Temporal shifts in the functional composition of Andean forests at different elevations are driven by climate change
Bibliographic record
Abstract
Abstract Aim Andean forests are a global biodiversity hotspot. They harbour many species living within narrow climate ranges and a high functional diversity of trees. It remains still unclear how such hotspots respond to climatic changes over time. We investigated whether Andean forests are changing their functional composition over time along an elevational gradient by assessing changes in species composition, abundance and functional traits. Location An elevational gradient in Colombia's northern Andes. Time Period Species composition changes were studied two to four times from 2006 to 2017, and functional composition from 2016 to 2017. Major Taxa Studied A total of 1104 tropical tree species with in situ traits characterization. Methods We used seven morphological leaf traits and wood density values to analyse the functional trait dynamic over 10 years along an elevational gradient. By analysing changes in species composition, abundance and trait representation, we inferred the magnitude and direction of changes in functional composition. Then, we assessed if the functional change was related to climate change and demography. Results With increased minimum temperature and vapour‐pressure deficit, we found a decrease over time in mean values for leaf area and specific leaf area and increases in leaf thickness and leaf dry matter content. Long‐term temperature increases are smaller with increasing elevation, but the magnitude of trait changes is greater than in lowlands. Main Conclusions The functional composition is changing towards more conservative strategies over time across the elevation gradient, with the strongest changes observed at the highest elevations. This pattern is explained by the change in species turnover within communities due to higher recruitment rates of species with high leaf dry matter content values and low leaf area values. These shifts may be related to communities' responses to higher evapotranspiration demand and thermal stress, mainly at higher elevations.
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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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".