How does hybridization affect multiple metrics of fitness in fragmented populations of brook trout under moderate climate warming
Bibliographic record
Abstract
As a population’s genetic makeup is often attributed to the combined fitness of its individuals, the adoption of deliberate hybridization practices is an area of interest for many hatchery and conservation programs. Using a common garden experimental design involving eight wild populations of Salvelinus fontinalis (brook trout), we studied how effective population size (Ne), divergence (QST, km), and environmental dissimilarity (pH, temperature) may influence hybridization outcomes for fitness related traits under moderate climate warming. Additionally, we looked at the ability of six of these populations to tolerate acute thermal warming, and whether or not this tolerance could be altered by hybridizing populations. Critical thermal maximum (CTmax) assays were conducted on juveniles from each population to assess thermal tolerance, and agitation temperature (a behavioural metric quantifying temperature at the onset of refugia-seeking behaviour) was recorded for assessing behavioural changes to elevated temperatures. Gametes were collected from different-sized, isolated populations of brook trout, and crossed in the lab. Fitness-related traits were compared between pure and F1 hybrid crosses via common garden experimental design. We had the unique opportunity to jointly investigate how these factors influence multiple metrics of hybrid fitness in wild, isolated, and varyingly-sized populations of a vertebrate species inhabiting a relatively undisturbed environment. Although population size and environmental dissimilarity were found to significantly affect hybrid fitness, these relationships were biologically weak. Although significant differences in CTmax were found between populations, this difference was at most 0.68 °C (29.11-29.79 °C), and no effect of hybridization was seen despite varying thermal regimes between these populations’ wild streams. These results will provide guidance to small population and captive-breeding conservation programs, as the lack of a strong relationship between hybridization and fitness encourages population-specific approach to genetic rescue projects. Additionally, this study highlights the level to which thermal tolerance is conserved between isolated populations of a vertebrate species, in the face of climate warming.
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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 | 0.001 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 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".