Inferring species abundance distribution across spatial scales
Bibliographic record
Abstract
A long‐standing problem in ecology is to understand how the species–abundance distribution (SAD) varies with sampling scale. The problem was first characterized by Preston as the veil line problem. Although theoretical and empirical studies have now shown the nonexistence of the veil line, this problem has generated much interest in scaling biodiversity patterns. However, research on scaling SAD has so far exclusively focused on the relationship between the SAD in a smaller sampling area and a known SAD assumed for a larger area. An unsolved challenge is how one may predict species–abundance distribution in a large area from that of a smaller area. Although upscaling biodiversity patterns (e.g. species–area curve) is a major focus of macroecological research, upscaling of SAD across scale is, with few exceptions, ignored in the literature. Methods that directly predict SAD in a larger area from that of a smaller area have just started being developed. Here we propose a Bayesian method that directly answers this question. Examples using empirical data from tropical forests of Malaysia and Panama are employed to demonstrate the use of the method and to examine its performance with increasing sampling area. The results indicate that only 10‐15% of the total census area is needed to adequately predict species abundance distribution of a region. In addition to species abundance distributions, the method also predicts well the regional species richness.
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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 | 0.008 |
| 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 | 0.001 |
| Open science | 0.001 | 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".