Functional regeneration and spectral reflectance of trees during succession in a highly diverse tropical dry forest ecosystem
Bibliographic record
Abstract
PREMISE OF THE STUDY: The function of most ecosystems has been altered by human activities. To asses the recovery of plant communities, we must evaluate the recovery of plant functional traits. The seasonally dry tropical forest (SDTF), a highly threatened ecosystem, is assumed to recover relatively quickly from disturbance, but an integrated evaluation of recovery in floristic, structural, and functional terms has not been performed. In this study we aimed to (a) compare SDTF plant functional, floristic, and structural change along succession; (b) identify tree functional groups; and (c) explore the spectral properties of different successional stages. METHODS: Across a SDTF successional gradient, we evaluated the change of species composition, vegetation structure, and leaf spectral reflectance and functional traits (related to water use, light acquisition, nutrient conservation, and CO(2) acquisition) of 25 abundant tree species. KEY RESULTS: A complete recovery of SDTF takes longer than the time period inferred from floristic or structural data. Plant functional traits changed along succession from those that maximize photoprotection and heat dissipation in early succession, where temperature is an environmental constraint, to those that enhance light acquisition in late succession, where light may be limiting. A spectral indicator of plant photosynthetic performance (photochemical reflectance index) discriminated between early and late succession. This constitutes a foundation for further exploration of remote sensing technologies for studying tropical succession. CONCLUSIONS: A functional approach should be incorporated as a regular descriptor of forest succession because it provides a richer understanding of vegetation dynamics than is offered by either the floristic or structural approach alone.
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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 | 0.000 |
| 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".