Plant functional type classifications in tropical dry forests in Costa Rica: leaf habit versus taxonomic approaches
Bibliographic record
Abstract
Summary 1. One way to simplify the high taxonomic diversity of plant species in vegetation models is to place species into groups based on shared, dominant traits. Many studies have suggested that morphological and physiological traits of tropical dry forest tree species vary with leaf habit (i.e. leaves from evergreen, deciduous or semi‐deciduous species) and thus this characteristic may serve as a useful way to distinguish ecologically meaningful functional types. 2. In this study we examine whether 10 plant traits vary with leaf habit in replicated leaves and individual trees of 87 species from a tropical dry forest in Costa Rica. We also looked for evidence of phylogenetic conservatism, i.e. closely related species sharing similar trait values compared to more distantly related taxa. 3. While some of the traits varied within and among individual trees of the same species, interspecific variation accounted for 57–83% of the variance among samples. Four traits in addition to leaf habit showed evidence of phylogenetic conservatism, but these results were strongly dependent on the inclusion of the 18 species of legumes (Fabaceae) in our dataset. Contrary to our predictions, none of the traits we measured differed among leaf habits. However, five traits (wood density, leaf C, leaf N, N/P and C/N) varied significantly between legumes and other functional types. Furthermore, when all high‐nitrogen non‐legume taxa were compared to the high‐nitrogen legumes, six traits excluding leaf N differed significantly, indicating that legumes are functionally different from other tree species beyond high N concentrations. Similarly, the 18 legume taxa (which all have compound leaves) also differed from other compound‐leaved species for six traits, thus leaf type does not explain these patterns. 4. Our main conclusions are that (i) a plant functional type classification based on leaf habit alone has little utility in the tropical dry forest we studied, and (ii) legumes have a different suite of traits including high leaf carbon and wood density in addition to high leaf nitrogen. Whether this result generalizes to other tropical forests is unknown, but merits future research due to the consequences of these traits for carbon storage and ecosystem processes.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".