Evaluating policy-relevant surrogate taxa for biodiversity conservation: a case study from British Columbia, Canada
Bibliographic record
Abstract
Conservation efforts often lead to a small proportion of species receiving a disproportionate amount of attention. This bias in funding may help or hinder broader goals of biodiversity conservation depending on the surrogacy value of the well-funded species. Surrogate species are selected to represent other taxa in a shared environment when it would be costly or impractical to obtain information on individual taxa. We compared the surrogacy value of common groups of taxa implicated in conservation — game species, carnivores, non-game species, and other species. Using a publicly available data set of species–habitat associations, we compared the surrogacy value for 1012 species and 64 habitat types in British Columbia, Canada. We used a conditional entropy metric to quantify pairwise associations between species via their occurrence in different habitat types. Our analysis reveals that game and non-game species surrogacy groups do not significantly differ in either the frequency of captured pairwise associations or their coverage of species. These results suggest that funding game species conservation is likely conferring some benefits to non-game species, but optimal habitat-based conservation outcomes will come from a combination of taxa. This analysis provides an important step in influencing management decisions for the preservation of biodiversity in British Columbia.
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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 | 0.003 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.006 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".