The potential of seedbank digital information in plant conservation
Bibliographic record
Abstract
Societal Impact Statement Seedbanks are vital for biodiversity conservation, but their potential remains underutilised due to a limited understanding of the intraspecific genetic diversity they hold. By leveraging digitised data associated with seedbank collections, such as sampling locations, number of maternal plants and seed traits, we can attempt the estimation of genetic variation and identify gaps in collections, enabling better prioritisation of species for conservation efforts. These advancements can inform policy targets like those of the Kunming‐Montreal Global Biodiversity Framework, promoting more effective conservation strategies. Digitisation and emerging machine‐learning technologies offer scalable, cost‐efficient solutions to enhance conservation knowledge, ensuring biodiversity resilience for future generations. Summary Seedbank collections hold significant untapped potential for advancing conservation science and practice, but the intraspecific genetic diversity (i.e. diversity within a species) stored in worldwide seedbank collections remains largely unknown, hindering the effective use of seeds for both informing and implementing in situ interventions. As producing genetic data is time‐consuming and expensive, other data associated with seedbank collections can greatly enhance our understanding of the genetic variation stored in seed collections when genetic data are unavailable. Information such as the location of sampling sites, estimated population size and the number of mother plants from which seeds were collected can facilitate the estimation of the genetic diversity captured in the collections. This information can also be used to estimate the sampling effort required to fill gaps in seedbank collections to better represent genetic diversity, through comparison with existing baselines from species where genetic diversity is characterised, and through simulations. Digitisation of the data associated with seedbank collections makes the approaches above practicable at scale. In addition, digital images of the seeds themselves may identify intraspecific phenotypic variation and can, therefore, be used to prioritise populations for future genetic studies. In this article, we explore the potential of digitised information made available by seedbanks for improving our understanding of the intraspecific genetic diversity preserved in collections. We describe possible improvements that might enhance the predictive power of digital information for genetic studies, and discuss the challenges and opportunities associated with these.
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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 | 0.008 | 0.040 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.006 | 0.008 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.007 | 0.014 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.020 | 0.004 |
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".