Genetic diversity targets and indicators in the CBD post-2020 Global Biodiversity Framework must be improved
Bibliographic record
Abstract
The 196 parties to the Convention on Biological Diversity (CBD) will soon agree to a post-2020 global framework for conserving the three elements of biodiversity (genetic, species, and ecosystem diversity) while ensuring sustainable development and benefit sharing. As the most significant global conservation policy mechanism, the new CBD framework has far-reaching consequences- it will guide conservation actions and reporting for each member country until 2050. In previous CBD strategies, as well as other major conservation policy mechanisms, targets and indicators for genetic diversity (variation at the DNA level within species, which facilitates species adaptation and ecosystem function) were undeveloped and focused on species of agricultural relevance. We assert that, to meet global conservation goals, genetic diversity within all species, not just domesticated species and their wild relatives, must be conserved and monitored using appropriate metrics. Building on suggestions in a recent Letter in Science (Laikre et al., 2020) we expand argumentation for three new, pragmatic genetic indicators and modifications to two current indicators for maintaining genetic diversity and adaptive capacity of all species, and provide guidance on their practical use. The indicators are: 1) the number of populations with effective population size above versus below 500, 2) the proportion of populations maintained within species, 3) the number of species and populations in which genetic diversity is monitored using DNA-based methods. We also present and discuss Goals and Action Targets for post-2020 biodiversity conservation which are connected to these indicators and underlying data. These pragmatic indicators and goals have utility beyond the CBD; they should benefit conservation and monitoring of genetic diversity via national and global policy for decades to come.
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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 | 0.043 | 0.056 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.002 | 0.006 |
| Scholarly communication | 0.008 | 0.008 |
| Open science | 0.004 | 0.006 |
| Research integrity | 0.009 | 0.011 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".