Co-limitation of resources reveals adaptations of a tropical tree to heterogeneous environments along an elevational cline
Bibliographic record
Abstract
Introduction Plant species often exhibit significant variation in functional traits in populations along elevational gradients to cope with varying stress conditions. While plant development has been assumed to be most limited by a single resource, growing evidence suggests the potential for interactions of co-limiting resources to impact plant performance. Here, we aimed to determine how light, nitrogen, and water availability influence the growth and physiology of different populations of koa ( Acacia koa ), a tree species of concern that occurs across a large elevational gradient in tropical Hawaii, United States. Methods Populations from three seed sources [low (L), mid (M), and high (H) elevation] were grown in a controlled greenhouse experiment and exposed to co-limiting light, water, and nutrient (nitrogen) conditions. Light response, gas exchange, water status, resource use efficiency, nutrients and shoot non-structural carbohydrate concentrations, and growth and biomass allocation responses were quantified. Results We found that resource co-limitation sometimes interacted to determine responses of the measured parameters. In general, the low elevation (L) koa population was more sensitive to conditions where both moisture and nutrients were limiting, while the high elevation (H) koa population was more sensitive to conditions where either light and moisture or light and nitrogen were co-limiting. The M population performed well overall regardless of resource limitation. Discussion Our findings lend support to the theory that multiple resources limit growth and physiology of populations rather than the traditional view of a single resource limiting performance. Therefore, the possibility that multiple resource limitations drive population differences should be considered when developing population-based guidelines for forest and tree species restoration.
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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 | 0.000 |
| 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 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".