A simple method for estimating species abundance from occurrence maps
Bibliographic record
Abstract
Summary The issue of how to estimate species abundance from presence/absence maps has attracted much attention. Several methods have been developed to address this problem. However, those methods either overlook the structure of spatial autocorrelation of species distribution, thus leading to underestimation, or they demand extra information besides presence/absence maps. This study first developed a new method that takes account of spatial autocorrelation and only requires occurrence maps, without any extra information. This method was further improved by incorporating a correction factor to it. We used an index defined by joint counts of occupied and unoccupied cells to measure spatial autocorrelation and to correct the underestimation of the random placement model. The performance of our method was compared against four other major methods (random placement model, negative binomial model, Conlisk et al.'s method and Solow & Smith's method) using both simulated and empirical data. The results showed that the performance of our method is comparable with other methods but requires less and readily obtained input data, a property important for real applications. We suggest this simple, data‐parsimonious method be a useful alternative to the currently available methods for estimating abundance from occurrence.
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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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".