Drivers of native and non‐native freshwater fish richness across North America: Disentangling the roles of environmental, historical and anthropogenic factors
Bibliographic record
Abstract
Abstract Aim A better understanding of native and non‐native species responses to environmental conditions, historical processes, and human pressures is crucial in the face of global environmental changes affecting biodiversity. Here, we evaluate the relative roles of environmental, historical and anthropogenic factors in influencing species richness of native and non‐native freshwater fishes in watersheds across North America. Location North America (exclusive of Mexico). Time period Recent. Major taxa studied Freshwater fishes. Methods We compiled an extensive dataset of native and non‐native fish richness in 2,993 watersheds across North America, together with corresponding data for environmental (climatic, geographic), historical and anthropogenic factors. We used variance partitioning and hierarchical partitioning to quantify the relative importance of environmental, historical and anthropogenic factors in explaining richness variation in native and non‐native [overall, and by geographic origin (foreign/translocated) and pathway (authorized/unauthorized)] fishes, while accounting for correlations among explanatory variables and spatial autocorrelation. Results Overall importance of environmental and anthropogenic factors was greater than historical factors in explaining both native and non‐native richness. Precipitation‐related factors were more important in explaining native richness, whereas non‐native richness was largely associated with temperature‐related factors. However, richness related to authorized introductions was less constrained by temperature than unauthorized introductions. Dam density, road density and urbanization gradient were major anthropogenic factors related to non‐native richness, yet their relative importance varied among origin‐ and pathway‐based categories. Conclusions Our findings indicate different environmental drivers influence native and non‐native fish richness patterns in North America. The accumulation of non‐native species in watersheds depends on the interaction between environmental conditions and anthropogenic‐based processes related to introduction history including geographic origin, introduction pathway, and propagule pressure, where the latter likely plays a major role. Warmer regions with high human population densities and more impoundments are more prone to fish invasions, mostly via unauthorized introductions.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".