Benefits of increased colonist quantity and genetic diversity for colonization depend on colonist identity
Bibliographic record
Abstract
Larger numbers of colonists can be more likely to establish and spread due to the benefits provided by either more individuals (quantity) or a greater diversity of genotypes or phenotypes (genetic diversity). However, the value of higher colonist quantity or genetic diversity varies widely across studies, leaving a great deal of uncertainty in how these respective mechanisms affect colonization success. This variability is potentially driven by differences in which traits are present in respective colonist pools (‘colonist identity’). Studies with high‐performing colonizers (e.g. genotypes pre‐adapted to the colonizing environment) may find increasing quantity or diversity to be beneficial because it increases the chance high‐performers are sampled, while studies with no high‐performers may find no effects of quantity or diversity. Alternatively, quantity and genetic diversity may play little to no role if the smallest populations already contain high‐performing colonists because there is no scope for a sampling effect to operate. We conducted a field mesocosm experiment to determine if variability in the benefits provided by increased quantity or genetic diversity relates to colonist traits. Nine distinct genotypes of Daphnia pulex characterized also by phenotype, were introduced in ‘single’ (one individual) or ‘many’ (nine individuals) introduction quantities and at ‘low’ (monoclonal) and ‘high’ (mixed genotypes) genetic diversities. We found that larger‐bodied D. pulex genotypes benefited less from increased colonist quantity, while increasing genetic diversity tended to have a lower effect on higher growth rate genotypes. Our results show that the trait values of the colonists can determine the benefits gained when colonist quantity or genetic diversity are increased, with potential applications to future research and practical efforts to promote, or prevent, population establishment.
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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".