Stochastic Processes during Invasion: The Influence of Population Size on Style-Morph Frequency Variation in<i>Lythrum salicaria</i>(Purple Loosestrife)
Bibliographic record
Abstract
Premise of research. During biological invasion, the genetic diversity of populations may be reduced by founder events and genetic drift. The floral polymorphism tristyly provides an exceptional opportunity to investigate the influence of stochastic forces on the maintenance of genetic polymorphism, because small population size leads to a characteristic signature of morph loss from populations. Here, we investigate the relations between population size and morph-frequency variation in invasive populations of tristylous Lythrum salicaria in Ontario, Canada. We also compare our results to a similar survey conducted 25 years ago in the same region.Methodology. We surveyed the size and morph ratios of 114 L. salicaria populations in 2013. We calculated the relations between population size and style morph absence, population size and style morph evenness, and the number of populations lacking particular style morphs. For comparison of the patterns of morph-frequency variation between surveys, we used a sample of populations (1988/1989: 51 populations; 2013: 101 populations) with similar size distributions.Pivotal results. Our survey confirmed that smaller populations were more likely to lack a style morph than larger populations and that morph ratios were less even in smaller populations. In dimorphic populations, the short-styled morph (S-morph) was absent most often, and the long-styled morph (L-morph) was least often missing, a pattern consistent with the stochastic theory of asymmetrical morph loss for tristylous species. There were no significant differences between the 1988/1989 and 2013 surveys in the frequency of populations missing style morphs or the relations between population size and style morph evenness.Conclusions. Despite an increase in abundance of L. salicaria in Ontario during the past 25 years, genetic drift and founder events still play a dominant role in governing patterns of morph-frequency variation.
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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 | 0.001 | 0.002 |
| 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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".