Climatic and biogeographical drivers of functional diversity in the flora of the Canary Islands
Bibliographic record
Abstract
Abstract Aim Functional traits can help us to elucidate biogeographical and ecological processes driving assemblage structure. We analysed the functional diversity of plant species of different evolutionary origins across an island archipelago, along environmental gradients and across geological age, to assess functional aspects of island biogeographical theory. Location Canary Islands, Spain. Major taxa studied Spermatophytes. Time period Present day. Methods We collected data for four traits (plant height, leaf length, flower length and fruit length) associated with resource acquisition, competitive ability, reproduction and dispersal ability of 893 endemic, non‐endemic native and alien plant species ( c . 43% of the Canary Island flora) from the literature. Linking these traits to species occurrences and composition across a 500 m × 500 m grid, we calculated functional diversity for endemic, non‐endemic native and alien assemblages using multidimensional functional hypervolumes and related the resulting patterns to climatic (humidity) and island biogeographical (geographical isolation, topographic complexity and geological age) gradients. Results Trait space of endemic and non‐endemic native species overlapped considerably, and alien species added novel trait combinations, expanding the overall functional space of the Canary Islands. We found that functional diversity of endemic plant assemblages was highest in geographically isolated and humid grid cells. Functional diversity of non‐endemic native assemblages was highest in less isolated and humid grid cells. In contrast, functional diversity of alien assemblages was highest in arid ecosystems. Topographic complexity and geological age had only a subordinate effect on functional diversity across floristic groups. Main conclusions We found that endemic and non‐endemic native island species possess similar traits, whereas alien species tend to expand functional space in ecosystems where they have been introduced. The spatial distribution of the functional diversity of floristic groups is very distinct across environmental gradients, indicating that species assemblages of different evolutionary origins thrive functionally in dissimilar habitats.
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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 | 0.002 |
| 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 | 0.001 |
| 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".