Relative costs of conserving threatened species across taxonomic groups
Bibliographic record
Abstract
Bias toward legally protecting and prioritizing charismatic taxonomic groups, such as mammals and birds, and against others, such as insects and plants, is well documented. However, the relative costs of conserving various taxonomic groups and the potential of these costs to interact with existing biases have been much less explored. We analyzed conservation programs across more than 2,000 species in 3 countries to investigate the costs of conserving species within taxonomic groups and how these costs might affect conservation planning. For each data set, we tested for differences in mean annual cost among taxonomic groups. For the data set from the United States, recovery plans differed in duration, so we also tested for differences in total costs among taxonomic groups. Although the costs for individual species varied widely, there were strong international consistencies. For example, mammals cost 8-26 times more on average to conserve than plants and 13-19 times more to conserve than aquatic invertebrates. On average, bird species cost 5-30 times more to conserve than plants and 6-14 times more to conserve than aquatic invertebrates. These cost differences could exacerbate unequal resource allocation among taxonomic groups such that more charismatic groups both receive more attention and require more resources, leading to neglect of other taxonomic groups.
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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 | 0.002 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| 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 | 0.000 |
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".