Effects of non‐native species on phylogenetic dispersion of freshwater fish communities in North America
Bibliographic record
Abstract
Abstract Aim Most studies focusing on terrestrial ecosystems have shown that non‐native species are more likely to invade phylogenetically clustered communities, that the introduction of non‐native species increases phylogenetic clustering in the recipient communities, and that the latter is more conspicuous for recipient communities at a larger spatial scale. However, whether these patterns hold for aquatic ecosystems is rarely tested. Here, we investigate the phylogenetic structure of native and non‐native freshwater fish assemblages at a continental spatial extent. Location The continental United States and Canada. Time period Current. Methods We collated native and naturalized non‐native species lists of freshwater fishes for each watershed at local and regional spatial scales. Two phylogenetic metrics (MPDses and MNNDses) representing different evolutionary depths were used to quantify the phylogenetic dispersion of each watershed fish assemblage and were related to temperature and precipitation using correlation and regression analyses. Values of the metrics were compared between the two spatial scales. Results Our study found that (1) phylogenetically more diverse fish communities tended to be invaded by fewer non‐native fish species, and that non‐native species more frequently invaded communities with lower phylogenetic dispersion (i.e. higher phylogenetic clustering), (2) introduction of non‐native fishes reduced phylogenetic clustering in some watershed but increased clustering in others, (3) introduction of non‐native species caused phylogenetic dispersion to increase in watersheds with low phylogenetic diversity and to decrease in watersheds with high phylogenetic diversity and (4) phylogenetic clustering for fish assemblages at the larger spatial scale was smaller than that at the smaller spatial scale. Main conclusions Support for Darwin's naturalization and preadaptation hypotheses in freshwater fish assemblages depends on the phylogenetic diversity of assemblages within the invaded watersheds. Our results are contrary to previous studies that found that the introduction of non‐native species increases phylogenetic clustering in the recipient communities more strongly at a larger spatial scale.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| 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".