Functional Dimension Reveal Impacts of Non‐Native Fishes on Native Fishes and Ecosystem Functionality
Bibliographic record
Abstract
Anthropogenic-mediated invasions of non-native species are leading to biodiversity loss in many trophic groups, with specific impacts on a wide range of ecosystem functions and services. However, the impacts of non-native species on native species and ecosystem multifunctionality are not well understood, particularly due to the lack of long-term studies that focus on the analysis of functional and phylogenetic diversities. Using a comprehensive dataset spanning nearly 80 years from Lake Erhai, China, we assessed the impact of non-native fishes on the multidimensional diversity of native fishes, as well as the cascading effects on the multifunctional components of the ecosystem, including productivity, decomposition, and average multifunctionality. Over time, the multidimensional diversity of non-native fishes steadily increased, whereas that of native species declined markedly. Concurrently, both ecosystem productivity and average multifunctionality exhibited significant upward trends. Long-term invasion by non-native fishes was significantly negatively correlated with the multidimensional diversity of native fishes. A consistent convergence-divergence-convergence pattern was observed in trait spaces and in functional and phylogenetic community patterns of overall fish communities. Ecosystem multifunctionality increased with non-native fish functional diversity, though this positive relationship became negative at higher diversity levels. In contrast, native fish functional diversity and environmental factors-including average water level, Secchi depth, and annual precipitation-were consistently negatively associated with multifunctionality. These findings underscore the importance of integrating a functional perspective in biodiversity monitoring and management to enhance our understanding of, and foster more effective strategies for addressing, the long-term impacts of non-native species on native species and ecosystem multifunctionality.
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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 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".