Global fern and lycophyte richness explained: How regional and local factors shape plot richness
Bibliographic record
Abstract
Abstract Aim To disentangle the influence of environmental factors at different spatial grains (regional and local) on fern and lycophyte species richness and to ask how regional and plot‐level richness are related to each other. Location Global. Taxon Ferns and lycophytes. Methods We explored fern and lycophyte species richness at two spatial grains, regional (hexagonal grid cells of 7,666 km 2 ) and plot level (300–500 m 2 ), in relation to environmental data at regional and local grains (the 7,666 km 2 hexagonal grid cells and 4 km 2 square grid cells, respectively). For the regional grain, we obtained species richness data for 1,243 spatial units and used them together with climatic and topographical predictors to model global fern richness. For the plot‐level grain, we collated a global dataset of nearly 83,000 vegetation plots with a surface area in the range 300–500 m 2 in which all fern and lycophyte species had been counted. We used structural equation modelling to identify which regional and local factors have the biggest effect on plot‐level fern and lycophyte species richness worldwide. We investigate how plot‐level richness is related to modelled regional richness at the plot's location. Results Plot‐level fern and lycophyte species richness were best explained by models allowing a link between regional environment and plot‐level richness. A link between regional richness and plot‐level richness was essential, as models without it were rejected, while models without the regional environment‐plot‐level richness link were still valid but had a worse goodness‐of‐fit value. Plot‐level richness showed a hump‐shaped relationship with regional richness. Main conclusions Regional environment and regional fern and lycophyte species richness each are important determinants of plot‐level richness, and the inclusion of one does not substitute the inclusion of the other. Plot‐level richness increases with regional richness until a saturation point is reached, after which plot‐level richness decreases despite increasing regional richness, possibly reflecting species interactions.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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 teacher head, 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".