The IUCN Red List is not sufficient to protect genetic diversity
Bibliographic record
Abstract
The International Union for Conservation of Nature (IUCN) Red List is an important and widely used conservation prioritization tool. It uses information about species range size, habitat quality and fragmentation levels, and trends in abundance to assess species extinction risk. Genetic erosion is an additional key factor determining extinction risk, but the Red List was not designed to assess genetic diversity. Declining populations experience stronger effects of genetic drift and higher rates of inbreeding, which can reduce the efficiency of selection, lead to fitness declines, and hinder species’ capacities to adapt to environmental change. Given the importance of conserving genetic diversity, several studies have attempted to find relationships between Red List status and genetic diversity. Yet, there is still no general consensus on whether genetic diversity is captured by the current Red List categories in a way that is informative for conservation, likely partly due to assessments using different molecular markers and taxa. Here, we synthesize previous work and re-analyze three datasets using different marker types (mitochondrial DNA, microsatellites, and whole genomes) to assess whether genetic diversity accurately predicts Red List threat status. Consistent with previous work we found that on average, species with higher threat status tended to have lower genetic diversity for all marker types, but the strength of these relationships varied across taxa. However, genetic diversity did not predict threat status well for any taxon or marker type. Our analyses indicate that Red List status is not a useful metric for informing species-specific decisions about the protection of genetic diversity. This is unsurprising because the Red List was not designed for conservation at the genetic level. Our findings clearly indicate a need to develop and incorporate metrics specifically developed to assess genetic diversity into our conservation policy frameworks.
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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 | 0.010 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 0.003 |
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".