Does Motility‐Restricting Fibrosis Influence Dispersal? An Experiment in Nature With Threespine Stickleback
Bibliographic record
Abstract
ABSTRACT Dispersal can affect individual‐level fitness and population‐level ecological and evolutionary processes. Factors that affect dispersal could therefore have important eco‐evolutionary implications. Here, we investigated the extent to which an inflammation and tissue repair response—peritoneal fibrosis—which is known to restrict movement, could influence dispersal by conducting a mark‐recapture experiment in a lake in Alaska with threespine stickleback (Gasterosteus aculatus). A subset of captured stickleback were injected with aluminium phosphate to experimentally induce fibrosis (‘treatment group’), and another subset were injected with saline or received no injection—both of which do not induce fibrosis (‘control group’). We released all fish at one introduction point and re‐sampled stickleback throughout the lake for 8 days. We recaptured 123 individuals (n = 47 fibrosis treatment; n = 76 control) and dissected them to determine fibrosis levels. Overall, fibrosis did not affect dispersal. Some compelling (but not statistically significant) trends suggest that early‐stage inflammation may affect dispersal, providing opportunities for future work. By showing that effects on dispersal are not important side effects of fibrosis, these findings improve our understanding of the ecological implications of immune responses.
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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 | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".