phylogeographical studies is that they apply only to one species, and so have no generality. Comparative phylogeography, where the responses of many lineages to the same barrier are explored, is one way of seeking general phylogeographical patterns. This approach is illustrated here by Myers et al. (2017), who show an asynchronous differentiation across the same North American interval by several snake lineages. The challenge in comparative phylogeographical studies is obtaining sufficient data, and Gratton et al. (2017) explore the possibility of using the enormous resources already available on Genbank to this end. Frustratingly, although the methods work, there are still very few georeferenced sequences on Genbank. In combination, this collection of papers, reporting on studies from almost all continents, illustrates the enormous range of questions that can be addressed with phylogeographical methods. There are still many other questions waiting to be addressed, ranging from the impact of the south-central African mega-droughts (Lyons et al., 2015) to a more general understanding of the links between trait and phylogeographical variation. Equally exciting is the development of new methods, from genomic (next generation sequencing) approaches to high resolution remote sensing, which may facilitate phylogeographical research. We are looking forward to being able to publishing many more exciting phylogeographical papers.

Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.

Comment cette classification a été obtenuedéplier

Prédiction distillée sur la base complète

Imitation des enseignants

Ni 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.

score de la tête « metaresearch » (Codex)0,000
score de la tête « metaresearch » (Gemma)0,000
Version: codex-gemma-dda1882f352aStatut de validation: machine_predicted_unvalidated
Catégories candidatesaucune
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Observationnel · Signal consensuel: Observationnel
GenreSignal candidat: Empirique · Signal consensuel: Empirique
Score de désaccord entre enseignants0,160
Score d'incertitude au seuil0,288

Scores Codex et Gemma par catégorie

CatégorieCodexGemma
Métarecherche0,0000,000
Méta-épidémiologie (sens strict)0,0000,000
Méta-épidémiologie (sens large)0,0000,000
Bibliométrie0,0000,000
Études des sciences et des technologies0,0000,000
Communication savante0,0000,000
Science ouverte0,0000,000
Intégrité de la recherche0,0000,000
Charge utile insuffisante (le modèle a refusé de juger)0,0000,000

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.

Tête enseignante Opus0,010
Tête enseignante GPT0,246
Écart entre enseignants0,236 · la distance entre les deux têtes enseignantes sur ce seul travail
Statut de validationscore_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 découle

Classification

machine, non validée

Prédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.

Les modèles n’ont appliqué aucune catégorie : rien dans la taxonomie ne correspondait à ce travail.
Devis d'étudeObservationnel
Domainenon disponible
GenreEmpirique

Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».

En bref

Citations2
Publié2017
Routes d'admission1
Résumé présentoui

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