Species communities can accurately predict the occurrence of an imperilled fish
Bibliographic record
Abstract
Species distribution information is essential for conservation. However, sampling the full range of a species’ potential distribution is rarely feasible, necessitating the development of models to predict distributions, as well as relevant environmental and biotic drivers. We applied a novel approach to model the distribution of a species at risk in Canada, silver shiner (SS; Notropis photogenis) in tributaries of Lake Ontario using the fish community as a predictor of SS occurrence. Associative rule learning (ARL) identified simple species combinations that provided strong insight into SS distribution, which may be particularly useful for identifying new occupied locations, including making sampling decisions in real time. The species with the most positive or negative associations with SS identified by ARL were included in a random forests model, which predicted SS distribution with high accuracy in test data from the study tributary system and in a neighbouring system where SS is exceedingly rare. Predicting species distributions based on biotic associations presents opportunities for discovering new populations, identifying critical habitat, and evaluating the suitability of sites for re-introduction potential.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".