The genetics of assisted gene flow: immediate costs and long-term benefits
Bibliographic record
Abstract
ABSTRACT Plant and animal populations are facing several novel risks such as human-mediated habitat fragmentation and climate change that threaten their long-term productivity and persistence. With the genetic health of many populations deteriorating due to climate change outpacing physiological adaptation, human interventions in the form of assisted gene flow (AGF) may provide genetic variation to adapt populations to predicted climate change scenarios and result in more robust and productive populations. We ran genetic simulations to mimic a variety of AGF scenarios and measured their outcomes on population-level fitness to answer the question: in which circumstances is it worthwhile to perform AGF? Based on the parameters we explored, AGF may be harmful in certain situations over the short term (e.g., the first ∼10-20 generations), due to outbreeding depression and introducing deleterious genetic variation. Moreover, under many parameter sets, the benefits of AGF were relatively weak or took many generations to accrue. In general, when the adaptive trait is controlled by many loci of small effect, the benefits of assisted gene flow take much longer to realize–potentially too long for most climate-related management decisions. We also show that when translocation effort is divided across several generations and outbreeding depression is strong, the recipient population experiences a smaller decrease in fitness as compared to moving all individuals in a single effort. Importantly, in most cases, we show that the genomic integrity of the recipient population remains relatively intact following AGF; the amount of genetic material from the donor population typically ends up constituting no more of the recipient population’s genome than the fraction introduced. Our results will be useful for conservation practitioners and silviculturists, for instance, aiming to intervene and adaptively manage so that populations maintain a robust genetic health and maintain productivity into the future given anthropogenic climate change.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".