Improved abundance prediction from presence–absence data
Bibliographic record
Abstract
ABSTRACT Aim Many ecological surveys record only the presence or absence of species in the cells of a rectangular grid. Ecologists have investigated methods for using these data to predict the total abundance of a species from the number of grid cells in which the species is present. Our aim is to improve such predictions by taking account of the spatial pattern of occupied cells, in addition to the number of occupied cells. Innovation We extend existing prediction models to include a spatial clustering variable. The extended models can be viewed as combining two macroecological regularities, the abundance–occupancy regularity and a spatial clustering regularity. The models are estimated using data from five tropical forest censuses, including three Panamanian censuses (4, 6 and 50 ha), one Costa Rican census (16 ha) and one Puerto Rican census (16 ha). A serpentine grassland census (8 × 8 m) from northern California is also studied. Main conclusions Taking account of the spatial clustering of occupied cells improves abundance prediction from presence–absence data, reducing the mean square error of log‐predictions by roughly 54% relative to a benchmark Poisson predictor and by roughly 34% relative to current prediction methods. The results have high statistical significance.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.007 | 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 teacher head, 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".