Modeling distribution and abundance of multiple species: Different pooling strategies produce similar results
Bibliographic record
Abstract
Quantifications of spatial distribution and abundance of animals are essential to identifying key landscape characteristics and targeting locations for conservation action. Since conservation decisions often focus on multiple species aggregated in groups, e.g., guild‐level, rather than individual species, predictions of species group abundance are of central importance. However, areas chosen for conservation action may differ if results from various modeling strategies also differ. Therefore, we compared three different strategies for modeling species group distribution and abundance:predict first, assemble later(PA);assemble first, predict later(AP); and the combinedassemble, predict, then assemble(APA). All strategies were performed using Boosted Regression Trees (BRTs), which were fit to individual species data and then grouped after modeling, or fit to datasets that were grouped before modeling. Modeling strategies produced very similar results in terms of statistical performance assessed through four evaluation metrics and in spatial patterns in predicted abundance. To further assess potential functional implications of any numeric differences to conservation planning, we examined the relative proportion of the predicted population within existing Canadian protected areas. This metric further confirmed similarity in predictions from the three modeling strategies. Our results suggest that locations targeted for conservation action would be highly consistent among modeling strategies. Slight differences we observed in spatial predictions may be due to data coverage across species ranges, data quality, and the flexibility of the BRTs.
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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 | 0.014 | 0.030 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.002 |
| 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".