The Evolution of Conservation Biobanking: A Literature Review and Analysis of Terminology, Taxa, Location, and Strategy of Wildlife Biobanks Over Time
Bibliographic record
Abstract
Nearly one-third of flora, fauna, and funga species on Earth are threatened with extinction. In response, the prevalence of repositories-often called "biobanks" or "genome resource banks"-for storing biological materials from threatened species has become more widespread. This research examined trends for the (1) terminology, (2) taxa representation, (3) global distribution, and (4) operational approach of biobanks versus genome resource banks relating to zoos and wildlife. Our literature search results indicate that although genome resource banking literature began earlier in the 1990s, biobanking has seen a surge in publications with over 3.5× more literature for biobanking since 2020. Genome resource bank articles were highly focused on mammals (68%), while biobanking literature focused more on multi-taxonomic overviews and less-studied taxa. Our search parameters found the largest number of wildlife biobanks in Europe (18) and the lowest number in South America (2), though results are likely impacted by the search being completed in English. Additionally, only 28% (7/25) of global biodiversity hotspots contain a wildlife biobank based on our methodology. While not all wildlife biobanking efforts are published or reported, these findings suggest that (1) "biobank" will likely be the more widely used term in the future, (2) more biobanking research is needed for non-mammalian taxa, (3) there are geographical gaps in wildlife biobanks, and (4) conservation biobanking programs should focus on storing biospecimens from a wide set of individuals and develop assisted reproductive technologies concomitantly with the goal of maintaining healthy, sustainable populations in the long term.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".