The importance of grain and cut-off size in shaping tree beta diversity along an elevational gradient in the northwest of Colombia
Bibliographic record
Abstract
Abstract Background Species turnover (β-diversity) along elevational gradients is one of the most important concepts in plant ecology. However, there is a lack of consensus about the main driving mechanisms of tree β-diversity at local scales in very diverse ecosystems (e.g., Andean mountains), as well as how the sampling effect can alter β-diversity estimations. Recently, it has been hypothesized that patterns of change in β-diversity at local scales along elevational gradients are driven by sampling effects stemming from differences in the size of the species pool rather than by underlying community assembly mechanisms. Thus, we aim to evaluate the relative extent to which sampling effects, such as species pool size, grain size, and tree size cut-off, determine species sorting, and thus, the variability of β-diversity at local scales along elevational gradients in the northwest of Colombia. Results Using 15 1-ha permanent plots spread out along a 3000 m elevational gradient, we used standardized β-deviation to assess the extent to which either sampling effects or the community assembly mechanisms determine the changes in species composition at local scales. Standardized β-deviation was measured as the difference between the observed and null β-diversity divided by the standard deviation of the null β-diversity. We found that the magnitude of change in local β-deviation along the elevational gradient was significant and dependent on the employed spatial grain size and tree size cut-off. However, β-deviation increased with elevation in all sampling designs, which suggests that underlying community assembly mechanisms play a key role in shaping local β-diversity along the elevational gradient. Conclusions Our findings suggest that grain size enlargement and the inclusion of trees with small diameters will improve our ability to quantify the extent to which the community assembly mechanisms shape patterns of β-diversity along elevational gradients. Overall, we emphasize the scale-dependent nature of the assessment of β-diversity. Likewise, we call for the need of a new generation of enlarged forest inventory plots along gradients of elevation in tropical forests that include small individuals to improve our understanding about the likely response of diversity and function to global change.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".