Role of genomics and transcriptomics in selection of reintroduction source populations
Bibliographic record
Abstract
The use and importance of reintroduction as a conservation tool to return a species to its historical range from which it has been extirpated will increase as climate change and human development accelerate habitat loss and population extinctions. Although the number of reintroduction attempts has increased rapidly over the past 2 decades, the success rate is generally low. As a result of population differences in fitness-related traits and divergent responses to environmental stresses, population performance upon reintroduction is highly variable, and it is generally agreed that selecting an appropriate source population is a critical component of a successful reintroduction. Conservation genomics is an emerging field that addresses long-standing challenges in conservation, and the potential for using novel molecular genetic approaches to inform and improve conservation efforts is high. Because the successful establishment and persistence of reintroduced populations is highly dependent on the functional genetic variation and environmental stress tolerance of the source population, we propose the application of conservation genomics and transcriptomics to guide reintroduction practices. Specifically, we propose using genome-wide functional loci to estimate genetic variation of source populations. This estimate can then be used to predict the potential for adaptation. We also propose using transcriptional profiling to measure the expression response of fitness-related genes to environmental stresses as a proxy for acclimation (tolerance) capacity. Appropriate application of conservation genomics and transcriptomics has the potential to dramatically enhance reintroduction success in a time of rapidly declining biodiversity and accelerating environmental change.
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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 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".