Tropical shrubs living in an extreme environment show convergent ecological strategies but divergent ecophysiological strategies
Bibliographic record
Abstract
BACKGROUND AND AIMS: Trait-based frameworks assess plant survival strategies using different approaches. Some frameworks use functional traits to assign species to a priori defined ecological strategies. Others use functional traits as the central element of a species ecophysiological strategy. We compared these two approaches by asking: (1) what is the primary ecological strategy of three dominant co-occurring shrub species from inselbergs based on the CSR scheme, and (2) what main functional traits characterize the ecophysiological strategy of the species based on their use of carbon, water and light? METHODS: We conducted our study on a Colombian inselberg. In this extreme environment with multiple stressors (high temperatures and low resource availability), we expected all species to be stress tolerant (S in the CSR scheme) and have similar ecophysiological strategies. We measured 22 anatomical, morphological and physiological leaf traits. KEY RESULTS: The three species have convergent ecological strategies as measured by CSR (S, Acanthella sprucei; and S/CS, Mandevilla lancifolia and Tabebuia orinocensis) yet divergent resource-use strategies as measured by their functional traits. A. sprucei has the most conservative carbon use, risky water use and a shade-tolerant strategy. M. lancifolia has acquisitive carbon use, safe water use and a shade-tolerant strategy. T. orinocensis has intermediate carbon use, safe water use and a light-demanding strategy. Additionally, stomatal traits that are easy to measure are valuable to describe resource-use strategies because they are highly correlated with two physiological functions that are hard to measure: stomatal conductance and maximum photosynthesis per unit mass. CONCLUSIONS: The two approaches provide complementary information on species strategies. Plant species can co-occur in extreme environments, such as inselbergs, because they exhibit convergent primary ecological strategies but divergent ecophysiological strategies, allowing them to use limiting resources differently.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".