Experimental test of hybrid fitness change as a cause of species collapse following species invasion
Bibliographic record
Abstract
Recent years have seen increased incidences of hybridization between previously stable sympatric species, sometimes leading to collapse. The causes, and particularly the role of improved hybrid fitness in a changing environment, are poorly known. A recent example is the sympatric stickleback species pair of Enos Lake, Vancouver Island, which collapsed into a hybrid swarm following the invasion of the lake by American signal crayfish. Environmental changes can increase hybridization through increased inter-species mating or enhanced hybrid fitness, though few mechanisms have been experimentally tested. Using mesocosms, we tested whether crayfish addition alters the prey community and changes F1 hybrid fitness relative to pure limnetic and benthic threespine stickleback. Crayfish addition depleted benthic invertebrate biomass and negatively impacted survival of all three stickleback cross types. Crayfish had little effect on relative survival but led to a higher growth rate of hybrids relative to pure species. This improvement in hybrid fitness is unlikely to be the sole reason for the Enos Lake collapse, as breakdowns in premating isolation might have also been crucial. Nevertheless, this work provides a rare experimental demonstration of a causal link to changes in hybrid fitness, providing evidence that reduced selection against hybrids has contributed to the collapse.
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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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".