Effects of range position and inbreeding on early life fitness traits in red spruce, Picea rubens (Sarg.)
Notice bibliographique
Résumé
Red spruce (Picea rubens Sarg.) is a boreal coniferous tree native to the eastern United States and Canada. As a result of post-glacial range expansion and more recent land-use change, red spruce populations have become fragmented, creating small isolated populations throughout the southeastern part of its range. A major question then becomes whether this range fragmentation has led to inbreeding and susceptibility of young seedlings to the effects of inbreeding depression. This study investigates variation in early-life fitness traits in relation to population size and inbreeding history in 340 open-pollinated seed families of red spruce trees sampled from populations located in three different source regions: the core, margin, and southeastern edge of the range. Four measures of fitness were used to calculate overall seedling early life fitness: average seed mass, germination proportion, survivorship, and growth (seedling height) after 12 weeks. To estimate inbreeding history for use in predicting seedling fitness, we used whole exome sequences obtained for the mother tree for each of the 340 families, and used these sequences to estimate population and individual-level homozygosity. Early life traits and a composite measure of overall seedling fitness were then predicted by genetic inbreeding history and source region using linear mixed-effect models with backwards selection and model ranking based on Akaike information criterion (AIC). There was a significantly higher level of population and individual-level homozygosity in the edge region when compared to the core and margin. Overall seedling fitness was also significantly lower in the edge region, consistent with an association between range fragmentation, inbreeding, and early life fitness. However, mixed models that accounted for random effects of population and family level variation did not show evidence of statistically significant associations between fitness traits with source region and inbreeding history. One possible explanation could be a counter-balancing that is occurring between the isolated, inbred population structure and the historic genetic variation in the southern part of the range, making some traits appear more significant than others. My results suggest that there appears to be a higher level of inbreeding and a lower level of overall seedling fitness in southern edge populations that could be caused by population inbreeding depression. These findings may have important implications for current and future restoration efforts targeted at red spruce as local environments continue to change.
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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 | 0,001 |
| Méta-épidémiologie (sens large) | 0,001 | 0,000 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,001 | 0,001 |
| Intégrité de la recherche | 0,001 | 0,001 |
| 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 ».