Multinational evaluation of genetic diversity indicators for the Kunming-Montreal Global Biodiversity Monitoring framework
Bibliographic record
Abstract
In December 2022, the United Nations Convention on Biological Diversity (CBD) adopted the Kunming-Montreal Global Biodiversity Framework, in which 196 Parties, for the first time, committed to report on the status of genetic diversity for all species. To facilitate this reporting, three genetic diversity indicators were developed, two of which focus on the processes contributing to genetic diversity loss: the loss of genetically distinct populations (measured by complementary indicator “proportion of populations maintained within species”) and populations being too small to maintain genetic diversity (measured by headline indicator A4, “The proportion of populations within species with an effective population size > 500”). The major advantage of these indicators is that they can be estimated without DNA-based data. However, demonstrating the feasibility of this approach to all Parties for their national reporting, requires addressing methodological challenges of using empirical data gathered from diverse sources, across diverse taxonomic groups and for countries of varying socio-economic status and biodiversity levels. Here, we assess the genetic indicators for 919 taxa, representing 5,271 populations across nine countries, including megadiverse and developing economies. Data were available to calculate indicators for each country and taxonomic group (765 taxa [83%] had data for at least one indicator). Additionally, 41% of taxa (n=518) have lost at least one-tenth of their populations (complementary indicator [populations maintained] value < 0.9), while 58% of taxa (n=568) have all populations too small to sustain genetic diversity (headline indicator [Ne 500] value = 0). By comparing taxon indicator values to their GlobalRed List status, range size, and other factors, we found the loss of genetic diversity shown by these indicators would go unnoticed by other biodiversity assessments, highlighting the critical importance of monitoring and conserving genetic diversity using these indicators.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.127 | 0.144 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.008 | 0.012 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.005 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| 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".