Multiple drivers of plant diversity in forest ecosystems
Bibliographic record
Abstract
Abstract Aim Theoretical and empirical studies have shown that climate is a major determinant of species richness, yet the importance of climate relative to local site conditions remains unclear in forest ecosystems across large regions. Furthermore, little is known about how climate and local site conditions affect species evenness and understorey plant diversity. Location Canadian forest (42°37′ to 68°14′ N; 53°25′ to 134°46′ W; 4 to 2170 m elevation). Methods Using national, broad‐scale forest inventory data, we tested the influence of growing degree days (GDD), climate moisture index (CMI), soil drainage class (SDC) and stand age (SA) on plant diversity across Canada's forests. Diversity was assessed on variable plot sizes from 125 to 500 m2 for canopy tree and total plant species, and from 100 to 400 m2 for understorey plants. Results Our models accounted for 47, 30 and 49% of total variation in canopy tree species richness, Simpson's dominance index and total plant species richness, respectively. After accounting for the effects of plot size and management history, canopy richness increased with GDD and CMI and peaked at intermediate SDC and SA. Simpson's dominance index decreased with increasing GDD and CMI and was lowest at intermediate SDC and SA. Total species richness was most strongly related to SDC and was highest on imperfectly drained soils. Climatic controls on species richness of understorey strata were generally weaker than those on canopy richness. Main conclusions Our results for canopy richness along a mainly longitudinal gradient demonstrate the positive effects of energy on tree species diversity and show evidence for the effects of local site conditions and secondary succession on diversity. Furthermore, our results show that the extent of the influence of climate and local site conditions on plant species diversity can strongly differ between vegetation strata.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".