Insights from a century of data reveal global trends in ex situ living plant collections
Bibliographic record
Abstract
Ex situ living plant collections play a crucial role in providing nature-based solutions to twenty-first century global challenges. However, the complex dynamics of these artificial ecosystems are poorly quantified and understood, affecting biodiversity storage, conservation and utilization. To evaluate the management of ex situ plant diversity, we analysed a century of data comprising 2.2 million records, from a meta-collection currently holding ~500,000 accessions and 41% of global ex situ species diversity. Our study provides critical insights into the historical evolution, current state and future trajectory of global living collections. We reveal sigmoidal growth of a meta-collection that has reached capacity in both total accessions and total diversity, and identify intrinsic constraints on biodiversity management, including a median survival probability of 15 years. We explore the impact of external constraints and quantify the influence of the Convention on Biological Diversity, which we link to reduced acquisition of wild-origin and internationally sourced material by 44% and 38%, respectively. We further define the impact of these constraints on ex situ conservation but highlight targeted initiatives that successfully mitigate these challenges. Ultimately, our study underscores the urgent need for strategic prioritization and the re-evaluation of ex situ biodiversity management to achieve both scientific and conservation goals.
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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 | 0.004 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".