Climate outweighs native vs. nonnative range‐effects for genetics and common garden performance of a cosmopolitan weed
Notice bibliographique
Résumé
Abstract Comparing genetic diversity, genetic differentiation, and performance between native and nonnative populations has advanced our knowledge of contemporary evolution and its ecological consequences. However, such between‐range comparisons can be complicated by high among‐population variation within native and nonnative ranges. For example, native vs. nonnative comparisons between small and non‐representative subsets of populations for species with very large distributions have the potential to mislead because they may not sufficiently account for within‐range adaptation to climatic conditions, and demographic history that may lead to non‐adaptive evolution. We used the cosmopolitan weed Conyza canadensis to study the interplay of adaptive and demographic processes across, to our knowledge, the broadest climatic gradient yet investigated in this context. To examine the distribution of genetic diversity, we genotyped 26 native and 26 nonnative populations at 12 microsatellite loci. Furthermore, we recorded performance traits for 12 native and 13 nonnative populations in the field and in the common garden. To analyze how performance was related to range and/or climate, we fit pedigree mixed‐effects models. These models weighed the population random effect for co‐ancestry to account for the influence of demographic history on phenotypic among‐population differentiation. Genetic diversity was very low, selfing rates were very high, and both were comparable between native and nonnative ranges. Nonnative populations out‐performed native populations in the field. However, our most salient result was that both neutral genetic differentiation and common garden performance were far more correlated with the climatic conditions from which populations originated than native vs. nonnative range affiliation. Including co‐ancestry of our populations in our models greatly increased explained variance and our ability to detect significant main effects for among‐population variation in performance. High propagule pressure and high selfing rates, in concert with the ability to adapt rapidly to climatic gradients, may have facilitated the global success of this weed. Neither native nor nonnative populations were homogeneous groups but responded comparably to similar environments in each range. We suggest that studies of contemporary evolution should consider widely distributed and genotyped populations to disentangle native vs. nonnative range effects from varying adaptive processes within ranges and from potentially confounding effects of demographic history.
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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 ».