Supporting Data for: Fish Show Genetic Evolutionary Responses to River Regulation
Bibliographic record
Abstract
Eco-hydraulics traditionally aims at managing riverine systems in a semi-natural state while meeting human demands, assuming aquatic species are evolutionarily static. However, evidence of rapid evolution suggests that ignoring evolutionary dynamics of fish species might limit long-term effectiveness of eco-hydraulics frameworks. It remains unclear how freshwater fish adapt to human perturbation. Why are some fish populations more resilient to human perturbation than others? What are the genetic mechanisms behind it? To answer these questions, we genotyped eleven populations of three-spined stickleback in a regulated river system and collected data on river morphology, connectivity, flow regimes, physico-chemistry and parasite abundance through a combination of field surveys and modelling. Gene-environment association analysis detected strong signals of genetic divergence associated with hydraulic features. Gene ontology analysis revealed evolutionary responses that primarily involve functions in the nervous and sensory systems. These findings demonstrate that fish can evolve in response to river regulation, highlighting the need to transition from eco-hydraulics toward eco-evo-hydraulics. Our results can currently be accessed through a non-peer-reviewed bioRxiv preprint at Cai and Deflem et al. (2025), Fish Show Genetic Evolutionary Responses to River Regulation (DOI: 10.1101/2025.08.01.668107). This supporting dataset includes Genotyping-by-Sequencing data from 14 populations of three-spined stickleback collected in 2017 from the Demer Basin, Belgium, together with parasite and habitat data from the paired sampling sites. After quality filtering, 11 populations were retained and analyzed in Cai and Deflem et al. (2025).
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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 | 0.004 | 0.028 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.004 | 0.002 |
| Bibliometrics | 0.007 | 0.018 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.007 | 0.006 |
| Research integrity | 0.008 | 0.003 |
| Insufficient payload (model declined to judge) | 0.639 | 0.298 |
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".