Bibliographic record
Abstract
Accurate description of spatial distribution of species is essential for correctly modeling macroecological patterns and thus to infer mechanisms of species coexistence. The Poisson and negative binomial distribution (NBD) are most widely used to respectively model random and aggregated distributions of species in infinitely large areas. As a finite version of the Poisson distribution, the binomial distribution is used to model random distribution of species populations in finite areas. Despite that spatial aggregation is the most widespread pattern and no species in nature are distributed in infinitely large areas, no model is currently available to describe spatial aggregation for species distributed in finite areas. Here we develop a finite counterpart of the NBD to model aggregated species in finite landscapes. Similar to the NBD, this new model also has a parameter k measuring spatial aggregation. When k --> infinity, this model becomes the binomial distribution; when study area approaches infinite, it becomes the NBD. This model was extensively evaluated against the distributions of over 300 tree species in a 50-ha stem-mapping plot from Barro Colorado Island, Panama. The results show that when sampling area is small (relative to the study area), the new model and the NBD are of little difference. But the former correctly models spatial distribution at the finite limit at which the NBD fails. We reveal serious theoretical pathologies by using infinite models to approximate finite distribution and show the theoretical and practical advantages for using the new finite model for modeling species-area relationships, species occupancy and spatial distribution of rare species.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".