Data from: Contemporary evolution of an at-risk stickleback population during a severe drought
Bibliographic record
Abstract
Populations can be granted conservation status because they harbour a set of unique traits, evolutionary histories, or ecological roles. Such populations are often isolated and specialized and, as such, can be particularly vulnerable to environmental disturbances. Even if distinct populations survive and adapt to severe disturbances, they could show changes in the very traits that made them distinct in the first place. Here, we leverage a natural “experiment” involving an unarmoured population of threespine stickleback (Gasterosteus aculeatus) in Rouge Lake (Haida Gwaii, BC) – a population listed as Special Concern under the Canadian Species at Risk Act. In 2015, Rouge Lake nearly dried up during a severe drought event; yet the stickleback population appeared to have fully recovered its abundance in subsequent years. Using phenotypic measurements, we assessed the extent to which evolution in this population was impacted by the drought. We document important shifts in several phenotypic traits, with the largest occurring in precisely the trait that made the population distinct and prompted its original conservation designation. Specifically, fish with no lateral plates (i.e., “unarmoured”) made up 51 % of the population before the drought, but only 13 % after the drought. This shift held (13-16 % unarmoured) over the four years of our post-drought monitoring. Field observations support a strong demographic bottleneck, which we suggest might have been coupled with a shift in the selective regime. These findings underscore how populations of conservation concern are not only at risk of extinction; they are also at risk of losing the characteristics that make them unique. These dynamics highlight the need for policies to consider a population’s evolutionary potential and develop more flexible approaches than simply considering single-timepoint assessments of diversity.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 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) | 0.003 | 0.001 |
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".