Empirical test of increasing genetic variation via inter‐population crossing for native plant restoration in variable environments
Bibliographic record
Abstract
Restoring native plant populations is an essential component of conserving biodiversity, ecological function, and ecosystem services. Restoration using local, ecotypic source materials is largely acknowledged as best practice; however, local populations are not always available or adapted to current or future site conditions. A major challenge in restoration comes from increasingly variable and unpredictable environmental conditions that impose selective pressures and threaten restoration success. Understanding how to conserve and restore populations under changing environments requires attention to within‐species genetic diversity, which can exert a number of population‐level effects. However, given that these effects can be positive and negative, it remains unclear how increasing genetic diversity via mixing distinct populations may ultimately affect restoration in variable environments. To empirically investigate these effects, we established lineages of a native forb, Helianthus petiolaris (prairie sunflower), with higher and lower individual‐ and population‐level genetic diversity by crossing plants using seed from four distinct locations. We planted and tracked a total of 3,200 individual seeds across all lineages in replicated plots in two common gardens representing a range of environmental conditions, and measured fitness components throughout the growing season. We found that populations with increased genetic diversity had intermediate emergence and reproduction, improved survival in the poorest quality plots, and were moderately buffered against environmental variability. Overall, higher diversity led to high or intermediate and stable performance across environments. Our findings support a strategy of increasing genetic diversity when restoring populations, in particular when a lack of information hampers selection of an optimal source population.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| 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".