Contrasting patterns of lichen functional diversity and species richness across an elevation gradient
Bibliographic record
Abstract
Major environmental gradients co‐vary with elevation and have been a longstanding natural tool allowing ecologists to study global diversity patterns at smaller scales, and to make predictions about the consequences of climate change. These analyses have traditionally studied taxonomic diversity, but new functional diversity approaches may provide a deeper understanding of the ecological mechanisms driving species assembly. We examined lichen taxonomic and functional diversity patterns on 195 plots (200 m²) together with forest structure along an elevational gradient of 1000 m in a temperate low mountain range (Bohemian Forest, Germany). Along this elevation gradient temperature decreased and precipitation increased, two macroclimatic variables critical for lichens. Elevation was more important than forest structure in driving taxonomic and functional diversity. While species richness increased with elevation, functional diversity decreased and revealed that community patterns shift with elevation from random to clustered, reflecting selection for key shared traits. Higher elevations favored species with a complex growth form (which takes advantage of high moisture) and asexual reproductive mode (facilitating establishment under low temperature conditions). Our analysis highlights the need to examine alternative forms of diversity and opens the avenue for community predictions about climate change. For a regional scenario with increasing temperature and decreasing availability of moisture, we expect a loss of specialized species with a complex growth form and those with vegetative organs at higher elevations in low mountain ranges in Europe.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".