Loss of distinct functionality during fish community disassembly
Bibliographic record
Abstract
Human-induced environmental changes increase species turnover, typically characterized by native species extirpation and non-native species (NNS) invasions, leading to multiple functional consequences owing to varying species roles and whether losses from extirpation are compensated by newly established NNS. We analysed community functional roles among fish species that persisted, were extirpated or were newly established over 75 years (1940-2015) in 15 lakes in southwest China, using functional diversity indices and trait-based network analyses. While species extirpation rate increased strongly through time, earlier extirpations caused the greatest losses to novel functional diversity. Functional losses from native species extirpation were not compensated by NNS. Functional patterns of extirpations were mainly random or overdispersed, whereas invasions were almost always random species replacements. Synthesis and applications. Our findings highlight that the loss of distinctive functional diversity persists even when extirpation rates are low, due to the incomplete functional replacement of native species by NNS, which are typically functionally random relative to extirpated species. This underscores the importance of prioritizing the protection of functionally distinct species to preserve community integrity. Additionally, our use of trait-based network analysis provides a novel perspective for understanding the functional implications of species turnover, and could be a valuable tool for researchers and conservation practitioners to evaluate community assembly processes and functional structure dynamics.
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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 | 0.001 |
| 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".