Multi-generation selective landscapes and sub-lethal injuries in stickleback
Bibliographic record
Abstract
The interaction between predation landscape and phenotypic variability within prey populations is of substantial significance in evolutionary biology. Extending from several decades of studies at a remote freshwater lake on Haida Gwaii, western Canada, we analyze the incidence of predator-induced sub-lethal injuries in 8,069 wild-captured threespine stickleback (Gasterosteus aculeatus) and using cohort analyses test whether the distribution of injuries informs the selective landscape influencing the bell-shaped frequency distribution of the traits. Our results indicate that (1) the incidence of injuries varies among phenotypes differing in the number and position of lateral plates, (2) these differences occur only among younger fish, (3) the incidence of injuries is inversely related to the estimated population frequencies of plate phenotypes, with the modal phenotype generally having the fewest injuries, (4) direct estimates of selective differentials and relative fitness based on analyses of 1,735 fish from 6 independent yearly cohorts indicates statistically informative elevated differentials in phenotypes with greater number of plates and elevated relative fitness of non-modal phenotypes, and (5) there are significant differences among yearly cohorts in strength and direction of selection, and an increased prevalence of diversifying versus stabilizing selection despite longer-term stasis (4 decades) in trait means. We conclude that the presence of multiple “optimal” phenotypes complements the renewed interest in quantifying short-term temporal or spatial variation in ecological processes in studies of fitness landscapes and intrapopulation variability.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.009 |
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".