Effects of range position and inbreeding on early life fitness traits in red spruce, Picea rubens (Sarg.)
Bibliographic record
Abstract
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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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".