Comparative Seedling Ecology of Eight North American Spruce (Picea) Species in Relation to their Geographic Ranges
Bibliographic record
Abstract
BACKGROUND AND AIMS: Allowing for dispersal limitation, a species' geographic distribution should reflect its environmental requirements. Comparisons among closely related species should reveal adaptive differentiation in species characteristics that are consistent with their differences in geographic distribution. This expectation was tested by comparing characteristics of seedlings of spruce species in relation to environmental factors representative of their current natural ranges. METHODS: Seedlings were grown from a total of 34 populations representing eight North American spruce (Picea) species in a controlled environment chamber for 140 d. Traits related to the potential of seedling establishment, including tolerance to stress events (high temperature, desiccation) were evaluated. Correlations were sought between these characteristics and modal values of latitude, aridity and continentality in the geographic range of each species. KEY RESULTS: Many seedling traits changed significantly in response to stress events, but only the response of chlorophyll concentration differed significantly among species. Components of seedling growth were good correlates of species distribution. Seedling relative growth rate (RGR) and specific leaf area (SLA) were positively correlated with latitude, and leaf weight ratio (LWR) negatively correlated with aridity. Seed mass was negatively correlated with latitude. CONCLUSIONS: Relationships found between seedling traits and geographical variation in environmental conditions suggest that factors such as temperature regime, water availability and perhaps litter depth affect species range in North American spruces. Seedling characteristics appear to be elements in a reasonably distinct environmental niche for each spruce species at the continental scale.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 source (direct Gemma or distilled Codex), 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".