Resistance and resilience of genetic and phenotypic diversity to “black swan” flood events: A retrospective analysis with historical samples of guppies
Bibliographic record
Abstract
Rare extreme "black swan" disturbances can impact ecosystems in many ways, such as destroying habitats, depleting resources, and causing high mortality. In rivers, for instance, exceptional floods that occur infrequently (e.g., so-called "50-year floods") can strongly impact the abundance of fishes and other aquatic organisms. Beyond such ecological effects, these floods could also impact intraspecific diversity by elevating genetic drift or dispersal and by imposing strong selection, which could then influence the population's ability to recover from disturbance. And yet, natural systems might be resistant (show little change) or resilient (show rapid recovery) even to rare extreme events - perhaps as a result of selection due to past events. We considered these possibilities in two rivers where native guppies experienced two extreme floods - one in 2005 and another in 2016. For each river, we selected four sites and used archived "historical" samples to compare levels of genetic and phenotypic diversity before vs. after floods. Genetic diversity was represented by 33 neutral microsatellite markers, and phenotypic diversity was represented by body length and male melanic (black) colour. We found that genetic diversity and population structure was mostly "resistant" to even these extreme floods; whereas the larger impacts on phenotypic diversity were short-lived, suggesting additional "resilience". We discuss the determinants of these two outcomes for guppies facing floods, and then consider the general implications for the resistance and resilience of intraspecific variation to black swan disturbances.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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 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".