Invasive Fish Reshape Biodiversity Patterns in China's Freshwater Lakes
Bibliographic record
Abstract
Globalization has dramatically accelerated the spread of non-native species, intensifying threats to freshwater ecosystems. While China ranks among the most heavily invaded countries, the role of non-native species as key drivers of biodiversity changes is often overlooked in research on anthropogenic impacts. This oversight hampers the development of effective conservation and management strategies by limiting a full understanding of what shapes biodiversity patterns. To bridge this gap, an extensive dataset from 131 lakes across China was compiled and analyzed using a novel composite diversity index that integrates species richness with functional and phylogenetic uniqueness, allowing for a more precise identification of fish multidimensional diversity hotspots. Additionally, gradient forest models were employed to elucidate the impacts of non-native species, geography, climate, and physicochemical factors on these patterns. Our findings revealed significant taxonomic and functional homogenization in non-native hotspots within the overall fish community, coupled with phylogenetic diversification. Notably, non-native fish diversity emerged as the primary factor shaping overall and native fish multidimensional diversity patterns. While the establishment of non-native species may provide an immediate enhancement to overall diversity, it often leads to the extirpation/extinction of native species, ultimately resulting in biodiversity loss at local and potentially the regional scale. This study highlights the importance of examining multiple dimensions to characterize the intricate dynamics between native and non-native species, which is essential for understanding their true impact on biodiversity and for achieving global conservation goals.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 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".