Effects of nonnative species on the stability of riverine fish communities
Bibliographic record
Abstract
Despite the increasing ubiquity of biological invasions worldwide, little is known about the scale‐dependent effects of nonnative species on real‐world ecological dynamics. Here, using an extensive time series dataset of riverine fish communities across different biogeographic regions of the world, we assessed the effects of nonnative species on the temporal variability and synchrony in abundance at different organizational levels (population, metapopulation, community and metacommunity) and spatial scales (stream reach and river basin). At the reach scale, we found that populations of nonnative species were more variable over time than native species, and that this effect scaled up to the community level – significantly destabilizing the dynamics of riverine fish communities. Nonnative species not only contributed to reduced community stability, but also increased variability of native populations. By contrast, we found no effect of nonnative species dominance on local interspecific synchrony among native species. At the basin scale, nonnative metapopulations were again more variable than the native ones. However, neither native metapopulations nor metacommunities showed differences in temporal variability or synchrony as nonnative species dominance increased basin‐wide. This suggests a ‘dilution effect’ where the contribution to regional stability of local native populations from sites displaying low levels of invasion reduced the destabilizing effects of nonnative species. Overall, our results indicate that accounting for the destabilizing effect of nonnative species is critical to understanding native species persistence and community stability.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".